CN119535422A

CN119535422A - Method, device, computer equipment, computer readable storage medium and computer program product for detecting target echo signal

Info

Publication number: CN119535422A
Application number: CN202411428944.9A
Authority: CN
Inventors: 张明洋; 姬利海; 贾鹏彦; 沈卫超
Original assignee: Shanghai Zhangjiang Institute Of Mathematics
Current assignee: Shanghai Zhangjiang Institute Of Mathematics
Priority date: 2024-10-14
Filing date: 2024-10-14
Publication date: 2025-02-28

Abstract

The present application relates to a method, an apparatus, a computer device, a computer readable storage medium and a computer program product for detecting a target echo signal. The method comprises the steps of obtaining an original echo signal, wherein the original echo signal comprises a noise signal and a target echo signal, carrying out frequency domain matching filtering processing and self-adaptive line spectrum enhancement processing on the original echo signal to obtain a corresponding signal filtering result, mapping the signal filtering result to a high-dimensional feature space, carrying out two classifications according to a target hyperplane in the high-dimensional feature space to obtain a signal type label corresponding to the signal filtering result, wherein the target hyperplane is determined according to the distance from a target echo signal sample and a sample data point of the noise signal sample in the high-dimensional feature space to the same hyperplane, and generating a detection result of the target echo signal according to the signal type label. The method can accurately identify and detect the target echo signal in a complex noise environment.

Description

Method, apparatus, computer device, computer readable storage medium and computer program product for detecting target echo signal

Technical Field

The present application relates to the field of signal processing technology, and in particular, to a method, an apparatus, a computer device, a computer readable storage medium, and a computer program product for detecting a target echo signal.

Background

In a complex marine environment, due to the influence of noise in the underwater environment, a great amount of noise and reverberation are often mixed in the active sonar when receiving a target echo signal. Therefore, how to detect a target echo signal in a signal mixed with noise and reverberation has become a current research focus.

In the prior art, a frequency domain adaptive filter can be used to perform matched filtering on an input signal received by an active sonar based on the frequency domain characteristics of a target echo signal, so as to obtain a detection result of the target echo signal. However, since the background noise of the signal processed by the frequency domain adaptive filter fluctuates greatly, the detection result of the target echo signal is likely to be inaccurate.

Disclosure of Invention

In view of the foregoing, it is desirable to provide a method, an apparatus, a computer device, a computer-readable storage medium, and a computer program product for detecting a target echo signal that can have high detection accuracy in complex background noise.

In a first aspect, the present application provides a method for detecting a target echo signal, including:

Acquiring an original echo signal, wherein the original echo signal comprises a noise signal and a target echo signal;

Performing frequency domain matched filtering processing and self-adaptive line spectrum enhancement processing on the original echo signal to obtain a corresponding signal filtering result;

Mapping the signal filtering result to a high-dimensional feature space, and performing two-class classification according to a target hyperplane in the high-dimensional feature space to obtain a signal class label corresponding to the signal filtering result, wherein the target hyperplane is determined according to the distances from a sample data point of a target echo signal sample and a noise signal sample in the high-dimensional feature space to the same hyperplane;

And generating a detection result of the target echo signal according to the signal class label.

In one embodiment, the performing frequency domain matched filtering processing and adaptive line spectrum enhancement processing on the original echo signal to obtain a corresponding signal filtering result includes:

inputting the original echo signal to a matched filter, and carrying out Fourier transform processing on the original echo signal by adopting the matched filter to obtain a frequency domain signal corresponding to the original echo signal;

carrying out matched filtering processing on the frequency domain signal by adopting the matched filter to obtain a preliminary filtering result, wherein the preliminary filtering result comprises a single frequency component and a noise component;

Performing adaptive line spectrum enhancement processing on a single frequency component in the preliminary filtering result by adopting the matched filter based on target frequency domain characteristics, so as to obtain a target line spectrum signal, wherein the target frequency domain characteristics are obtained by performing Fourier transform on a frequency domain sample of a transmitting signal corresponding to the target echo signal;

And carrying out inverse Fourier transform processing on the target line spectrum signal by adopting the matched filter to obtain the signal filtering result in the time domain.

In one embodiment, the performing adaptive line spectrum enhancement processing on the single frequency component in the preliminary filtering result by using the matched filter based on the target frequency domain characteristic to obtain a target line spectrum signal includes:

Multiplying the initial weight coefficient by the initial filtering result by adopting the matched filter to obtain a processed enhanced line spectrum signal;

determining a minimum mean square error corresponding to the initial weight coefficient according to the target frequency domain characteristic and the enhanced line spectrum signal by adopting the matched filter;

dynamically adjusting the initial weight coefficient to obtain a target weight coefficient according to the minimum mean square error by adopting the matched filter based on a minimum mean square error criterion;

and multiplying the preliminary filtering result by the target weight coefficient by adopting the matched filter to obtain the target line spectrum signal.

In one embodiment, the performing fourier transform processing on the original echo signal by using the matched filter to obtain a frequency domain signal corresponding to the original echo signal includes:

the method comprises the steps of carrying out weighting treatment on original echo signals received by each array element in a multi-element array by adopting signal weights corresponding to preset directions to obtain treated weighted echo signals;

superposing the weighted echo signals corresponding to each array element to obtain a target beam signal corresponding to the multi-element array;

And carrying out Fourier transform processing on the target beam signal by adopting the matched filter to obtain the frequency domain signal.

In one embodiment, the method further comprises:

constructing a range-azimuth spectrum corresponding to the target beam signal;

And positioning the target detection position corresponding to the target echo signal in the distance-azimuth spectrum according to the signal class label of the signal filtering result.

In one embodiment, mapping the signal filtering result to a high-dimensional feature space, and performing two classifications according to a target hyperplane in the high-dimensional feature space to obtain a signal class label corresponding to the signal filtering result, where the method includes:

normalizing the signal filtering result to obtain a normalized signal filtering result;

Mapping the normalized signal filtering result to the high-dimensional feature space by adopting a linear kernel function to obtain a corresponding linear separable data set;

and carrying out two classification on the linear separable data set according to the position relation between the linear separable data set and the target hyperplane to obtain the signal class label.

In a second aspect, the present application further provides a device for detecting a target echo signal, including:

the signal acquisition module is used for acquiring an original echo signal, wherein the original echo signal comprises a noise signal and a target echo signal;

The matched filtering module is used for carrying out frequency domain matched filtering processing and self-adaptive line spectrum enhancement processing on the original echo signal to obtain a corresponding signal filtering result;

The signal classification module is used for mapping the signal filtering result to a high-dimensional feature space, and carrying out two-classification according to a target hyperplane in the high-dimensional feature space to obtain a signal class label corresponding to the signal filtering result, wherein the target hyperplane is determined according to the distance from a sample data point of a target echo signal sample and a noise signal sample in the high-dimensional feature space to the same hyperplane;

and the result generation module is used for generating a detection result of the target echo signal according to the signal class label.

In a third aspect, the present application also provides a computer device. The computer device comprises a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the method for detecting the target echo signal according to any one of the embodiments of the first aspect.

In a fourth aspect, the present application also provides a computer-readable storage medium. The computer readable storage medium has stored thereon a computer program which, when executed by a processor, implements the method for detecting a target echo signal according to any one of the embodiments of the first aspect.

In a fifth aspect, the present application also provides a computer program product. The computer program product comprises a computer program which, when executed by a processor, implements the method for detecting a target echo signal according to any one of the embodiments of the first aspect.

According to the method, the device, the computer equipment, the computer readable storage medium and the computer program product for detecting the target echo signals, the original echo signals are obtained, frequency domain matched filtering processing and adaptive line spectrum enhancement processing are carried out on the original echo signals, the processed signal filtering results are mapped to a high-dimensional characteristic space, the signal filtering results are subjected to two-classification according to the target hyperplane in the high-dimensional characteristic space, the detection results of the target echo signals are generated based on the signal class labels obtained by two-classification, the signal-to-noise ratio of the original echo signals can be improved through the frequency domain matched filtering processing and the adaptive line spectrum enhancement processing, the accuracy of the detection results is improved through the two-classification algorithm based on the support vector machine, and therefore accurate identification detection of the target echo signals under the complex noise environment is achieved.

Drawings

In order to more clearly illustrate the embodiments of the present application or the technical solutions in the related art, the drawings that are needed in the description of the embodiments of the present application or the related technologies will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and other related drawings may be obtained according to these drawings without inventive effort to those of ordinary skill in the art.

FIG. 1 is an application environment diagram of a method for detecting a target echo signal in one embodiment;

FIG. 2 is a flow chart of a method for detecting a target echo signal according to an embodiment;

FIG. 3 is a flowchart illustrating a signal filtering result generating step in one embodiment;

FIG. 4 is a flow chart of a target line spectrum signal generation step in one embodiment;

FIG. 5 is a flow chart of a beam forming step in one embodiment;

FIG. 6 is a flow chart illustrating the steps of generating a signal class label in one embodiment;

FIG. 7 is a flowchart of a method for detecting a target echo signal according to another embodiment;

FIG. 8 is an algorithm flow diagram of a matched filter in one embodiment;

FIG. 9A is a range-azimuth spectrum based on conventional signal processing results in one embodiment;

FIG. 9B is a range-azimuth spectrum of adaptive line spectral enhancement technique results based on matched filtering in one embodiment;

FIG. 10A is a range-azimuth spectrum based on a constant false alarm algorithm in one embodiment;

FIG. 10B is a range-azimuth spectrum based on a support vector machine in one embodiment;

FIG. 11 is a block diagram illustrating a target echo signal detection device 1100 according to an embodiment;

Fig. 12 is an internal structural diagram of a computer device in one embodiment.

Detailed Description

The present application will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present application more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.

It should be noted that, the user information (including but not limited to user equipment information, user personal information, etc.) and the data (including but not limited to data for analysis, stored data, presented data, etc.) related to the present application are both information and data authorized by the user or sufficiently authorized by each party, and the collection, use and processing of the related data are required to meet the related regulations.

The method for detecting the target echo signal provided by the embodiment of the application can be applied to an application environment shown in fig. 1.

Wherein sonar detection device 102 communicates with server 104 via a network. During operation, the sonar detection device 102 may transmit a signal to an object to be detected, receive an echo signal corresponding to the transmitted signal returned from the object, and generate a corresponding detection result according to the received echo signal. The echo signals are susceptible to underwater environmental noise in the underwater propagation process, so that the original echo signals received by the sonar detection device 102 are mixed with noise signals and target echo signals.

The data storage system may store data that the server 104 needs to process. The data storage system may be integrated on the server 104 or may be located on a cloud or other network server. The server 104 may be an independent physical server, a server cluster or a distributed system formed by a plurality of physical servers, or a cloud server providing cloud computing services.

Specifically, server 104 may acquire the raw echo signals transmitted by sonar detection device 102. Because the target echo signal is generated based on the transmit signal, the server 104 may construct a matched filter that yields the target echo signal based on the adaptive line spectral enhancement technique of frequency domain matched filtering. The server 104 may perform frequency domain matched filtering processing on the original echo signal by using a matched filter corresponding to the target echo signal, and then perform adaptive line spectrum enhancement processing on the signal after the frequency domain matched filtering processing to obtain a signal filtering result corresponding to the original echo signal. The server 104 may have stored therein a target hyperplane determined from the distances of sample data points of the target echo signal samples and noise signal samples in the high-dimensional feature space to the same hyperplane in the high-dimensional feature space. And mapping the signal filtering result to a high-dimensional feature space, and performing two-classification on the signal filtering result according to a target hyperplane in the high-dimensional feature space to obtain a signal class label corresponding to the signal filtering result. The server 104 may generate a detection result of the target echo signal according to the signal class label.

In this embodiment, the server is used to perform matched filtering processing on the original echo signal received by the sonar detection device, so that the signal-to-noise ratio of the echo signal can be improved, and the server is used to perform two classifications in the high-dimensional feature space based on the signal filtering result obtained by the matched filtering processing, so as to separate the noise signal and the target echo signal, and thus the detection accuracy of the target echo signal in the complex noise environment can be improved.

In an exemplary embodiment, as shown in fig. 2, a method for detecting a target echo signal is provided, and the method is applied to the server 104 in fig. 1 for illustration, and includes the following steps S202 to S208. Wherein:

Step S202, an original echo signal is acquired.

Wherein the original echo signal may be used to characterize the signal directly received by the sonar detection device. The original echo signal includes a noise signal and a target echo signal. The noise signal may comprise underwater environmental noise, noise of the sonar detection device itself, etc., and the target echo signal may be used to characterize an echo signal that matches the transmit signal of the sonar detection device.

For example, an echo signal processing program may be run in the server. The server may receive data signals transmitted by the sonar exploration device over a network. And analyzing and processing the data signals through an echo signal processing program to obtain original echo signals sent by sonar detection equipment in real time.

Step S204, carrying out frequency domain matched filtering processing and self-adaptive line spectrum enhancement processing on the original echo signals to obtain corresponding signal filtering results.

Wherein the matched filter may be used to characterize a linear filter having a maximum ratio of instantaneous power of the signal to average power of the noise at the output.

For example, the server may store therein an adaptive line spectrum enhancement algorithm based on frequency domain matched filtering and a transmission signal of the sonar detection device corresponding to the target echo signal. Optionally, in some embodiments, a frequency-domain matched filtering adaptive line spectrum enhancement algorithm may be used to perform matched filtering processing on the original echo signal, and then perform line spectrum enhancement processing on the original echo signal after matched filtering on a frequency domain based on the frequency domain characteristics of the transmission signal after matched filtering (which may be obtained by performing fourier transform on a frequency domain sample of the transmission signal), so as to obtain a signal filtering result corresponding to the original echo signal.

Step S206, mapping the signal filtering result to a high-dimensional feature space, and performing two-classification according to the target hyperplane in the high-dimensional feature space to obtain a signal class label corresponding to the signal filtering result.

The target hyperplane may be determined based on the distances of the target echo signal samples and the noise signal samples from the same hyperplane at the sample data points in the high-dimensional feature space. For example, the target hyperplane may be one optimal hyperplane that maximizes the distance of the target echo signal samples and the noise signal samples in the high-dimensional feature space from the hyperplane.

The signal class label may include a target echo signal class and a noise signal class.

For example, the server may perform an arithmetic process in advance using sample data points of the target echo signal sample and the noise signal sample mapped in the high-dimensional feature space to construct a target hyperplane for distinguishing sample data points of the target echo signal class and the noise signal class.

The server can map the signal filtering result corresponding to the original echo signal from the current low-dimensional data space to the high-dimensional feature space to obtain the mapping position of the signal filtering result in the high-dimensional feature space. And carrying out secondary classification on the signal filtering result according to the spatial relationship between the mapping position of the signal filtering result and the target hyperplane, and adopting the result obtained by the secondary classification as a signal class label corresponding to the signal filtering result.

Step S208, according to the signal type label, a detection result of the target echo signal is generated.

Alternatively, in some embodiments, when the signal class tag includes the target echo signal class, a detection result including the target echo signal in the signal filtering result may be generated. Or in other embodiments, when the signal class label does not include the target echo signal class, a detection result that does not include the target echo signal in the signal filtering result may be generated.

In the method for detecting the target echo signal, the original echo signal is subjected to frequency domain matching filtering processing and adaptive line spectrum enhancement processing, the processed signal filtering result is mapped to the high-dimensional feature space, the signal filtering result is subjected to two-classification according to the target hyperplane in the high-dimensional feature space, the detection result of the target echo signal is generated based on the signal class label obtained by two-classification, the signal-to-noise ratio of the original echo signal can be improved through the frequency domain matching filtering and adaptive line spectrum enhancement processing means, and the accuracy of the detection result is improved through the two-classification algorithm based on the support vector machine, so that the accurate identification and detection of the target echo signal under the complex noise environment are realized.

In an exemplary embodiment, as shown in fig. 3, the above step S204 may further include the following steps S302 to S308. Wherein:

Step S302, the original echo signal is input to a matched filter, and Fourier transform processing is carried out on the original echo signal by adopting the matched filter, so as to obtain a frequency domain signal corresponding to the original echo signal.

The matched filter may be used to represent an optimal linear filter, which is designed to maximize the output signal-to-noise ratio when receiving a signal, thereby improving the detection performance of the system. Illustratively, in the present embodiment, a linear filter whose filter coefficient is a default initial value may be used as the matched filter.

Because of the high time complexity of fourier transform of the original echo signal in the time domain, the server may transform the original echo signal to the frequency domain before performing the processing operations of the subsequent steps S304 to S308. For example, the initialized matched filter may be stored in the server. The original echo signal is input to a matched filter, and Fourier transform processing is carried out on the original echo signal through the matched filter so as to transform the original echo signal in the time domain into signal data in the frequency domain, namely, a frequency domain signal corresponding to the original echo signal is obtained.

Step S304, a matched filter is adopted to carry out matched filtering processing on the frequency domain signals, and a preliminary filtering result is obtained.

The preliminary filtering result may include a single frequency component and a noise component, among others. The single frequency component may be used to characterize a signal component corresponding to the target echo signal. The noise component may be used to characterize a signal component corresponding to the noise signal.

For example, the server may multiply the frequency domain signal with a matched filter using a transfer function corresponding to its own filter coefficient, and use the multiplication result obtained after the processing as the frequency domain signal after the matched filtering processing. Wherein the transfer function ‌ may be a mathematical expression describing the relationship between the filter output signal and the input signal, reflecting the response characteristics of the filter to signals of different frequencies.

And step S306, performing self-adaptive line spectrum enhancement processing on the single frequency component in the primary filtering result based on the target frequency domain characteristic by adopting a matched filter to obtain a target line spectrum signal.

The target frequency domain characteristic may be obtained by fourier transforming frequency domain samples of the transmission signal corresponding to the target echo signal.

Illustratively, since the single frequency component in the preliminary filtering result obtained in step S304 is easily buried in the noise component, the server may employ a matched filter to reuse an adaptive line spectrum enhancement algorithm (for example ADAPTIVE LINE ENHANCER, abbreviated as ALE, an adaptive line spectrum enhancer algorithm) on the preliminary filtering result, to suppress the uncorrelated component of the input signal thereof, and at the same time, to enhance the single frequency component corresponding to the target echo signal by attenuating a narrowband component with little attenuation. The server may perform fourier transform processing on the frequency domain samples of the transmission signal in advance, take the signal data result obtained after the frequency domain sample processing as a target frequency domain characteristic of the transmission signal subjected to matched filtering, and store the target frequency domain characteristic into the matched filter. And carrying out self-adaptive line spectrum enhancement processing on a primary filtering result obtained by matched filtering on the frequency domain based on the target frequency domain characteristic by adopting a matched filter so as to obtain a target line spectrum signal by a single frequency component in the primary filtering result.

Step S308, performing inverse Fourier transform processing on the target line spectrum signal by adopting a matched filter to obtain a signal filtering result in a time domain.

The server performs inverse fourier transform processing on the target line spectrum signal in the frequency domain by using a matched filter, for example, may perform arithmetic processing on the target line spectrum signal by using an inverse fast fourier transform algorithm, so as to transform the target line spectrum signal in the frequency domain into signal data in the time domain, that is, obtain a signal filtering result in the time domain output by the matched filter.

In the embodiment, the self-adaptive line spectrum enhancement algorithm based on the frequency domain matched filtering is adopted to process the original echo signals to obtain corresponding signal filtering results, so that the signal to noise ratio of the signals output after the processing of the matched filter can be greatly improved, the calculation time complexity in the matched filtering process can be simplified, and the generation efficiency of the signal filtering results can be improved. Meanwhile, as the single-frequency component corresponding to the target echo signal in the preliminary filtering result is easily submerged in the noise component corresponding to the noise signal, the signal-to-noise ratio of the output signal of the matched filter can be improved by adopting the adaptive line spectrum enhancement method in the embodiment, and noise and other interference can be removed or reduced.

In an exemplary embodiment, as shown in fig. 4, the above step S306 may further include the following steps S402 to S408. Wherein:

And step S402, multiplying the initial weight coefficient by the initial filtering result by adopting a matched filter to obtain the processed enhanced line spectrum signal.

Step S404, a matched filter is adopted to determine the minimum mean square error corresponding to the initial weight coefficient according to the target frequency domain characteristic and the enhanced line spectrum signal.

The weight coefficients of the matched filter, i.e. the conjugate of the signal spectrum, may be realized by the transfer function of the matched filter. The initial weight coefficients may be used to characterize default values for the matched filter initialization configuration.

For example, the server may multiply the preliminary filtering result using the initial weight coefficient stored in itself by using the matched filter, and take the multiplied signal data result as the enhanced line spectrum signal. The signal output expectations of the matched filter are determined according to the target frequency domain characteristics. And performing operation processing on the enhancement line spectrum signal obtained by processing the signal output expected and initial weight coefficient, and generating a minimum mean square error corresponding to the initial weight coefficient.

Step S406, a matched filter is adopted to dynamically adjust the initial weight coefficient according to the minimum mean square error based on the minimum mean square error criterion to obtain the target weight coefficient.

In step S408, a matched filter is used to multiply the preliminary filtering result by the target weight coefficient, so as to obtain a target line spectrum signal.

The Minimum Mean Square Error criterion, that is, minimum Mean Square Error, MMSE criterion for short, is a data rule that measures an index of the performance of a filter or an estimator by using a Minimum value of a Minimum Mean Square Error output by the filter or the estimator. In this embodiment, when the difference between the output obtained after the adaptive line spectrum enhancement processing performed by the matched filter and the signal output expectation is smaller, the obtained minimum mean square error value is smaller, which indicates that the performance of the weight coefficient currently used by the matched filter is better. Conversely, when the minimum mean square error is larger, the performance of the weight coefficient currently used by the matched filter is indicated to be poorer.

For example, the server may dynamically adjust the initial weight coefficient used by the matched filter according to the minimum mean square error calculated in step S404, for example, adjust the initial weight coefficient up or down, and calculate the minimum mean square error corresponding to the adjusted weight coefficient by referring to the above manner from step S402 to step S404. And determining a weight coefficient capable of enabling the minimum mean square error to reach the minimum value as a target weight coefficient based on a minimum mean square error criterion. And multiplying the primary filtering result by a target weight coefficient by adopting a matched filter to obtain a target line spectrum signal.

In this embodiment, the weight coefficient is calculated by an adaptive method, and the weight coefficient is dynamically adjusted based on a minimum mean square error criterion to enhance the single-frequency signal component, so that the signal-to-noise ratio of the target line spectrum signal can be increased.

In an exemplary embodiment, as shown in fig. 5, the above step S302 may further include the following steps S502 to S506. Wherein:

Step S502, adopting signal weight corresponding to a preset direction to carry out weighting processing on the original echo signals received by each array element in the multi-element array, and obtaining the processed weighted echo signals.

The preset direction may be used to represent the incident azimuth of the original echo signal.

The multi-element matrix may be used to represent a structure formed by a number of array elements (also called transducers) arranged according to a certain law. The purpose of the ‌ multiple array is to obtain the desired target beam pattern in space.

Array elements may be used to represent individual receivers or sensors in a sonar detection device that make up a sonar array. The array element can be an antenna, a microphone, a underwater acoustic sensor and the like. The array elements may be used to receive signals from a signal source.

For example, the server may store a signal weight corresponding to a preset direction, for example, a signal weight of 1 in the preset direction, a signal weight of 0.5 in a non-preset direction, or determine the corresponding signal weight according to an angle between the non-preset direction and the preset direction. And carrying out weighted operation processing on the original echo signals received by each array element in the multi-element array of the sonar detection equipment by adopting the signal weight corresponding to the preset direction, so as to obtain the processed weighted echo signals corresponding to each array element.

And step S504, overlapping the weighted echo signals corresponding to each array element to obtain a target beam signal corresponding to the multi-element array.

And step S506, carrying out Fourier transform processing on the target beam signal by adopting a matched filter to obtain a frequency domain signal.

For example, the server may superimpose weighted echo signals corresponding to each array element to form a target beam signal corresponding to the multi-element array. And carrying out Fourier transform processing on the target beam signal by adopting a matched filter corresponding to the target echo signal to obtain a frequency domain signal corresponding to the original beam signal in the frequency domain.

In this embodiment, the amplitude and the phase corresponding to each array element are adjusted by adopting a beam forming algorithm based on a multi-element array, so as to enhance the signal in the specific direction in the original echo signal, eliminate or suppress the interference and noise in other directions, improve the signal-to-noise ratio of the target beam signal, and simultaneously realize the estimation of the incident azimuth of the original echo signal.

In an exemplary embodiment, based on the embodiment provided in fig. 5, the method for detecting a target echo signal in the present application may further include:

and locating the target detection position corresponding to the target echo signal in the distance-azimuth spectrum according to the signal class label of the signal filtering result.

Wherein the range-azimuth spectrum can be used to describe the location of the object to be detected. The distance is the distance between the object to be detected and the sonar detection device. The azimuth is the azimuth of the object to be detected relative to the sonar detection device.

For example, the server may calculate the distance of the object to be detected from the sonar detection device based on the delay time of the original echo signal relative to the transmitted signal. And determining azimuth angles of the object to be detected and sonar detection equipment according to the signal intensity of the array elements of the target beam signals in each direction. Using the azimuth and the distance, a range-azimuth spectrum corresponding to the target beam signal is constructed. And determining whether signals under the target echo signal category exist in the signal filtering result according to the signal category labels obtained by performing two-dimensional classification on the signal filtering result in the high-dimensional feature space. Or the spatial position of the signal under the target echo signal class in the signal filtering result relative to the sonar detection device. Thereby realizing the technical effect of locating and obtaining the target detection position corresponding to the target echo signal in the distance-azimuth spectrum.

In this embodiment, by providing a visual range-azimuth spectrum, the target echo signal can be positioned, and the spatial position where the target echo signal is located is intuitively displayed, so that further research and processing on the target echo signal are facilitated.

In an exemplary embodiment, as shown in fig. 6, step S206 may include the following steps S602 to S606. Wherein:

step S602, normalizing the signal filtering result to obtain a normalized signal filtering result.

And step S604, mapping the normalized signal filtering result to a high-dimensional feature space by adopting a linear kernel function to obtain a corresponding linear separable data set.

For example, in the data preprocessing stage of the signal filtering result, the server may normalize the signal filtering result to obtain a normalized signal filtering result. And carrying out operation processing on the normalized signal filtering result by adopting a prestored linear kernel function to obtain a linear separable data set which is obtained after the normalized signal filtering result is mapped to a high-dimensional feature space.

Step S606, according to the position relation between the linear separable data set and the target hyperplane, the linear separable data set is classified into two categories to obtain the signal category label.

For example, the server may perform a two-classification of the linearly separable dataset according to a positional relationship of the linearly separable dataset to the target hyperplane. For example, when data points in the linearly separable data set are distributed above the target hyperplane, a classification result corresponding to the linearly separable data set may be determined as the target echo signal class. Otherwise, a classification result corresponding to the linearly separable data set may be determined as a noise signal classification.

In this embodiment, the signal classification efficiency can be improved by normalizing the signal filtering result. By mapping the low-dimensional signal filtering result to the high-dimensional feature space by adopting the linear kernel function, the problem that the feature vectors of the target echo signal and the noise signal cannot be linearly separated can be solved. By determining the signal category to which the signal filtering result belongs by using a support vector machine-based classification algorithm, the target detection problem can be converted into the classification problem, and the calculation complexity is reduced. Meanwhile, in the embodiment, the mapping is performed by adopting a linear kernel function, and the mapping efficiency of the linear separable data set can be improved.

In an exemplary embodiment, as shown in fig. 7, a method for detecting a target echo signal is also provided, which includes the following steps S702 to S710. Wherein:

step S702, obtaining an original echo signal received by each array element in the multi-element array.

Step S704, beam forming is performed on the original echo signals of each array element, so as to obtain a target beam signal corresponding to the multi-element array.

The server may perform weighting processing on the original echo signal received by each array element by using a signal weight corresponding to a preset direction, so as to enhance a signal in a specific preset direction, and eliminate or suppress interference and noise in other directions, thereby obtaining a weighted echo signal after processing. And overlapping the weighted echo signals corresponding to each array element to obtain the target beam signals corresponding to the multi-element array, thereby improving the output signal-to-noise ratio of the target beam signals and simultaneously realizing the estimation of the incidence azimuth of the target beam signals.

Step S706, processing the target beam signal based on the adaptive line spectrum enhancement algorithm of the frequency domain matched filtering to obtain a signal filtering result output by the matched filter.

Illustratively, as shown in fig. 8, an algorithm flow chart of an adaptive line spectrum enhancement technique based on frequency domain matched filtering is provided. The server may perform fast fourier transform processing on the target beam signal in the time domain through a matched filter as shown in fig. 8, to obtain a frequency domain signal corresponding in the frequency domain. And carrying out matched filtering treatment on the frequency domain signals through a matched filter to obtain a preliminary filtering result, and then carrying out self-adaptive line spectrum enhancement treatment on the preliminary filtering result to obtain corresponding target line spectrum signals. And carrying out inverse fast Fourier transform processing on the target line spectrum signal in the frequency domain to obtain a corresponding signal filtering result in the time domain.

Optionally, in some embodiments, the target beam signal input to the matched filter is as follows:

Wherein, Representing the beamformed (spatially filtered) output, i.e., the target beam signal as described in the present application.Representing the beam-forming weight vector(s),The guiding vector is represented by a vector of the guiding vector,Representing the data received by the mth array element,Representing gaussian white noise received by the array.

When (when)The target beam signal can be simplified as:

Wherein M represents the number of array elements, and the definition of the rest parameters is as defined above.

The noise observed by each array element is assumed to be random, the space-time uncorrelated, the mean value is 0, and the variance isIs a gaussian white noise of (c). Since the amplitudes of the echo signal and the noise signal only have an influence on the signal-to-noise ratio of the output signal. Thus, the above-mentioned first frequency domain signal can be further simplified into after ignoring the influence of the amplitude:

Wherein, A first frequency domain signal is input to a matched filter.The amplitude and the phase of the target echo signal corresponding to the transmitting signal are unknown.Band-limited white noise with an average value of 0.

In the frequency domain, the signal may be matched filtered by a matched filter with reference to:

Wherein, Is the second frequency domain signal.Is the transfer function of the matched filter.Is the first frequency domain signal.Is a spectral function of the target echo signal.Is a spectral function of the noise signal.

Is arranged at a preset time) Target echo signalMaximum instantaneous power signal-to-noise ratio of (c):

Wherein, Is the transfer function of the matched filter. k is an arbitrary constant, and generally 1 is desirable.Is a fourier transform coefficient.Representation ofIs a complex conjugate of (a) and (b).

Then further there are:

Wherein, Is the second frequency domain signal. k is an arbitrary constant.Is a spectral function of the target echo signal.Is a fourier transform coefficient.Is a spectral function of the noise signal. The remaining parameter definitions are set forth above.

As can be seen from the above, the result of the original echo signal after being processed by the matched filter of the frequency domain is that the second frequency domain signalComprising two parts of content. One part of the two parts of content isIt can be regarded as a target echo signal component containing a single frequency component. Another part isCan be seen as a band-limited white noise sum. The single frequency component of the first part is submerged in the noise of the second part, and the detection performance of the matched filter can be further improved through the means of line spectrum enhancement and the like.

Alternatively, as shown in fig. 9A, a distance-azimuth spectrum of a conventional signal processing result is provided, with the vertical axis representing its normalized amplitude, the horizontal axis representing its distance, and the vertical axis representing the azimuth angle. While in fig. 9B, a distance-azimuth spectrum based on the result of the adaptive line spectrum enhancement technique is shown, the vertical axis represents its normalized amplitude, the horizontal axis represents its distance, and the vertical axis represents azimuth. By comparison, it is apparent that the result obtained by the adaptive line spectrum enhancement technique based on matched filtering provided in fig. 9B significantly improves the signal-to-noise ratio of the echo signal compared with the result of fig. 9A.

Step S708, mapping the normalized filtering processing result to a high-dimensional feature space by adopting a linear kernel function, and performing two-classification according to a target hyperplane of the high-dimensional feature space to obtain a corresponding signal class label.

Alternatively, in some embodiments, the linear kernel function may be as follows:

Wherein, Can represent the utilization vectorSum vectorA kernel function formed by the inner product of (a).

Alternatively, in some embodiments, the target hyperplane may be determined by:

Wherein, Is a target hyperplane. Determining parameters according to the "maximum separation" principle (i.e. the principle that the distance from the data points of different classes to the hyperplane is the greatest)And。Is a variable of the relaxation,Is a penalty parameter for balance interval maximization and error minimization. By solving forAndTo determine an optimal target hyperplane, and thus to achieve separation of the noise signal and the target echo signal.

Step S710, generating a detection result of the target echo signal according to the signal type label, and positioning in a distance-azimuth spectrum corresponding to the target beam signal to obtain a target detection position corresponding to the target echo signal.

Alternatively, a schematic diagram of target echo signal detection for target beam signals over a range-azimuth spectrum using a constant false alarm algorithm is shown in fig. 10A. A result diagram of target echo signal detection over a range-azimuth spectrum by means of the support vector machine provided in the present embodiment is shown in fig. 10B. By comparison, it can be seen that the positioning of the target echo signal achieved by the technical means of the embodiment in fig. 10B has significantly less interference and noise than the detection result of the target echo signal obtained by the constant false alarm algorithm in fig. 10A.

In this embodiment, by adopting the beam forming technology of the multi-element array, the original echo signal is converted into the corresponding target beam signal, so that the signal in the specific preset direction can be enhanced, interference and noise in other directions can be eliminated or suppressed, and the output signal-to-noise ratio of the signal can be improved. By adopting the frequency domain-based adaptive matched filtering technology to carry out matched filtering and adaptive line spectrum enhancement, the signal to noise ratio of the signal filtering result can be improved. By adopting a support vector machine-based two-classification mode to determine the signal class label, the detection result of the target echo signal is further obtained, the low-dimensional data can be mapped into a high-dimensional feature space, and the target echo signal and the noise signal which cannot be linearly separated are converted into a linearly separable data set, so that the accurate detection and positioning of the target echo signal are realized, and meanwhile, the time complexity of the detection of the target echo signal can be reduced.

It should be understood that, although the steps in the flowcharts related to the embodiments described above are sequentially shown as indicated by arrows, these steps are not necessarily sequentially performed in the order indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in the flowcharts described in the above embodiments may include a plurality of steps or a plurality of stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of the steps or stages is not necessarily performed sequentially, but may be performed alternately or alternately with at least some of the other steps or stages.

Based on the same inventive concept, the embodiment of the application also provides a target echo signal detection device for realizing the target echo signal detection method. The implementation of the solution provided by the device is similar to the implementation described in the above method, so the specific limitation in the embodiments of the detection device for one or more target echo signals provided below may refer to the limitation of the detection method for the target echo signal hereinabove, and will not be repeated herein.

In an exemplary embodiment, as shown in fig. 11, there is provided a target echo signal detection device 1100, including a signal acquisition module 1102, a matched filtering module 1104, a signal classification module 1106, and a result generation module 1108, wherein:

The signal acquisition module 1102 is configured to acquire an original echo signal, where the original echo signal includes a noise signal and a target echo signal.

The matched filtering module 1104 is configured to perform frequency domain matched filtering processing and adaptive line spectrum enhancement processing on the original echo signal, so as to obtain a corresponding signal filtering result.

The signal classification module 1106 is configured to map the signal filtering result to a high-dimensional feature space, and perform two classifications according to a target hyperplane in the high-dimensional feature space, so as to obtain a signal class label corresponding to the signal filtering result, where the target hyperplane is determined according to distances between a target echo signal sample and a sample data point of a noise signal sample in the high-dimensional feature space and the same hyperplane.

The result generating module 1108 is configured to generate a detection result of the target echo signal according to the signal class label.

In an exemplary embodiment, the matched filtering module 1104 includes a frequency domain transforming unit configured to input an original echo signal to a matched filter, perform fourier transform processing on the original echo signal with the matched filter to obtain a frequency domain signal corresponding to the original echo signal, a frequency domain filtering unit configured to perform matched filter processing on the frequency domain signal with the matched filter to obtain a preliminary filtering result, where the preliminary filtering result includes a single frequency component and a noise component, a line spectrum enhancing unit configured to perform adaptive line spectrum enhancement processing on the single frequency component in the preliminary filtering result with the matched filter based on a target frequency domain characteristic to obtain a target line spectrum signal, where the target frequency domain characteristic is obtained by performing fourier transform on a frequency domain sample of a transmission signal corresponding to the target echo signal, and a time domain transforming unit configured to perform inverse fourier transform processing on the target line spectrum signal with the matched filter to obtain a signal filtering result in a time domain.

In an exemplary embodiment, the line spectrum enhancement unit is further configured to multiply the initial weight coefficient with the preliminary filtering result by using a matched filter to obtain a processed enhanced line spectrum signal, determine a minimum mean square error corresponding to the initial weight coefficient according to the target frequency domain characteristic and the enhanced line spectrum signal by using the matched filter, dynamically adjust the initial weight coefficient according to the minimum mean square error based on a minimum mean square error criterion by using the matched filter to obtain a target weight coefficient, and multiply the initial filtering result with the target weight coefficient by using the matched filter to obtain the target line spectrum signal.

In an exemplary embodiment, the frequency domain transforming unit is further configured to perform weighting processing on an original echo signal received by each array element in the multi-element array by using a signal weight corresponding to a preset direction, to obtain a processed weighted echo signal, superimpose the weighted echo signals corresponding to each array element to obtain a target beam signal corresponding to the multi-element array, and perform fourier transform processing on the target beam signal by using a matched filter, to obtain a frequency domain signal.

In an exemplary embodiment, the target echo signal detection device 1100 further includes a target positioning module, configured to construct a distance-azimuth spectrum corresponding to the target beam signal, and position a target detection position corresponding to the target echo signal in the distance-azimuth spectrum according to the signal class label of the signal filtering result.

In an exemplary embodiment, the signal classification module 1106 is further configured to normalize the signal filtering result to obtain a normalized signal filtering result, map the normalized signal filtering result to a high-dimensional feature space by using a linear kernel function to obtain a corresponding linear separable dataset, and perform two classifications on the linear separable dataset according to a positional relationship between the linear separable dataset and the target hyperplane to obtain a signal class label.

The modules in the target echo signal detection device 1100 may be implemented in whole or in part by software, hardware, or a combination thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.

In one exemplary embodiment, a computer device is provided, which may be a server, and the internal structure thereof may be as shown in fig. 12. The computer device includes a processor, a memory, an Input/Output interface (I/O) and a communication interface. The processor, the memory and the input/output interface are connected through a system bus, and the communication interface is connected to the system bus through the input/output interface. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer equipment is used for storing data such as original echo signals, matched filters, signal filtering results, target hyperplane, signal class labels, detection results and the like. The input/output interface of the computer device is used to exchange information between the processor and the external device. The communication interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a method of detecting a target echo signal.

It will be appreciated by those skilled in the art that the structure shown in FIG. 12 is merely a block diagram of some of the structures associated with the present inventive arrangements and is not limiting of the computer device to which the present inventive arrangements may be applied, and that a particular computer device may include more or fewer components than shown, or may combine some of the components, or have a different arrangement of components.

In an exemplary embodiment, a computer device is provided, comprising a memory and a processor, the memory having stored therein a computer program, the processor performing the steps of the method embodiments described above when the computer program is executed.

In an exemplary embodiment, a computer-readable storage medium is provided, on which a computer program is stored, which computer program, when being executed by a processor, carries out the steps of the method embodiments described above.

In an exemplary embodiment, a computer program product is provided, comprising a computer program which, when executed by a processor, implements the steps of the method embodiments described above.

Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, database, or other medium used in embodiments provided herein may include at least one of non-volatile memory and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high density embedded nonvolatile Memory, resistive random access Memory (RESISTIVE RANDOM ACCESS MEMORY, reRAM), magneto-resistive Memory (Magnetoresistive Random Access Memory, MRAM), ferroelectric Memory (Ferroelectric Random Access Memory, FRAM), phase change Memory (PHASE CHANGE Memory, PCM), graphene Memory, and the like. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory, and the like. By way of illustration, and not limitation, RAM can be in various forms such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), etc. The databases referred to in the embodiments provided herein may include at least one of a relational database and a non-relational database. The non-relational database may include, but is not limited to, a blockchain-based distributed database, and the like. The processor referred to in the embodiments provided in the present application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic unit, a data processing logic unit based on quantum computation, an artificial intelligence (ARTIFICIAL INTELLIGENCE, AI) processor, or the like, but is not limited thereto.

The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the present application.

The foregoing examples illustrate only a few embodiments of the application and are described in detail herein without thereby limiting the scope of the application. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the application, which are all within the scope of the application. Accordingly, the scope of the application should be assessed as that of the appended claims.

Claims

1. A method for detecting a target echo signal, the method comprising:

2. The method of claim 1, wherein performing frequency domain matched filtering and adaptive line spectrum enhancement on the original echo signal to obtain a corresponding signal filtering result comprises:

3. The method according to claim 2, wherein the performing adaptive line spectrum enhancement processing on the single frequency component in the preliminary filtering result by using the matched filter based on the target frequency domain characteristic to obtain a target line spectrum signal includes:

4. The method according to claim 2, wherein said performing fourier transform processing on said original echo signal using said matched filter to obtain a frequency domain signal corresponding to said original echo signal, comprises:

5. The method according to claim 4, wherein the method further comprises:

constructing a range-azimuth spectrum corresponding to the target beam signal;

6. The method of claim 1, wherein mapping the signal filtering result to a high-dimensional feature space and performing two classifications according to a target hyperplane in the high-dimensional feature space to obtain a signal class label corresponding to the signal filtering result comprises:

7. A device for detecting a target echo signal, the device comprising:

8. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1 to 6 when the computer program is executed.

9. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 6.

10. A computer program product comprising a computer program, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 6.