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CN119687892A - Collaborative navigation filtering method based on dynamic event triggering conditions - Google Patents

Collaborative navigation filtering method based on dynamic event triggering conditions Download PDF

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CN119687892A
CN119687892A CN202411588303.XA CN202411588303A CN119687892A CN 119687892 A CN119687892 A CN 119687892A CN 202411588303 A CN202411588303 A CN 202411588303A CN 119687892 A CN119687892 A CN 119687892A
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estimate
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CN119687892B (en
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战兴群
王翰禹
王士壮
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Shanghai Jiao Tong University
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Abstract

一种基于动态事件触发条件的协同导航滤波方法，通过信息发送方在每个采样时刻根据先验状态估计和绝对观测解算后验估计，根据先验状态估计解算预测估计；再计算后验估计与预测估计差的加权欧几里得范数并判断是否超过动态触发门限：当超过触发门限时，将后验估计发送给信息接收方后，由信息接收方根据接收到的状态估计进行协同导航解算，否则信息发送方不对外发送信息；最后信息接收方基于系统模型对信息发送方的状态进行估计，得到事件触发估计，并利用事件触发估计和相对观测进行协同导航解算。本发明将事件触发机制引入协同导航滤波器，载体在每个采样时刻根据事件触发条件来进行选择性的通信，通信方式由周期通信转变为事件触发通信，从而降低了通信频率的同时，对事件触发估计进行修正从而保证导航性能。

A collaborative navigation filtering method based on dynamic event trigger conditions, wherein the information sender solves the posterior estimate based on the prior state estimate and absolute observation at each sampling moment, and solves the predicted estimate based on the prior state estimate; then the weighted Euclidean norm of the difference between the posterior estimate and the predicted estimate is calculated and it is determined whether it exceeds the dynamic trigger threshold: when the trigger threshold is exceeded, the posterior estimate is sent to the information receiver, and the information receiver performs collaborative navigation solution based on the received state estimate, otherwise the information sender does not send information to the outside; finally, the information receiver estimates the state of the information sender based on the system model, obtains the event trigger estimate, and uses the event trigger estimate and relative observation to perform collaborative navigation solution. The present invention introduces an event trigger mechanism into a collaborative navigation filter, and the carrier selectively communicates according to the event trigger condition at each sampling moment, and the communication mode is changed from periodic communication to event triggered communication, thereby reducing the communication frequency and correcting the event trigger estimate to ensure navigation performance.

Description

Collaborative navigation filtering method based on dynamic event triggering condition
Technical Field
The invention relates to a technology in the field of collaborative navigation, in particular to a collaborative navigation filtering method based on a dynamic event triggering condition.
Background
In order to reduce the communication pressure of collaborative navigation, the prior art introduces an event triggering mechanism, and an information sender judges whether a triggering condition is met at each sampling moment, if not, communication is not carried out, so that the communication frequency is reduced. However, the existing trigger conditions are all static, and are difficult to adapt to dynamic scenes.
Disclosure of Invention
Aiming at the problems that the communication burden is overlarge and the static event triggering condition is difficult to adapt to a dynamic scene in the existing collaborative navigation system, the invention provides a collaborative navigation filtering method based on the dynamic event triggering condition, an event triggering mechanism is introduced into a collaborative navigation filter, a carrier selectively communicates according to the event triggering condition at each sampling moment, and the communication mode is changed from periodic communication to event triggering communication, so that the communication frequency is reduced, and the event triggering estimation is corrected to ensure the navigation performance.
The invention is realized by the following technical scheme:
The invention relates to a collaborative navigation filtering method based on a dynamic event triggering condition, which comprises the following steps:
Step 1, an information sender calculates posterior estimation according to prior state estimation and absolute observation at each sampling moment, and calculates predictive estimation according to prior state estimation;
Step 2, calculating a weighted Euclidean norm of a posterior estimation and predictive estimation difference and judging whether the weighted Euclidean norm exceeds a dynamic trigger threshold, wherein after the posterior estimation is sent to an information receiver when the trigger threshold is exceeded, the information receiver carries out collaborative navigation calculation according to the received state estimation, otherwise, the information sender does not send information to the outside;
And 3, estimating the state of the information sender by the information receiver based on the system model to obtain event trigger estimation, and performing collaborative navigation calculation by utilizing the event trigger estimation and relative observation.
The trigger threshold consists of a constant and a dynamic variable, wherein the dynamic variable gradually converges to zero along with time and accords with the characteristic of convergence along with time of a filtering error, so that the trigger threshold can adapt to the dynamic change of a scene, and when the weighted Euclidean norm of the difference between the posterior estimation and the predictive estimation is larger than the trigger threshold, the trigger condition is met.
Technical effects
The invention introduces a time related variable in the event triggering condition, so that the triggering threshold is reduced along with time, the information sender judges whether the difference value between the posterior estimation and the event triggering estimation exceeds the triggering threshold at each sampling moment, and if the difference value does not exceed the triggering threshold, communication is not carried out, thereby reducing the communication frequency. Compared with the prior art, the method and the device have the advantages that the periodic communication is converted into the event triggering communication, the communication frequency is greatly reduced, and the characteristic that the filtering error in the collaborative navigation filter converges along with time can be well adapted because the triggering threshold is reduced along with time, so that the system robustness is enhanced.
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FIG. 1 is a schematic diagram of a system according to the present invention;
FIG. 2 is a schematic diagram of a dynamic event triggering condition according to the present invention;
FIG. 3 is a schematic diagram of an implementation scenario of the present embodiment;
fig. 4 is a diagram showing the effect of the simulation experiment in this example.
Detailed Description
As shown in FIG. 1, this embodiment is a collaborative navigation system for implementing the method, which includes a prediction estimator, a posterior estimator and an event trigger condition judgment module located at an information sender, and a posterior estimator and a collaborative navigation filter module located at an information receiver, where the information sender and the information receiver sequentially pass through the posterior estimator and respectively calculate respective posterior estimates at each sampling time by using absolute observations, the information sender calculates the prediction estimate at each sampling time by using a system model through the prediction estimator, the event trigger condition judgment module judges whether a trigger condition is satisfied according to the prediction estimate and the posterior estimate, and when the trigger condition is satisfied, sends the posterior estimate to the information receiver, and when the trigger condition is not satisfied, does not send the posterior estimate to the information receiver, the collaborative navigation filter module calculates the event trigger estimate of the information sender according to the judgment result of the event trigger condition, and then performs collaborative navigation solution by using relative observations and the event trigger estimate, and updates the self posterior estimate.
The event trigger condition judging module comprises a difference value calculating unit, a trigger threshold calculating unit and a trigger condition judging unit, wherein the difference value calculating unit calculates a weighted Euclidean norm of the difference between the difference value calculating unit and the information sender according to posterior estimation and predictive estimation, the trigger threshold calculating unit calculates a numerical value of a dynamic trigger threshold according to time, and the trigger condition judging unit judges whether the event trigger condition is met according to the weighted Euclidean norm and the dynamic trigger threshold, and when the trigger condition is met, the information is sent to the information receiver.
The collaborative navigation filtering module comprises a relative observation acquisition unit, an event trigger estimation unit and a filtering unit, wherein the relative observation acquisition unit acquires relative observation between an information sender and the information receiver based on a sensor of the information receiver, the event trigger estimation unit estimates the state of the information sender according to an event trigger condition judgment result to obtain an event trigger estimation, and the filtering unit updates posterior estimation of the information receiver according to the relative observation and the event trigger estimation to obtain a collaborative navigation filtering result.
The embodiment relates to a collaborative navigation filtering method based on the system, which comprises the following steps:
Step one, a posterior estimator of an information sender and an information receiver calculates posterior estimation of each sampling moment by using prior state estimation and absolute observation respectively, wherein the posterior estimator specifically comprises the following steps: Wherein T represents the transpose, -1 represents the inverse of the matrix, the subscript k represents the moment k, the subscript k|k represents the posterior estimate of the moment k, the subscript k|k-1 represents the prior estimate of the moment γk, the superscript 1 represents the sender of the information, the superscript 2 represents the receiver of the information, Representing a posterior estimate of the sender of the k-time information,Representing the a-posteriori estimated covariance,Representing an a priori estimate of the sender of the k time instant information,Representing a priori estimated covariance; representing a posterior estimate of the k-time information receiver, Representing the a-posteriori estimated covariance,Representing an a priori estimate of the receiver of the k time information,Representing a priori estimated covariance, the absolute observations of the information sender areAbsolute observations of information recipients are Representing an absolute observation of the sender of the k-time information,The measurement matrix is represented by a set of data,A state vector representing the sender of the k-time information,Representing the measurement noise and obeying zero-mean Gaussian distribution, the noise covariance isRepresenting an absolute observation of the receiver of the k moment information,The measurement matrix is represented by a set of data,A state vector representing the recipient of the k-time information,Representing the measurement noise and obeying zero-mean Gaussian distribution, the noise covariance is
Step two, a prediction estimator of the information sender calculates the prediction estimation of the information sender at each sampling moment by using a system model, specifically: Wherein the superscript 1, y denotes the predicted estimate of the sender of the information, Representing a predictive estimate of the sender of the k-time information,Representing predictive estimation covariance, the system model of the information sender is that A state transition matrix is represented and is used to represent,Representing process noise and obeying zero-mean Gaussian distribution, the noise covariance is
Step three, a difference value calculation unit of the information sender event triggering condition judgment module calculates Euclidean norms of difference values of the posterior estimation and the predictive estimation, wherein the Euclidean norms are specifically as follows: wherein: Is the euclidean norm.
And fourthly, a trigger threshold calculation unit of the event trigger condition judgment module calculates a value of a dynamic trigger threshold according to time, specifically, Λ=delta+ρe -σt, wherein Λ is the dynamic trigger threshold, delta is a constant, the value range is 1-10, ρe -σt is a dynamic variable, ρ is a constant, the value range is 1-10, sigma is a dynamic factor, the value range is 0-1, and t is time.
Step five, a trigger condition judging unit of the event trigger condition judging module judges whether a trigger condition gamma k is met according to the Euclidean norm and the dynamic trigger threshold, and sends posterior estimation to an information receiver when the trigger condition is met, specifically:
as shown in fig. 2, when δ=3, ρ=2, and σ=1, the time-dependent diagram is shown.
Step six, a relative observation acquisition unit of the information receiver collaborative navigation filtering module acquires relative observation between an information sender and the information receiver, specifically: representing a relative observation at the moment k, Representing a measurement matrix of relative observations about the status of the sender of the information,Representing a measurement matrix of relative observations about the status of the information receiver,Representing the measurement noise and obeying zero-mean Gaussian distribution, the noise covariance is
Step seven, an event trigger estimation unit of the collaborative navigation filtering module judges the value of a trigger condition gamma k according to whether information is received or not, and estimates the state of an information sender, specifically: wherein the superscript 1, e denotes the time-triggered estimation, An event trigger estimate representing the sender of the information,Representing event-triggered estimation covariance, when the information receiver receives the information, then Otherwise γ k =0.
Step eight, updating posterior estimation of an information receiver by a filtering unit of the collaborative navigation filtering module according to event trigger estimation and relative observation, wherein the method specifically comprises the following steps: wherein the relative observation is Representing a relative observation at the moment k,Representing a measurement matrix of relative observations about the status of the sender of the information,Representing a measurement matrix of relative observations about the status of the information receiver,Representing the measurement noise and obeying zero-mean Gaussian distribution, the noise covariance is Representing the updated information receiver state estimate of the collaborative navigation filtering,Representing the corresponding covariance.
As shown in fig. 3, through simulation experiments of double unmanned aerial vehicle formation, the method is operated by using the simulation parameters shown in table 2 under the sensor configuration shown in table 1 by using the periodic filtering and the static event triggering filtering method as a comparison, through the simulation experiments, the obtained collaborative navigation filtering result is shown in fig. 4, and the communication times of different filtering methods are shown in table 3.
TABLE 1
Sensor configuration parameters Value of
Positioning noise standard deviation (m) of information sender satellite navigation receiver [3,3]
Information receiver satellite navigation receiver positioning noise standard deviation (m) [6,6]
Visual sensor azimuth measurement noise standard deviation (deg) 1
Ultra-wideband ranging noise standard deviation (m) 1
TABLE 2
TABLE 3 Table 3
Filtering method Number of communications
Periodic filtering 100
Static event triggered filtering 33
Collaborative navigation filtering based on dynamic event trigger conditions 23
As shown in Table 3, compared with the periodic filtering method and the static event triggering filtering method, the collaborative navigation communication frequency can be greatly reduced, and the navigation performance is basically consistent.
Compared with the prior art, the method has the advantages that the selective communication is carried out through the dynamic event triggering condition, the collaborative navigation communication frequency can be greatly reduced, the adaptability of the system to dynamic scenes is enhanced, and the seventh step and the eighth step are realized through the event triggering estimation and the collaborative navigation filtering algorithm, and when the event triggering condition is not met, the relative observation is fused through the event triggering estimation, so that the collaborative navigation performance can be ensured not to be obviously reduced due to the reduction of the communication frequency.
The foregoing embodiments may be partially modified in numerous ways by those skilled in the art without departing from the principles and spirit of the invention, the scope of which is defined in the claims and not by the foregoing embodiments, and all such implementations are within the scope of the invention.

Claims (6)

1.一种基于动态事件触发条件的协同导航滤波方法，其特征在于，包括：1. A collaborative navigation filtering method based on dynamic event triggering conditions, characterized by comprising: 步骤1、信息发送方在每个采样时刻根据先验状态估计和绝对观测解算后验估计，根据先验状态估计解算预测估计；Step 1: The information sender solves the posterior estimate based on the prior state estimate and the absolute observation at each sampling time, and solves the predictive estimate based on the prior state estimate; 步骤2、计算后验估计与预测估计差的加权欧几里得范数并判断是否超过动态触发门限：当超过触发门限时，将后验估计发送给信息接收方后，由信息接收方根据接收到的状态估计进行协同导航解算，否则信息发送方不对外发送信息；Step 2: Calculate the weighted Euclidean norm of the difference between the posterior estimate and the predicted estimate and determine whether it exceeds the dynamic trigger threshold: When the trigger threshold is exceeded, the posterior estimate is sent to the information receiver, and the information receiver performs collaborative navigation solution based on the received state estimate, otherwise the information sender does not send information externally; 步骤3、信息接收方基于系统模型对信息发送方的状态进行估计，得到事件触发估计，并利用事件触发估计和相对观测进行协同导航解算。Step 3: The information receiver estimates the state of the information sender based on the system model, obtains the event trigger estimate, and uses the event trigger estimate and relative observation to perform collaborative navigation solution. 2.根据权利要求1所述的基于动态事件触发条件的协同导航滤波方法，其特征是，所述的触发门限由常数和动态变量组成，其中：动态变量随时间逐渐收敛于零；当后验估计与预测估计差的加权欧几里得范数大于触发门限，表明后验估计与预测估计相差较大，则满足触发条件。2. According to the collaborative navigation filtering method based on dynamic event trigger conditions in claim 1, it is characterized in that the trigger threshold is composed of a constant and a dynamic variable, wherein: the dynamic variable gradually converges to zero over time; when the weighted Euclidean norm of the difference between the posterior estimate and the predicted estimate is greater than the trigger threshold, indicating that the posterior estimate is significantly different from the predicted estimate, the trigger condition is met. 3.一种实现权利要求1或2所述方法的协同导航系统，其特征在于，包括：位于信息发送方的预测估计器、后验估计器和事件触发条件判断模块以及位于信息接收方的后验估计器和协同导航滤波模块，其中：信息发送方和信息接收方依次通过后验估计器分别根据先验状态估计，利用绝对观测计算每个采样时刻各自的后验估计，信息发送方通过预测估计器，利用系统模型计算每个采样时刻的预测估计，事件触发条件判断模块根据预测估计和后验估计判断触发条件是否满足，当满足触发条件时，向信息接收方发送后验估计；当不满足触发条件，不向信息接收方发送后验估计，信息发送方的协同导航滤波模块根据事件触发条件的判断结果计算信息发送方的事件触发估计，然后利用相对观测和事件触发估计进行协同导航解算，对自身的后验估计进行更新。3. A collaborative navigation system for implementing the method described in claim 1 or 2, characterized in that it includes: a prediction estimator, a posterior estimator and an event trigger condition judgment module located at the information sender, and a posterior estimator and a collaborative navigation filtering module located at the information receiver, wherein: the information sender and the information receiver respectively calculate their respective posterior estimates at each sampling moment based on the prior state estimation through the posterior estimator in turn, using absolute observations; the information sender calculates the prediction estimate at each sampling moment using the system model through the prediction estimator; the event trigger condition judgment module judges whether the trigger condition is met based on the prediction estimate and the posterior estimate; when the trigger condition is met, the posterior estimate is sent to the information receiver; when the trigger condition is not met, the posterior estimate is not sent to the information receiver; the collaborative navigation filtering module of the information sender calculates the event trigger estimate of the information sender based on the judgment result of the event trigger condition, and then uses relative observations and event trigger estimates to perform collaborative navigation solution and update its own posterior estimate. 4.根据权利要求3所述的协同导航系统，其特征是，所述的事件触发条件判断模块包括：差值计算单元、触发门限计算单元以及触发条件判断单元，其中：差值计算单元根据信息发送方的后验估计和预测估计，计算二者之差的加权欧几里得范数，触发门限计算单元根据时间，计算动态触发门限的数值，触发条件判断单元根据加权欧几里得范数和动态触发门限，判断事件触发条件是否满足，当触发条件满足时，向信息接收方发送信息。4. The collaborative navigation system according to claim 3 is characterized in that the event trigger condition judgment module includes: a difference calculation unit, a trigger threshold calculation unit and a trigger condition judgment unit, wherein: the difference calculation unit calculates the weighted Euclidean norm of the difference between the posterior estimate and the predicted estimate of the information sender according to the two, the trigger threshold calculation unit calculates the value of the dynamic trigger threshold according to time, and the trigger condition judgment unit judges whether the event trigger condition is met based on the weighted Euclidean norm and the dynamic trigger threshold, and sends information to the information receiver when the trigger condition is met. 5.根据权利要求3所述的协同导航系统，其特征是，所述的协同导航滤波模块包括：相对观测获取单元、事件触发估计单元以及滤波单元，其中：相对观测获取单元基于信息接收方的传感器，获取信息发送方和信息接收方之间的相对观测，事件触发估计单元根据事件触发条件判断结果，对信息发送方的状态进行估计，得到事件触发估计，滤波单元根据相对观测和事件触发估计，对信息接收方的后验估计进行更新，得到协同导航滤波结果。5. The collaborative navigation system according to claim 3 is characterized in that the collaborative navigation filtering module includes: a relative observation acquisition unit, an event trigger estimation unit and a filtering unit, wherein: the relative observation acquisition unit acquires the relative observation between the information sender and the information receiver based on the sensor of the information receiver, the event trigger estimation unit estimates the state of the information sender according to the result of the event trigger condition judgment to obtain the event trigger estimation, and the filtering unit updates the posterior estimation of the information receiver according to the relative observation and the event trigger estimation to obtain the collaborative navigation filtering result. 6.一种基于权利要求3-6中任一所述系统给的协同导航滤波方法，其特征在于，包括：6. A collaborative navigation filtering method based on the system according to any one of claims 3 to 6, characterized in that it comprises: 步骤一、信息发送方和信息接收方的后验估计器分别利用先验状态估计和绝对观测计算每个采样时刻的后验估计，具体为： 其中：T表示转置，-1表示矩阵的逆，下标k表示k时刻，下标k|k表示k时刻的后验估计，下标k|k-1表示k时刻的先验估计，上标1表示信息发送方，上标2表示信息接收方，表示k时刻信息发送方的后验估计，表示后验估计协方差，表示k时刻信息发送方的先验估计，表示先验估计协方差；表示k时刻信息接收方的后验估计，表示后验估计协方差，表示k时刻信息接收方的先验估计，表示先验估计协方差，信息发送方的绝对观测为信息接收方的绝对观测为 表示k时刻信息发送方的绝对观测，表示量测矩阵，表示k时刻信息发送方的状态向量，表示量测噪声，且服从零均值高斯分布，噪声协方差为 表示k时刻信息接收方的绝对观测，表示量测矩阵，表示k时刻信息接收方的状态向量，表示量测噪声，且服从零均值高斯分布，噪声协方差为 Step 1: The a posteriori estimators of the information sender and the information receiver respectively use the prior state estimate and the absolute observation to calculate the a posteriori estimate at each sampling time, specifically: Where: T represents transpose, -1 represents the inverse of the matrix, subscript k represents time k, subscript k|k represents the posterior estimate at time k, subscript k|k-1 represents the prior estimate at time k, superscript 1 represents the sender of the information, and superscript 2 represents the receiver of the information. represents the posterior estimate of the sender of the information at time k, represents the posterior estimated covariance, represents the prior estimate of the sender of information at time k, represents the prior estimated covariance; represents the posterior estimate of the information receiver at time k, represents the posterior estimated covariance, represents the prior estimate of the information receiver at time k, represents the prior estimated covariance, and the absolute observation of the information sender is The absolute observation of the information receiver is represents the absolute observation of the sender of the information at time k, represents the measurement matrix, represents the state vector of the sender at time k, represents the measurement noise and obeys a zero-mean Gaussian distribution. The noise covariance is represents the absolute observation of the information receiver at time k, represents the measurement matrix, represents the state vector of the information receiver at time k, represents the measurement noise and obeys a zero-mean Gaussian distribution. The noise covariance is 步骤二、信息发送方的预测估计器利用系统模型计算每个采样时刻信息发送方的预测估计，具体为：其中：上标1,Y表示信息发送方的预测估计，表示k时刻信息发送方的预测估计，表示预测估计协方差，信息发送方的系统模型为 表示状态转移矩阵，表示过程噪声，且服从零均值高斯分布，噪声协方差为 Step 2: The prediction estimator of the information sender uses the system model to calculate the prediction estimate of the information sender at each sampling time, specifically: Where: superscript 1, Y represents the prediction estimate of the information sender, represents the prediction estimate of the sender of the information at time k, represents the predicted estimated covariance, and the system model of the information sender is represents the state transfer matrix, represents process noise and obeys zero-mean Gaussian distribution. The noise covariance is 步骤三、信息发送方事件触发条件判断模块的差值计算单元根据后验估计和预测估计计算二者差值的欧几里得范数，具体为：其中：为欧几里得范数；Step 3: The difference calculation unit of the event trigger condition judgment module of the information sender calculates the Euclidean norm of the difference between the posterior estimate and the predicted estimate according to the posterior estimate and the predicted estimate, specifically: in: is the Euclidean norm; 步骤四、事件触发条件判断模块的触发门限计算单元根据时间计算动态触发门限的数值，具体为Λ＝δ+ρe-σt，其中：Λ为动态触发门限，δ为常数，其取值范围为1-10，ρe-σt为动态变量，ρ为常数，其取值范围为1-10，σ为动态因子，其取值范围为0-1，t为时间；Step 4: The trigger threshold calculation unit of the event trigger condition judgment module calculates the value of the dynamic trigger threshold according to the time, specifically Λ=δ+ρe -σt , where: Λ is the dynamic trigger threshold, δ is a constant, and its value range is 1-10, ρe -σt is a dynamic variable, ρ is a constant, and its value range is 1-10, σ is a dynamic factor, and its value range is 0-1, and t is time; 步骤五、事件触发条件判断模块的触发条件判断单元根据欧几里得范数和动态触发门限判断触发条件γk是否满足，并在满足触发条件时向信息接收方发送后验估计，具体为： Step 5: The trigger condition judgment unit of the event trigger condition judgment module judges whether the trigger condition γ k is met according to the Euclidean norm and the dynamic trigger threshold, and sends a posteriori estimation to the information receiver when the trigger condition is met, specifically: 步骤六、信息接收方协同导航滤波模块的相对观测获取单元获得信息发送方和信息接收方之间的相对观测，具体为： 表示k时刻的相对观测，表示相对观测关于信息发送方状态的量测矩阵，表示相对观测关于信息接收方状态的量测矩阵，表示量测噪声，且服从零均值高斯分布，噪声协方差为 Step 6: The relative observation acquisition unit of the cooperative navigation filtering module of the information receiving party obtains the relative observation between the information sending party and the information receiving party, specifically: represents the relative observation at time k, represents the measurement matrix of the relative observation about the state of the information sender, represents the measurement matrix of the relative observation with respect to the state of the information receiver, represents the measurement noise and obeys a zero-mean Gaussian distribution. The noise covariance is 步骤七、协同导航滤波模块的事件触发估计单元根据是否接收到信息判断触发条件γk的取值，并对信息发送方的状态进行估计，具体为： 其中：上标1,E表示事件触发估计，表示信息发送方的事件触发估计，表示事件触发估计协方差，当信息接收方接收到信息，则γk＝1，否则γk＝0；Step 7: The event trigger estimation unit of the collaborative navigation filtering module determines the value of the trigger condition γk according to whether the information is received, and estimates the state of the information sender, specifically: Where: superscript 1, E represents event trigger estimation, represents the event trigger estimate of the message sender, represents the event-triggered estimated covariance. When the information receiver receives the information, γ k = 1, otherwise γ k = 0; 步骤八、协同导航滤波模块的滤波单元根据事件触发估计和相对观测对信息接收方的后验估计进行更新，具体为： 其中：相对观测为 表示k时刻的相对观测，表示相对观测关于信息发送方状态的量测矩阵，表示相对观测关于信息接收方状态的量测矩阵，表示量测噪声，且服从零均值高斯分布，噪声协方差为 表示协同导航滤波更新后的信息接收方状态估计，表示对应的协方差。Step 8: The filter unit of the collaborative navigation filter module updates the a posteriori estimate of the information receiver according to the event trigger estimate and the relative observation, specifically: Where: Relative observation is represents the relative observation at time k, represents the measurement matrix of the relative observation about the state of the information sender, represents the measurement matrix of the relative observation with respect to the state of the information receiver, represents the measurement noise and obeys a zero-mean Gaussian distribution. The noise covariance is represents the state estimate of the information receiver after the collaborative navigation filter is updated, represents the corresponding covariance.
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