KR20240000519A - target tracking system - Google Patents

Abstract

Systems and methods for tracking targets. The imaging system continuously captures multiple images of the target. The centroid tracker filters clutter and noise from the image, determines the center of the target, and generates a plurality of circular reference images of the target based on the image. The correlator tracker determines the shift between successive or rotating reference images of the target and generates an offset to enable the target tracking system to aim at the target. The system runs the central and correlator trackers simultaneously to monitor each tracker for failures and reverts to only one tracker when a failure is detected.

Description

target tracking system

The present invention relates to object detection using high energy laser (HEL) systems and in particular to target targeting systems.

High-energy laser (HEL) weapons systems require a fine tracking system to accurately point the laser beam to the target. A wide area medium infrared camera system is required to acquire and track targets, either automatically or with operator assistance, by closing the tracking loop around a gimbaled sensor to signal and direct the precision tracking system. do. The wide-area camera field of view includes other targets of interest, is small in size (in the case of mortars and UAS), obscured by cluttered backgrounds, and handles various lighting conditions. A wide area tracking system must continuously track during HEL engagements for reacquisition, battle damage assessment, and acquisition of other targets after the primary target is destroyed. Targets under tracking maneuver, emit a HEL beam heating effect, have variable lighting, and break apart.

Current infrared (IR) and visible tracking algorithms and systems are designed for open- or closed-loop tracking of targets, but are not suitable for HEL mission requirements. For example, while a target is heating, image artifacts appear, obscuring the target shape affecting computer vision and/or traditional correlation tracker techniques. These trackers provide tracking for multiple targets, but do not track multiple pieces of a target that are spatially separated if they move separately during destruction and result in a target break lock. This makes it impossible to accurately assess damage to the target. Therefore, these trackers are generally designed to track targets, but are not equipped to track targets in a way that can indicate combat damage or target kills.

Additionally, engaging fast-moving targets such as rockets or mortars requires the HEL weapon system to automatically acquire, track, and provide a target aimpoint to aim the HEL beam. Targets under tracking can be relatively stationary or maneuver against cluttered backgrounds, emit HEL beam heating effects, and have variable illumination, either passively or from active illuminators, all while maintaining a track break lock. Affects correlation tracker performance by incorporating negative effects on the target signature used for the correlations it induces. For mortars and rockets, tracking targets can be very small for a given pixel size, limiting the use of existing computer vision techniques that require larger target dimensions. Most importantly, when a target becomes hot due to the HEL effect, hot spots on the target cause traditional tracking techniques to lose lock on target features, causing track drift and ultimately unlocking of the target point. This will allow us to track hotspots created by HEL beams.

Current semi-active or passive target imaging trackers are based on frame-to-frame correlation to an integrated or fixed target reference and provide tracking lock against cluttered backgrounds and target aspect changes. Release. Break locks result from feeding corrupted target information back to the integrated target reference, impairing correlation measurements between the target and the reference. Some approaches to mitigate the negative effects of target reference updates include estimating the rotation and size of the target from the image using (CV) techniques as well as SNR-based thresholding of the target before integration into the reference. Most CVs are unsuitable for small targets and dynamic strength changes, resulting in track break locks and corrupted track estimates.

Additionally, unlocking occurs at the target's hotspot as HEL heating biases the track target position and causes the tracker to lose its lock aimpoint features, causing the tracked position to deviate from the target. Others have attempted to solve this problem by removing hotspots from the image or finding the target's tracking location that is not near the target's expected hotspot. Approaches to remove hotspots from images do not account for temporal effects such as saturation due to increased hotspot size and intensity or dynamic range compression and intensity increase, all of which cause trace point corruption and unlocking. Approaches utilizing offset target features at hotspots are limited and may result in inconsistent location and SNR, resulting in break-locking during HEL heating.

HEL weapon systems require precision and ultra-fast target point tracking. Precision tracking approaches, including centroid-based tracking and correlator-based tracking, are known for their performance and robustness to clutter and noise. The correlation tracker estimates the shift between the target's reference map and the current input image. This shift provides pointing to filtering algorithms for various system laser beam pointing and tracking functions. The most efficient computational implementation for estimating the cross-correlation of target and reference maps is performed using 2D (FFT). For state-of-the-art HEL tracking applications, the target region is typically separated from the background to reduce estimates from being corrupted by background clutter. The size and shape of the target may change during the engagement. The 2D FFT size used in the implementation should be tailored to the target size to minimize computation time and latency for high-speed image tracking and subsequent laser beam collimation. FFT correlators must execute 2D FFT operations in real time at very high execution (frame) rates on real-time devices with limited processing. The additional execution time impacts the algorithm processing timeline and hinders the tracking mission.

In previous attempts, the reduced execution time of generating 2D FFT plans of varying sizes in real time was accepted as an unavoidable cost, or suboptimal prediction plan sizes were selected to minimize plan generation costs. Another approach generates a large FFT covering all incoming signal sizes, and the results are extracted after execution. Another attempt created a series of plans based on guesses and selected the closest plan without guaranteeing optimal size or execution. Finally, the approach reduces the computational load of the correlator by using a binary or threshold representation of the target that is sensitive to noise and clutter.

In at least one aspect, the present technology relates to a target tracking system that tracks a target. The system includes at least one imaging system configured to continuously capture a plurality of images of a target. The system operates in a first tracker mode to filter clutter and noise from the image, determine the centroid of the target, and generate a plurality of recursive reference images of the target based on the image. ) and a centroid tracker configured to run. The system includes a correlator tracker configured to execute a second tracker mode to determine the shift between successive images of the target and generate an offset for aiming the target tracking system at the target. The correlator tracker utilizes a circular reference image when the first tracker mode is running. The system is configured to execute the first tracker mode and the second tracker mode simultaneously and track the target by monitoring whether the tracker mode fails. If a failure is detected in one of the tracker modes, the system executes only the tracker mode in which no fault was detected.

In some embodiments, the system includes a high energy laser (HEL) adjustment module. The HEL coordination module is configured to execute two stages. In the first stage, the HEL effect area is identified by detecting intensity changes in the spatial pattern of the target in the image due to the heating effect. After the intensity change is detected, the second stage turns on the HEL adjustment to eliminate heating effects within the spatial pattern of the target in the image. In some cases, when HEL adjustment is turned on, the system is further configured to track the target using only target areas that are not within the HEL influence area. In some embodiments, the system is further configured to update the circular reference image based on the HEL area of influence. In some cases, when the HEL adjustment module is turned on, the system is configured to continuously monitor the peak intensity of the target in the image and update system parameters based on the peak intensity of the target in the image. In some embodiments, when the HEL adjustment module is turned on, the imaging system is configured to adjust sensor gains based on the peak intensity of the target in the image to maintain the peak intensity of the target in the image within a predetermined range. do.

In some embodiments, the correlation tracker runs at a relatively fast speed compared to the speed of the centroid tracker. In some cases, the system includes a target state estimator configured to fuse data from the correlator tracker and the centroid tracker to estimate the current state of the target. The centroid tracker and correlator tracker can then be configured to depend on the estimated current state of the target.

In some embodiments, the system includes a line-of-sight manager configured to identify irregular movements of the target and determine an aimpoint for the target. When the first tracker mode and the second tracker mode operate, the target point is based on the centroid position of the centroid tracker adjusted by the shift of the correlator tracker and the update of the HEL module. The system may also include a HEL, and the line-of-sight manager may be configured to determine a second offset relative to the target point for aiming the HEL. The system can then point the HEL to the target based on the second offset and target point.

In some embodiments, the system utilizes gates to set boundaries within the image where the system searches for a target. The system can continuously adjust the gate size based on shifts between successive cyclic reference images. In some cases, the gate consists of two concentric areas. The first concentric area is a buffer around the estimated target size range used to determine the target position. The second concentric area is an annulus around the first concentric area. The system may estimate the background based on the second concentric region.

In at least one aspect, the present technology relates to a method of tracking a target using a target tracking system. The method includes sequentially capturing a plurality of images of the target using at least one imaging system. The method includes executing a first tracker mode using a centroid tracker, wherein the first tracker mode filters out clutter and noise from the image, determines the centroid of the target, and determines the target based on the image. It includes generating a plurality of circular reference images. The method also includes executing a second tracker mode using the correlator tracker, wherein the second tracker mode determines the shift between successive images of the target and generates an offset for aiming the target tracking system at the target. and wherein the correlator tracker utilizes a circular reference image when the first tracker mode is executing. Finally, the method includes running the first tracker mode and the second tracker mode simultaneously to track the target with the system and monitoring the tracker mode for a failure, and if a failure is detected in one of the tracker modes, the failure and running only the undetected tracker mode.

In some embodiments, the method includes executing a high energy laser (HEL) steering module in two stages. The first stage involves detecting intensity changes in the spatial pattern of the target within the image due to heating effects to identify HEL impact areas. After the intensity change is detected, the second stage includes turning on the HEL adjustment to eliminate heating effects within the spatial pattern of the target within the image. In some cases, after turning on HEL adjustment, the method includes tracking the target using only target areas that are not within the HEL influence area. The circular reference image is then updated based on the HEL influence area. In some embodiments, after turning on the HEL adjustment, the method includes continuously monitoring the peak intensity of the target in the image and updating parameters of the system based on the peak intensity of the target in the image. In some cases, after turning on the HEL adjustment, the method includes adjusting the sensor gain of the imaging system based on the peak intensity of the target in the image to maintain the peak intensity of the target in the image within a predetermined range.

In some embodiments, the correlation tracker runs at a relatively fast speed compared to the speed of the centroid tracker. The method may include estimating the current state of the target by fusing data from the correlator tracker and the centroid tracker with a target state estimator. The centroid tracker and correlator tracker may then be configured to depend on the estimated current state of the target.

In some embodiments, the method includes checking for irregular motion of the target and determining an aim point for the target using a gaze manager. When the first tracker mode and the second tracker mode are in operation, the target point is based on the offset of the correlator tracker and the centroid position of the centroid tracker adjusted by updates in the HEL module. The method may further include determining a second offset to an aimpoint for targeting the HEL, and aiming the HEL to the target based on the second offset and the aimpoint. there is.

In some embodiments, the method includes determining a plurality of gates that outline boundaries within an image through which the system locates a target. The gate is then continuously resized according to the shift between successive cyclic reference images. In some cases, the gate includes two concentric regions. The first concentric area is a buffer around the estimated target size range used to determine the target position. The second concentric area is an annulus around the first concentric area. The system may estimate the background based on the second concentric region.

In at least one aspect, the present technology relates to a target tracking system for tracking targets using ATS and FTS. ATS includes a wide field of view (FOV) camera system that continuously captures images of the target. The ATS is configured to identify the target to be tracked and send a signal to the target to be tracked. FTS includes a narrow FOV camera system. The FTS continuously re-centers the narrow FOV camera system around the target after the target is signaled by the ATS. ATS and FTS include independent steering mechanisms for each camera system.

In some embodiments, the target tracking system is a high energy laser (HEL) tracking system. ATS can be a MWIR tracker and FTS can be a SWIR tracker. The SWIR may include a centroid tracker configured to filter clutter and noise from the image, determine the center of the target, and generate a plurality of circular reference images of the target based on the image. The SWIR may include a correlator tracker that determines the shift between successive circular reference images of the target and generates an offset to the target's point of origin. In some embodiments, the system may include a target state estimator. The target state estimator may be configured to estimate the current state of the target by fusing data from the correlator tracker, centroid tracker, and MWIR, and the circular reference image may be updated based on the estimated current state of the target. In some cases, the SWIR tracker includes a high-speed SWIR camera system and a low-speed SWIR camera system and the MWIR camera system includes a low-speed MWIR camera system.

In some embodiments, the FTS is configured to track multiple characteristics of the target, including multiple segments of the target. The system may be a HEL tracking system and may include a HEL beam. The system can then aim the HEL beam to the signal target based on the FTS.

In some embodiments, the system is configured to execute a battle damage assessment mode. In combat evaluation mode, the system detects instances where the target's kinematics deviate from the target's expected kinetics after the target is hit by the HEL beam. In combat evaluation mode, the system also detects when one or more segments of the target become spatially separated from the target and determine target damage. In combat assessment mode, the system provides a target damage assessment indicator based on the determined target damage. In some cases, the system is configured to reassess the target after the HEL beam is turned off to determine target damage, which is based in part on the distance between spatially separated segments of the target. In some embodiments, the ATS is configured to signal a target for tracking when target characteristics meet a plurality of predetermined criteria, including target size, target shape, target speed, signal-to-noise ratio, and amount of time it appears. It is composed.

In at least one aspect, the present technology relates to a method of tracking a target using a target tracking system. The method includes operating an ATS that includes a wide field of view (FOV) camera system that continuously captures images of the target, identifies the target to be tracked, and signals the target to be tracked. The method also includes operating an FTS including a narrow FOV camera system to continuously re-center the narrow FOV around the target after the target is signaled by the ATS, wherein the ATS and FTS each have a camera system. Includes an independent steering mechanism.

In some embodiments, the target tracking system is a high energy laser HEL tracking system, the ATS is a MWIR tracker, and the FTS is a SWIR tracker. In some cases, operating the FTS includes operating a centroid tracker to filter out clutter and noise from the image, determine the center of the target, and generate a plurality of circular reference images of the target based on the image. . Operating the FTS may also include operating a correlator tracker to determine the shift between successive cyclic reference images of the target and generate an offset relative to the target's endpoint. In some embodiments, the method includes estimating the current state of the target with a target state estimator by fusing data from the correlator tracker, centroid tracker, and MWIR. The circular reference image may be updated based on the estimated current state of the target. In some cases, the SWIR tracker includes a high-speed SWIR camera system and a low-speed SWIR camera system, and the MWIR camera system includes a low-speed MWIR camera system.

In some embodiments, the method includes tracking a plurality of characteristics of the target, including a plurality of segments of the target, with the FTS. In some cases, the system is a HEL tracking system that includes a HEL beam. The method may include sending a signal to the target, based on the SWIR tracker, and then aiming the HEL beam at the target. In some embodiments, the method includes running the system in a combat damage assessment mode. In the combat damage assessment mode, the method includes detecting instances where the target's kinematics deviate from the target's expected kinetics after the target is hit by the HEL beam. In a combat damage assessment mode, the method may include detecting when one or more segments of the target become spatially separated from the target. In combat damage assessment mode, the method includes determining target damage and A target damage assessment indicator is provided based on the determined target damage.

In some embodiments, the method includes turning off the HEL beam. After the HEL beam is turned off, the target can be reevaluated to determine target damage, which is based in part on the distance between spatially separated segments of the target. In some embodiments, the ATS signals a target for tracking when target features meet a plurality of predetermined criteria including target size, target shape, target speed, signal-to-noise ratio, and amount of time they appear.

To help those skilled in the art to which the disclosed system pertains more easily understand how to make and use it, reference may be made to the following drawings.
1 is a block diagram of a target tracking environment including a target tracking system according to the present technology.
2 is a block diagram of a tracking architecture for a target tracking system according to the present technology.
FIG. 3 is a block diagram of the functions performed by an acquisition track sensor (ATS) tracker as part of the tracking system of FIG. 2.
Figure 4 is an example image generated by an ATS tracker according to the technique.
Figure 5 is a block diagram of the process for adaptively scaling and executing an FFT within a target tracking system in accordance with the present technology.
Figure 6 is a block diagram of the functions performed by the ATS tracker as part of the tracking system of Figure 2.

The present technology overcomes many prior art problems associated with target detection systems. Advantages and other features of the systems and methods disclosed herein will become more readily apparent to those skilled in the art from the following detailed description of certain preferred embodiments, taken together with the drawings illustrating representative embodiments of the present disclosure. Like reference numerals are used herein to indicate like parts. Additionally, directional words such as “upper,” “lower,” “distal,” and “proximate” merely help describe the position of components relative to each other. It is used to give. For example, the “top” surface of a part is merely intended to describe a separate surface from the “bottom” surface of the same part. Directional words are not used to describe absolute direction (e.g., the "upper" part should always be at a higher altitude).

In addition to providing auto-queuing tracking functionality to the ATS to cue the FTS narrow field of view camera, the tracker disclosed herein is a multi-object tracker that incorporates a laser upon detection of the FTS. trackfile, multiple feature trackers within the FTS tracker to generate consistent and robust tracks associated with the same target, and a target state estimator that fuses both FTS and ATS track outputs for accuracy and tracking consistency. ) includes. The system also includes mode selection to turn on the ATS's anti-HEL tracker features, including gate size locking, system gain control, and algorithm processing. For example, if the target feature is obscured by HEL heating effects, switch to correlation only track or centroid tracking. The third feature is the battle damage assessment mode, which detects cases where target kinematics are incorrect from expected dynamics and measurement features that are displaced in unexpected ways. The FTS tracker also runs continuously in the background and re-centers the narrow field of view tracking sensor when tracking is lost. This is also an indicator for assessing battle damage. The system can track various segments of the target and determine damage to the target in part based on the distance between separate segments of the target. In particular, as used herein, an ATS may be a short-wave infrared tracker (SWIR), while an FTS may be a medium wave infrared tracker (MWIR). However, in some cases, ATS and FTS can operate in the EM wavelength range of optical sensors, typically outside the SWIR or MWIR.

The system features an intra-target tracker that tracks various characteristics and dynamics of targets instead of multiple targets to assess combat damage impact, and a wide field of view to evaluate tracking performance and provide metrics for combat damage assessment. A target state estimator that fuses the field-of-view and narrow-field tracking sensor outputs, including the ability to turn on and off various tracking features that are more resistant to HEL effects in the tracker output, selecting the tracker type based on the state of the HEL effect among the lasers. an adaptive learning system that removes the background of both stationary and moving targets, and adaptive clutter filtering that removes the background of both stationary and moving targets.

The tracker disclosed herein uses both correlation and centroid tracking algorithms, with two modes running simultaneously and reverting to a random single mode if one fails. The centroid tracker has both clutter and noise filtering to support target segmentation to prevent correlation tracker drift by repeatedly updating the correlation tracker's reference template. The correlation tracker then uses the circular image updated by the centroid tracker to provide a high-speed offset for laser beam aiming. Cyclic image updates adapt to mitigation of HEL heating effects and illumination variations. Automatic aimpoint selection is performed using a computer based on a generalized estimate of the target shape obtained from a library of aim point locations.

The tracker system disclosed herein is interleaved and parallel centroid, target reference, high speed to prevent tracking drift and provide robustness for tracking loss due to artifacts (clutter and noise). It includes high-speed correlation trackers and allows you to select the default mode among the modes. The system tracks small, large, and overfilled targets within the field of view. The system utilizes clutter and noise filtering using bandpass and spatial noise adaptive filters, background statistics estimation with adaptive thresholding, positional feedback of predicted target dynamics, and dynamically updated spatial target gates. This system handles tasks with zero velocity and moving targets. Recursive target references are dynamically updated for correlation tracking and targeting in terms of target maneuvers and/or changes. The system uses automatic gain learning with target mask reference integration used for correlation to compensate for intensity changes due to lighting and HEL heating effects. Line-of-sight manager control optimally mixes centroid and correlation tracking positions, integrates target point offsets, and detects errors in tracking positions. Aimpoint estimation is performed by morphologically filtering target feature estimates without requiring a stored target model for target side changes. The target reference tracker keeps the laser aimed and the target reference position centered to minimize residual tracker drifts.

The tracker disclosed here also accounts for HEL heating effects by applying an algorithm utilizing two stages. The first is the "HEL ON" detector (or HEL detection module), which detects intensity changes due to heating effects and sends a flag to the system and tracker algorithm that HEL is on. When the HEL is detected to be ON (i.e., the HEL effect is identified on the target), the HEL ON detector notifies the imaging system, which uses this information to adjust the sensor gain to adjust the peak intensity and target to A/ The saturation effect can be minimized by maintaining D within the range for as long as possible. The second part of the algorithm then cores out the target's HEL influence area and uses the non-HEL heated target for tracking, including the circular reference. During tracking, the peak intensity of the target after filtering is continuously monitored and the tracker algorithm parameters are adaptively updated to mitigate residual heating effects on the tracker.

Accordingly, the present technology includes methods for integrating processes and/or algorithms (at the system level and/or algorithm level) that detect the presence of HEL heating effects and adaptively adjust tracker algorithms and system parameters to resist the effects. do. Additionally, the system disclosed herein provides adaptive spatial filtering technology that provides for the removal of spatial target heating effects in the target spatial pattern to maintain tracking without drift and maintain track lock by tracking unaffected target features. filtering techniques). Adaptive spatial filtering technology and temporal detection of the HEL beam are integrated together for a complete closed loop HEL heating effects mitigation algorithm solution for tracking.

Additionally, an optimized FFT library requires a priori computational path, that is, an execution profile, memory layout, and transformations to follow the optimal path for a particular FFT task and size. A plan that includes the sizes of transforms. The planning phase is process-intensive and it is impossible to plan in a real-time loop. This negative impact increases when the FFT size is changed on a frame-by-frame basis to match the FFT correlator requirements, and each size change requires different planning. Additionally, any plans created must be dismantled, resulting in an additional time penalty. The system disclosed herein automatically selects the most appropriate FFT size in the main execution loop using an algorithm that exhaustively searches for the optimal size, thereby achieving the optimal size for the dynamics transform size while minimizing the impact on the run time of the correlator. Solve the problem of creating and using 2D FFT plans.

To achieve this, the technology includes an algorithm that thoroughly searches the Prime Power Factor (PPF) and Power of 2 (PO2) to mathematically calculate the optimally sized FFT for all sizes. The algorithm automatically selects the best 2D FFT size for the two dimensions of the input signal outside the main execution loop and generates all necessary data structures for real-time use. The algorithm can also be tuned and optimized for any 2D FFT implementation, including hardware, software, and firmware. Additionally, the algorithm can be extended to 3D, 4D and ND where the image or signal representation is extended to multiple dimensions. Additionally, the algorithm for automatically selecting a 2D FFT may also be applied as part of other known processes using 2D FFTs. This algorithm can also be used in other environments where FFT is applied.

Referring now to Figure 1, a target tracking environment is depicted. A target 100 is being launched from a target launch area 102. Target 100 may be a military target, such as a mortar, missile, drone, etc., or a non-military projectile. A HEL target tracking system 104 according to the teachings herein seeks to acquire a target 100 in flight and damage or destroy the target 100 using a HEL beam. The tracking blocks shown herein and described below represent the steps of tracking a target 100 during its trajectory.

System 104 includes a beam control system 108 that operates in conjunction with a target cueing system 106. The target cue at any given time is a representation of the internal location "truth" given to the tracker system 104 from an external source. This may be a one-time update event or may be provided to system 104 on an ongoing basis. Therefore, as discussed in more detail herein, the target cue is used as the "truth" input (measurement update) upon which to build target tracking. After a number of features are observed and meet predetermined criteria, including predetermined target size, target shape, target speed, signal-to-noise ratio, and time (e.g., time of appearance in the image), the target may be signaled. Target queuing system 106 may utilize an ATS (e.g., MWIR) tracking system, while beam control system 108 may utilize a FTS (e.g., SWIR) tracking system, described in more detail below. The target queuing system 106 initially locates the target 100 while rejecting clutter and classifying the target 100 to begin tracking the target with a radar track 110 at block 112. Goes. After radar handover to beam control system 108, beam control system 108 initiates a low bandwidth gimbal track 114 and uses a wide field of view (WFOV) acquisition and tracking sensor at 116. Detect target 100. Next, the beam control system 108 pulls the target away from the block 118 and centers it. At block 120, the acquisition and tracking sensor determines edge and size features for tracking.

Next, the beam control system 108 initiates a high bandwidth precision track 122. During precision track 122, system 108 uses narrow FOV tracking using a fine tracking sensor (FTS). At block 124, the beam control system 108 classifies the target using FTS image features, kinematics, acquisition and track signatures, and LOS geometry. At block 128, the FTS centers the target, determines the target ID, and selects an endpoint for the target. Optionally, Beacon Illuminator (BIL) atmospheric compensation may be applied in block 130. In the final block 132, the beam control system 108 may combine the HEL and target 100 based on the determined target point. Additionally, the HEL engages the target 100 to maintain the FTS target point, including target jerk detection, FTS shape change detection, TS debris field separation, and compensation for HEL effects on the target 100. may be performed at block 132. The functionality of system 104 to effectively track target 100 is described in further detail herein. Referring now to Figure 2, a block diagram is shown illustrating the architecture of a tracking system 200 in accordance with the present technology. Tracking system 200 may be used to track a target for engagement with a HEL according to system 104 previously discussed. System 200 includes an ATS tracking system 202 that initially seeks and discovers targets, similar to target signaling system 106. Once a target is positioned on the ATS tracker, the ATS system 202 uses a gimbal controller 224 to center the target on the system's point of aim. Tracking and Closed Loop Tracking Sensor pointing is transferred to the tracker FTS or SWIR for high-speed, precision tracking in a relatively small field of view because the general location of the target is already known. Precise sensor aiming is controlled by an FSM controller 246. High-level input is provided to the FSM controller 246, which manages gaze at high rates (tens of kilohertz). FSM controller 246 provides target angular position through a fast FTS controller. System 200 is configured to control the actions of system 200 based on the current operation objective (e.g., target elimination) and other beam director states and modes required for particular engagements. It may include a mission processor unit 248 containing designed processing. MPU 248 may include a processor and memory configured to execute instructions to perform the processing tasks of ATS system 202 and FTS system 204 described herein.

In particular, the ATS system 202 uses a detector to signal a target to generate a filtered image based on various criteria. The detector may be a Robinson filter, another target filter, or a machine learning target shape detector. Detection of images is screened based on expected size, cue gating and temporal persistence until sufficient confidence is established to initiate tracking on a target, such as an object. Potential targets can also be selected based on the correlation of their kinematic characteristics with given cue expectations.

To this end, ATS system 202 generates ATS video 208, which can be between 60-120 Hz. The ATS system 202 uses a spatial filter 206, a Moving Target Indicator (MTI) clutter suppression module 212, and a low SNR target integration module 214 to video 208 ) can be processed. Spatial filter 206 is designed to apply adaptive spatial filtering to remove spatial scene clutter that affects the ability to detect targets. This allows the ATS system 202 to maintain target tracking without drift and track lock by tracking unaffected target features. Spatial filter 206 integrates the signal to increase SNR as needed to detect low SNR targets. In block 216, multiple detections in the image are identified, and in block 218, a signal is selectively delivered to the target based on its association with expected target parameters, such as the position and velocity of the detection compared to the expected position and velocity of the target of interest. do. To identify a target from the background, the Robinson detector can reject detections that are too small, or other spatial filters or machine learning target detectors can be used to set a default size for the target. The ATS system 202 may include a Kalman filter 220 to filter the image position estimate of the target location relative to the smoothed tracked target location in the image. After the target has been identified by the ATS system 202 and determined to meet the expected criteria, target tracking can be initiated and the gimbal is commanded to close the tracking loop around the ATS system 202 target tracking. The target location tracked in the ATS system 202 is used as a cue to the FTS system 204 to initiate high-speed precision target tracking.

Referring now to FIG. 6, flow diagram 600 illustrates the functionality of ATS system 202 from target detection 602 to selection of the most likely target to engage HEL 612. The ATS system 202 selects for detection of a target, such as an object, over predefined time intervals during the track acquisition phase, allowing target tracking to be initiated. At block 602, multiple detections in the current image are identified. The detections of blocks 602 in block 604 are assembled together in time using a coherence metric to track the location of the target in the image. Each detection in block 602 is then compared to the previous detection, and the most likely match is used to update the tracked object location in block 606. Block 606 is used to discard unwanted target tracks, including false target tracks due to clutter or targets that disappear from view. Block 610 updates the kinematic parameters (position, velocity, and time) of target tracking through estimation filtering using a Kalman filter.

ATS system 202 may use a Kalman-J filter with a kinematic dynamic model in pixel space for temporal and measurement updates. At block 604, a temporal update to the existing trace is provided according to:

(Equation 1)

(Equation 2)

In the equations here, “F” is the state transition matrix and “x” is the six state estimate of the trace. is the Joseph form for numerical stability.

At block 606, the algorithm prunes the target trace if the information is outdated. The new detection is associated with the existing current trace at block 608. Detection can be matched to tracking based on the measured feature distance between every pair (not necessarily the Euclidean distance). at For detection of , the distance between every pair can be measured as:

(Equation 3)

The measurement update is performed at block 610. Measurement updates can be performed according to:

(Equation 4)

(Equation 5)

(Equation 6)

(Equation 7)

(Equation 8)

Finally, at block 612, the most likely track to the target is selected by comparing the target track to the target cue, and the target is selected based on its proximity to the cue. Mahalanobis distance is used to find the number of standard deviations from the tracking center to the cue center according to:

(Equation 9)

A target track that minimizes the distance metric D for a given cue is selected. If there is a high probability that the target trace is associated with a target cue (based on the strength of correlation to the cue), the detection is declared target. If no target is declared, the system defaults to cue track, where targets are still acquired and no targets are declared. Without a queue, target resolution is insufficient due to missing measurements, and the last known target trace is propagated until measurements are available.

Using the ATS system 202, detection is provided with the azimuth of elevation coordinates in pixel space relative to the focal plan array of the imaging system according to:

(Equation 10)

(Equation 11)

Using navigation data, the tracker can track in a coordinate system such as ECEF (Earth-Centered, Earth-Fixed). A unit line-of-sight can be rotated into ECEF according to:

(Equation 12)

(Equation 13)

Pixel coordinates can be replaced with ECEF angles and passed to the FTS system 204 as follows:

(Equation 14)

(Equation 15)

Referring back to FIG. 2, the FTS system 204 disclosed herein includes steering a high-speed mirror (platform jitter) to a target point of view while the target is maneuvering and/or in apparent gaze movement due to atmospheric and platform jitter. It is a high-speed target tracker that functions as a high-speed target tracker. The FSM controller 246 directs the high-speed mirror commanded by the FTS system 204 to guide the HEL to provide lethal effect on the target, similar to the beam control system 108. The illustrated fast FTS system 204 acquires a target from a cue or operator-specified image location, engages in tracking once acquired, automatically determines the target point (e.g., block 238), and then mode to provide an offset 244 for the laser beam position around the target point determined automatically via a line of sight (LOS) pointing manager utilized by the FSM controller 246 to aim. provides.

The tracking architecture of the combined ATS and FTS tracking subsystems is detailed in Figure 2. Block 226 shows a high-speed FTS trace flow of processing that generates commands to the FSM to aim the HEL beam from the FTS image. The functionality of Figure 2 is explained in more detail in Figure 3.

The tracking modes of the FTS system 204 are broken down in detail in Figure 3. In a brief overview, the FTS system 204 generates a video feed of captured images, which may be between 400-800 Hz, for example. The FTS system 204 uses a correlation tracking algorithm (path 231) and a centroid tracking algorithm (path 233) that revert to a random single mode if one fails, with the two modes typically running simultaneously. For example, system 200 can switch to correlation-only tracking or centroid tracking when target features are obscured from HEL heating effects. Actions in the central tracking path 233 are described herein as being performed by the central tracker, while actions in the lower paths are described as being performed by the correlator tracker. The centroid tracker path 233 filters the captured image using a clutter filter 228. The center tracker path 233 can make further changes using the HEL adjustment module 230 when a HEL effect is detected at the target. This is advantageous because the heat of the HEL causes the target to heat up significantly after binding, resulting in high intensity output and large illumination changes in the detected image, making it difficult to continue tracking the target. By adjusting to take into account the HEL effect, HEL can effectively engage the target until the target is eliminated.

There are a number of situations that indicate a possible failure that can be observed, resulting in a reversion to single tracker mode. A failure mode of correlation tracker 300 (described in more detail below) that can occur is correlation drift due to target signal corruption due to noise and signature changes. In this case, the centroid tracker appropriately thresholds the target and segments the target from the background providing an absolute position reference for the target location. Failure modes of center tracker 302 (described in more detail below) include missed target detections in clutter filtering and target detection blocks in block 236 of FIG. 2. . In this case, the correlation tracker continues to check correlations with previously stored target signature references, enabling continuous tracking updates. Examples of failure modes are: (1) the HEL effect provides positive feedback to the tracker, so the tracker tracks the HEL effect instead of the target; (2) the fast correlator is decorrelated, so the actual track moves away from the target; (3) Center target point tracking becomes very unstable and tracking is canceled.

Within the HEL adjustment module 230, detection determines whether the target is experiencing the HEL effect at block 232 based on increased intensity and illumination changes within the image. If the target is experiencing a HEL effect, the HEL ON detector (block 232) triggers the system 200 to make further modifications within the HEL ON module 234 to account for the HEL effect. At block 236, the integrated target signature used for correlation is thresholded and segmented. The detected integrated target signature is used by the target point algorithm in block 238 to determine where to aim the HEL beam for lethality. In particular, when the HEL ON module 234 is activated, the imaging system of system 200 can adjust the sensor gain to minimize saturation effects by keeping the peak intensity and target within the A/D range for as long as possible. At block 240, a centroid estimate is provided to provide an absolute reference of the target location within the image used for the target endpoint and integrating circular images used by the high-speed correlator. The circular reference image can be relied upon in tracking and is used as a reference template by the correlator 229 as well as a template for blocks 236 for determining target endpoints. Additional processing may be performed in block 242 to remove unwanted background noise, clutter, jitter, etc. using alpha-beta filters, Kalman filters, or other filters. The updated circular reference image used for the target point provides a fast offset to the HEL at block 244, which in turn provides the HEL for the target point added to the frame-to-frame shift provided by the centroid and correlation tracker for coupling to the target. It may be relied upon by the FSM controller 246 for the indicated offset.

Target state estimator 222 may fuse data from FTS system 204 (e.g., correlator tracker and centroid tracker) and ATS system 202 to ensure accuracy and consistency. System 200 can then rely on the fused data for subsequent tracking. System 200 continuously tracks targets and dynamically updates rotating target references for correlation tracking and target points as described above, ensuring that system 200 is updated for target maneuvers and/or changes.

The combination of ATS system 202 and FTS system 204 allows multiple targets to be signaled simultaneously and allows split object tracking to be opened with multiple tracks related to the same target. This allows individual pieces of the target to be tracked even if the target is engaged and destroyed by the HEL, breaking the target into multiple pieces. System 200 may evaluate combat damage to a target when the target's kinematics differ from expected dynamics and/or when features of the target are displaced in an unexpected manner. System 200 may provide combat damage indicators based on combat damage assessments.

In particular, although portions of system 200 are shown and described herein as functional blocks or modules, it should be understood that the functions disclosed herein may be performed using a specially configured system designed to perform the described functions.

Referring now to Figure 3, the functionality of FTS system 204 is shown and described in greater detail. The tracking functions of the FTS system 204 are further separated into high-speed and low-speed target tracking paths 300 and 302. These paths 300 and 302 generally correspond to FTS paths 231 and 233, respectively. The high-speed tracker path 300 provides target position updates at the current frame rate to the line of sight (LOS) pointing manager 314. The fast tracker path 300 is the primary path that provides LOS updates to the fast mirror directed by the FSM controller 246 when a target is being tracked. A low-speed tracker path 302 provides periodic updates on the target point, temporarily integrates an image-centric target reference for correlation to the high-speed path, and detects and segments target tracking gates. and update and initialize.

An exemplary target tracking gate can be seen in Figure 4. Referring now to Figure 4, an image 400 of target 406 acquired by FTS system 204 is shown. Image 400 includes two concentric areas 402 and 404 representing the target gate. The first is a target area 402, which contains a buffer around an estimated extent of the target size. The second is the background estimation area 404, which is shaped like a ring around the target area 402 used for background estimation. The background estimate is determined during the slow tracking path 302 of Figure 3. Tracker gates 402 and 404 allow the tracking system 200 to target images 400 to maximize detection, provide efficient locations to search in image space, and reject background contributions. This is one way to effectively isolate the region containing (406). Tracker gates 402, 404 continuously update size and position on both high-speed and low-speed interleaved tracker paths 300, 302. When cue information is initially absent, tracker gates 402, 404 may be sized to initially include the entire image 400. Alternatively, tracker gates 402, 404 may initially be sized from the target location and/or size information, if present, from the ATS tracker 202 or other system function.

FTS tracker 204 also uses a feature state estimator to track and center the HEL on image features. To this end, FTS tracker 204 includes multiple state estimators. In its truest sense, 'state' refers to the velocity, acceleration and position of the tracked feature. Since these features are in image coordinates, these feature state estimators provide target feature states in image coordinates rather than inertial coordinates. The target feature state estimator includes a correlation tracker module 308, a centroid tracker 324, an aimpoint tracker 312, and a circular reference image tracker, as discussed in more detail below. (recursive reference image tracker) 326.

Referring again to FIG. 3, low speed tracker 302 is shown in the lower path and high speed tracker 300 is shown in the upper path, but both may be part of FTS tracker 204. After the target is initially acquired, subsequent tracking may be performed using both high-speed and low-speed trackers 300, 302. In general, components of path 300 may serve as correlation trackers, while components of path 302 may serve as centroid trackers. An image interleave 330 may be used between the two trackers 300, 302. Image interleaving is a mode in which the low-speed tracker 302 updates its tracking position, center, and target reference every Nth frame, where N is a variable set before the tracker 302 operates. The output of the tracker 302 at the target position is typically the fast tracker 300 correlation output for the subsequent N-1 frames, with the Nth frame being the centroid tracker position, the correlated tracker target estimate position, or a mixed combination of both tracker target positions. am.

In the slow tracker 302, the image is spatially filtered in block 316. Filter image block 316 removes background and noise and enhances the target signal for further processing. Filter image block 316 operates on each frame of the image and represents the first part of the processing chain. In this step, the pixel detection threshold is set by determining the background statistics of the area around the target excluding the target. Next, the signal is processed by an estimate background function 318. The estimate background process calculates statistics on an annulus around the target location for tracking, and on a larger boundary for acquisition (i.e., when the target is still identifiable).

Next, the tracker executes the threshold and segment target functions (320). The threshold and segment function 320 takes as input each image processed by the slow centroid tracker 324, applies a threshold based on the background estimation function 318, and then ) Detect pixels. Pixels that exceed a threshold are grouped using an 8-way concatenation and converted into objects with statistics such as size, area, and centroid.

Next, a target search and selection function 322 is performed. The target find and select function 322 has two modes of operation; There is a target acquisition mode in which the target is identified and a tracking mode in which the identified target is tracked. In target acquisition, function 322 takes segmented objects as input from the threshold and segment target function 320 and selects the segmented object closest to the acquisition criteria. In tracking, the target find and select function 322 selects the segmented object closest to the estimated tracking location. The target find and select function 322 also calculates the range of target locations and their positions to estimate target and background (or constant false alarm CFAR) gates (e.g., 402, 402).

Further processing is then performed by the center target tracker 324. The centroid target tracker 324 implements the ability to calculate the centroid for each target selected in the target find and select function 322 and filter the positions to develop speed and position estimates of the current position and future positions at the next update. . There are a variety of trace filter implementation options, including Kalman filters, alpha beta, and other known filters.

An update recursive reference function 326 is then applied to shift the segmented and selected target object into a reference image coordinate frame used for trackpoint and closed-loop gaze aiming. Each segmented object is integrated into a reference coordinate system to improve the signal-to-noise ratio. This circular reference picture supports two functions. First, the cyclic reference picture is used to estimate each input image translation in the correlation tracker module 308 of the fast tracker (i.e., path 300) for closed-loop tracking to a tracking reference point. This is a reference image. Second, a target aimpoint 312 is estimated. Input frames are temporally weighted and designed to match target shape changes due to motion and dynamics to maximize the reference image signal-to-noise ratio while ensuring tight alignment between the reference and current input frames. . A 'reference image track state estimator' is built into block 326 to track the center location of the target center for pointing the line of sight pointing manager 314. do.

The propagating gate function 328 then updates the gate area where the image pixels are captured to calculate the track point and target feature for the next frame based on the current segmented target size and the centroid tracker predicted target position for the next frame. The gate defines a target area (e.g. gate 402) and a concentric area around the target (e.g. gate 404) for background estimation in the background estimation function 318 of the next frame when in tracking. do.

The target point estimation function 312 then uses an update recursive reference function to calculate the target target point using computer vision-based techniques to estimate shape, rotation axis, and direction, or any other computer vision or machine learning technique. ) (326) uses the integrated target reference image. The target point is determined relative to the axis of rotation by adding a predefined position to the axis of rotation, a user-adjustable position, or a user-adjustable position to a predefined position. The target point estimate 312 provides the LOS aiming manager 314 with an offset aiming position relative to the target point or a shift to the center of the circular reference for the laser position and offset tracking. Target locations are filtered and tracked similarly to the center target tracker 324 as previously described.

High-speed tracker 300 functions concurrently with low-speed tracker 302. This high-speed tracker 300 is the main tracker function used to instantaneously aim the LOS of the FTS system 204. In the fast tracker 300 path, the input image is initially filtered at block 304, which functions similarly to the filter image block 316 described above (e.g., for each frame to enhance the target signal). performs calculations and removes background and noise).

The correlation to the reference function 306 is applied to calculate the current image shift with respect to the reference image estimated in the updated circular reference function 326 of the slow tracker 302 path. The correlation area is adapted to the size of the target gate calculated in the target find and select function 322. Correlation to the reference function is a cross-correlation between the reference image and the current input image using an adaptive size 2D FFT and 2D inverse FFT, scaled to fit the currently gated target region, to perform the cross-correlation. -correlation) is determined. This process of adaptively adjusting the size of the FFT is depicted in Figure 5 and is described in more detail below. The current image shift for the circular image is the detected peak position of the correlation output, interpolated to sub-pixel positions using a 2D peak interpolation method. The correlation to the reference function 306 includes a gated image intensity and a pixel threshold that clips the reference image intensity to create a semi-binary image. Semi-binary images preserve key target shape properties for cross-correlation while protecting against intensity changes across the target shape. A predetermined pixel is searched for in the current image.

The shift is then performed by the correlation tracking function 308. The correlation tracker function 308 is one of the target state estimates previously discussed. The correlation tracker function 308 uses the output of the image shift between the current frame and the reference frame.

When the correlation mode is operating, the correlation gate area size location is updated in the update tracking gate block 310. The size of the correlation region is updated with the interleaved centroid tracker finding and target selection function 322 discussed previously.

The LOS aim manager 314 is responsible for providing closed-loop line-of-sight target tracking position to the system's angle controller for aiming. FSM controller 246 may respond to the aiming position. When the tracking system 200 is in acquisition mode, the central location of the target detected in the target find and select function 322 focuses the line of sight. In tracking mode, when the correlator is operating, the correlator image shifts the reference image center position relative to the reference. When the interleaved centroid mode is operating, the centroid location (e.g., centroid target tracker 324) is either aimed at the gaze as an option or mixed with the correlation tracker or a correlation tracker-only output. The LOS aim manager 314 also checks for aim irregularities, limits spurious aim commands, and adds a point of aim estimate to the aim offset calculated in block 312.

Referring again to FIG. 4, image 400 of target 406 is generated into a correlation track 408, a track state estimate smoothed target position 410, and automatically Includes an automatically generated aimpoint 412.

Referring now to Figure 5, system 200 utilizes algorithm 500 to mathematically calculate the optimal FFT size by exhaustively searching the fractional power factor (PPF) and power of 2 (PO2). The algorithm builds an optimal selector that selects the FFT size that best suits the dimensions of the input signal. It does this outside of the main run loop to prepare all the data structures needed for real-time FFT and iFFT transformations. System 200 utilizes a 2D FFT, but in some cases the algorithm may be performed on a 3D FFT.

At step 502, the algorithm begins by defining the coverage area for the FFT size. The coverage area can be entered by the user based on the expected signal magnitude in both dimensions (minS, maxS). Algorithm 500 then creates a solution space of optimal size within plan creation engine 504. This begins with module 506 sweeping each characteristic dimension of the FFT (scalable from 1 to N dimensions) and retrieving all principal power factor (PPF) and power-of-two (PO2) elements of a given dimensional range MxN. Within module 508, for each combination of factors, a 2D FFT plan is generated that is optimal for execution and memory for that size. The lookup algorithm converts each set of incoming dimensions to the minimum power factor that is greater than or equal to the dimensions. Within module 510, a lookup table is created that maps every element combination to an inverse FFT (IFFT) for the combination with an index into the storage space of the FFT, which is guaranteed to be optimal for the algorithm used to compute the FFT. This creates an exhaustive space for all PPF combinations in each dimension and allows the library to utilize deep benchmarking to select the optimal instructions and memory space for a particular combination. A set of lookup tables index the locations of these data structures, allowing for immediate selection during fast loops of the application (i.e., while the FFT size is dynamically changing). Any incoming size included in the original range is assigned an optimal FFT solution by the selection process of algorithm 500.

The plan generated in plan generation engine 504 may be utilized by correlator 229 to apply an FFT. In particular, input 512 from tracking system 200 is provided to algorithm 500. This includes the input from the sensor subsystem 518 of system 200, the 2D size coming from the image acquisition system 516, and the correlator dynamic signal sizes 514. Includes. The FFT for correlator 229 may be executed within a high-rate execution loop 520. During fast execution loop 520, pre-generation of mapping tables and functions ensures that the choice of FFT plan meets real-time requirements without incurring a penalty for in-loop plan generation. A real time optimal plan selection module 522 selects an optimal plan from the plan generation engine 504. The FFT correlator algorithm 524 then optimally executes a 2D FFT (optimal execution module 526) to determine the cross-correlation between the reference image and the current input image using an adaptively sized FFT. Results 528 are utilized by correlator 229 to perform cross-correlation. Plan creation destruction engine 530 provides out-of-the-loop plan deconstruction, reducing memory deallocation execution penalties within fast algorithmic loops. guaranteed.

Overall, through interleaved and parallel centroid, target-to-target tracking using target references and high-speed correlation tracking, the tracking system described here provides robustness for preventing tracking drift and tracking losses due to artifacts (clutter and noise). By default, you can choose between two tracking modes to track small targets, large targets, and overfilled targets in the field of view.

All orientations and arrangements of components shown herein are used as examples only. Additionally, it will be understood by those skilled in the art that in alternative embodiments the functions of several elements may be performed by fewer elements or a single element. Likewise, in some embodiments certain functional elements may perform fewer or different operations than described for the illustrated embodiments. Additionally, functional elements shown separately for illustrative purposes may be integrated within other functional elements in a particular implementation.

Although the present technology has been described with respect to preferred embodiments, those skilled in the art will readily understand that various changes and/or modifications may be made to the present technology without departing from the spirit or scope of the present technology. For example, each claim may be dependent on some or all claims in a multiple dependency manner even if they were not originally claimed.

Claims (20)

In a target tracking system for tracking a target,
At least one imaging system configured to continuously capture a plurality of images of the target,
a centroid tracker configured to filter clutter and noise from the image, determine the center of the target, and execute a first tracker mode to generate a plurality of circular reference images of the target based on the image;
a correlator tracker configured to execute a second tracker mode to determine a shift between successive images of the target and generate an offset for aiming the target tracking system at the target.
Including,
The correlator tracker,
utilizing the circular reference image when the first tracker mode is executing;
The system is,
executing the first tracker mode and the second tracker mode simultaneously to track the target, and monitoring the tracker mode for a failure, and if a failure is detected in one of the tracker modes, a tracker mode in which a failure is not detected is configured to run only,
system.
According to paragraph 1,
The system is,
High Energy Laser (HEL) Tuning Module
It further includes,
The HEL adjustment module is,
It is configured to run two stages,
The two stages are,
a first stage that detects intensity changes in the spatial pattern of said target in the image due to heating effects to identify HEL affected areas, and
After the change in intensity is detected, a second stage turns on HEL adjustment to eliminate the heating effect within the spatial pattern of the target in the image.
Including,
system.
According to paragraph 2,
When the HEL adjustment is on, the system:
further configured to track the target using only the target area that is not within the HEL area of influence,
system.
According to paragraph 3,
The system is,
further configured to update the circular reference image based on the HEL area of influence,
system.
According to paragraph 3,
When the HEL adjustment module is turned on, the system:
configured to continuously monitor the peak intensity of the target in the image and update the system parameters based on the peak intensity of the target in the image.
system.
According to clause 5,
When the HEL adjustment module is turned on, the imaging system:
configured to adjust sensor gain based on the peak intensity of the target in the image to maintain the peak intensity of the target in the image within a predetermined range,
system.
According to paragraph 2,
The correlation tracker is,
Running at a relatively fast speed compared to the speed of the center tracker,
system.
According to paragraph 1,
A target state estimator configured to fuse data from the correlator tracker and the centroid tracker to estimate the current state of the target.
It further includes,
The centroid tracker and the correlator tracker are:
configured to depend on the estimated current state of the target,
system.
According to paragraph 2,
The system is,
A gaze manager configured to check irregular movements of the target and determine an aiming point for the target, and
HEL
It further includes,
When the first tracker mode and the second tracker mode are operating,
The target point is,
Based on the centroid position of the centroid tracker adjusted by a shift of the correlator tracker and an update of the HEL module,
The gaze manager is,
configured to determine a second offset relative to a target point for aiming the HEL, and aim the HEL at the target based on the second offset and the target point,
system.
According to paragraph 1,
The system is,
the system utilizes a gate to outline a boundary within the image to locate the target, and
configured to continuously adjust the gate size based on shifts between successive cyclic reference images,
system.
According to clause 10,
The gate is,
two concentric zones
Including,
The first concentric region is,
A buffer around the estimated range of the target size used to determine the target location,
The second concentric region is,
a ring around the first concentric region,
The system is,
configured to estimate a background based on the second concentric region,
system.
In the target tracking method using a target tracking system,
sequentially capturing a plurality of images of the target using at least one imaging system;
executing a first tracker mode using a centroid tracker;
executing a second tracker mode using a correlator tracker;
tracking the target with the system by simultaneously executing the first tracker mode and the second tracker mode and monitoring the tracker mode for a failure, and if a failure is detected in one of the tracker modes, the failure is not detected. Steps to run only the tracker mode, not the above
Including,
The first tracker mode is,
filtering out clutter and noise from the image, determining the center of the target, generating a plurality of circular reference images of the target based on the image,
The second tracker mode is,
determining a shift between successive images of the target and generating an offset for aiming the target tracking system at the target, wherein the correlator tracker comprises:
utilizing the circular reference image when the first tracker mode is executing,
method.
According to clause 12,
Steps to run a high-energy laser (HEL) steering module in two stages
It further includes,
The two stages are,
a first stage comprising detecting changes in intensity in the spatial pattern of the target in the image due to heating effects to identify HEL affected areas, and
After the change in intensity is detected, a second stage comprising turning on a HEL adjustment to eliminate the heating effect within the spatial pattern of the target in the image.
Including,
method.
According to clause 13,
After turning on the HEL adjustment, tracking the target using only areas of the target that are not within the HEL influence area, and
Updating the circular reference image based on the HEL area of influence.
Containing more,
method.
According to clause 14,
After turning on the HEL adjustment, continuously monitoring the peak intensity of the target in the image and updating parameters of the system based on the peak intensity of the target in the image.
Containing more,
method.
According to clause 15,
After turning on the HEL adjustment, adjusting the sensor gain of the imaging system based on the peak intensity of the target in the image to maintain the peak intensity of the target in the image within a predetermined range.
Containing more,
method.
According to clause 12,
The correlation tracker is,
Run at a relatively fast speed compared to the speed of the center tracker,
The above method is,
With a target state estimator, fusing data from the correlator tracker and the centroid tracker to estimate the current state of the target.
Including,
The centroid tracker and the correlator tracker are:
configured to depend on the estimated current state of the target,
method.
According to clause 12,
Checking irregular movements of the target and determining an aiming point for the target using a gaze manager,
determining a second offset relative to the target point for aiming the HEL, and
Aiming the HEL at the target based on the second offset and the target point
It further includes,
When the first tracker mode and the second tracker mode are operating,
The target point is,
Based on the offset of the correlator tracker and the centroid position of the centroid tracker adjusted by an update of the HEL module,
method.
According to clause 12,
determining a plurality of gates outlining boundaries within the image through which the system locates the target, and
Continuously adjusting the gate size based on shifts between successive cyclic reference images.
Containing more,
method.
According to clause 19,
The gate is,
two concentric zones
Including,
The first concentric region is,
A buffer around the estimated range of the target size used to determine the target location,
The second concentric region is,
a ring around the first concentric region,
The above method is,
configured to estimate a background based on the second concentric region,
method.