Collaborative navigation filtering method based on dynamic event triggering condition

Abstract

The invention relates to a collaborative navigation filtering method based on a dynamic event triggering condition, which comprises the following steps of: calculating posteriori estimation according to prior state estimation and absolute observation at each sampling moment by an information sender, and calculating predictive estimation according to the prior state estimation; the weighted Euclidean norm of the posteriori estimation and the predicted estimation difference is calculated, and whether the weighted Euclidean norm exceeds a dynamic trigger threshold or not is judged: when the weighted Euclidean norm exceeds the trigger threshold, the posteriori estimation is sent to an information receiver, and the information receiver carries out collaborative navigation solution according to the received state estimation, otherwise, the information sender does not send information to the outside; and finally, the information receiver estimates the state of the information sender based on the system model to obtain event trigger estimation, and cooperative navigation calculation is performed by using the event trigger estimation and relative observation. According to the invention, an event triggering mechanism is introduced into a collaborative navigation filter, a carrier performs selective communication at each sampling moment according to event triggering conditions, and a communication mode is converted from periodic communication to event triggering communication, so that the communication frequency is reduced, and event triggering estimation is corrected to ensure the navigation performance.

Description

Technical Field

[0001] The present invention relates to a technology in the field of collaborative navigation, in particular to a collaborative navigation filtering method based on dynamic event triggering conditions. Background Art

[0002] In order to reduce the communication pressure of collaborative navigation, the existing technology introduces an event trigger mechanism. The information sender determines whether the trigger condition is met at each sampling moment. If not, no communication is performed, thereby reducing the communication frequency. However, the existing trigger conditions are static and difficult to adapt to dynamic scenarios. Summary of the invention

[0003] Aiming at the problems in existing collaborative navigation systems of excessive communication burden and difficulty of static event triggering conditions to adapt to dynamic scenarios, the present invention proposes a collaborative navigation filtering method based on dynamic event triggering conditions, introduces an event triggering mechanism into the collaborative navigation filter, and the carrier selectively communicates according to the event triggering conditions 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 triggering estimation to ensure navigation performance.

[0004] The present invention is achieved through the following technical solutions:

[0005] The present invention relates to a collaborative navigation filtering method based on dynamic event triggering conditions, comprising:

[0006] 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;

[0007] 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;

[0008] 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.

[0009] The trigger threshold is composed of a constant and a dynamic variable, wherein: the dynamic variable gradually converges to zero over time, which is consistent with the characteristic that the filtering error converges over time and can adapt to the dynamic changes of the scene; 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 differs greatly from the predicted estimate, the trigger condition is met. Technical Effects

[0010] The present invention introduces a time-related variable into the event trigger condition, so that the trigger threshold decreases with time. The information sender determines whether the difference between the posterior estimate and the event trigger estimate exceeds the trigger threshold at each sampling moment. If it does not exceed the threshold, no communication is performed, thereby reducing the communication frequency. Compared with the prior art, the present invention transforms periodic communication into event-triggered communication, which greatly reduces the communication frequency. Since the trigger threshold decreases with time, it can well adapt to the characteristics of the filter error in the collaborative navigation filter converging with time, thereby enhancing the robustness of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 It is a schematic diagram of the system of the present invention;

[0012] Figure 2 A schematic diagram of the triggering conditions of dynamic events of the present invention;

[0013] Figure 3 This is a schematic diagram of a scenario for implementing this embodiment;

[0014] Figure 4 This is a simulation experiment effect diagram of this embodiment. DETAILED DESCRIPTION

[0015] like Figure 1 As shown, this embodiment involves a collaborative navigation system for implementing the above method, including: 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 use the posterior estimator to calculate their respective posterior estimates at each sampling moment based on the prior state estimation and absolute observation in turn, the information sender uses the prediction estimator to calculate the prediction estimate at each sampling moment using the system model, the event trigger condition judgment module judges whether the trigger condition is met based on the prediction estimate and the posterior estimate, and 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 the relative observation and the event trigger estimate to perform collaborative navigation solution and update its own posterior estimate.

[0016] 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 according to the weighted Euclidean norm and the dynamic trigger threshold, and sends information to the information receiver when the trigger condition is met.

[0017] 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 event trigger condition judgment result, and obtains 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, and obtains the collaborative navigation filtering result.

[0018] This embodiment relates to a collaborative navigation filtering method based on the above system, including:

[0019] 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 γk, 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 of information 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

[0020] 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

[0021] 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.

[0022] 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 whose value range is 1-10, ρe -σt is a dynamic variable, ρ is a constant whose value range is 1-10, σ is a dynamic factor whose value range is 0-1, and t is time.

[0023] Step 5: The trigger condition judgment unit of the event trigger condition judgment module judges the trigger condition γ according to the Euclidean norm and the dynamic trigger threshold k Whether it is satisfied, and send the posterior estimate to the information receiver when the trigger condition is met, specifically:

[0024] like Figure 2 As shown, it is a schematic diagram of the change over time when δ=3, ρ=2, σ=1.

[0025] 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

[0026] Step 7: The event trigger estimation unit of the collaborative navigation filtering module determines the trigger condition γ based on whether the information is received k The value of 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, Indicates the event triggers the estimated covariance. When the information receiver receives the information, Otherwise k =0.

[0027] 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.

[0028] like Figure 3 As shown, after the simulation experiment of the dual UAV formation, the periodic filtering and static event trigger filtering methods are used as controls. Under the sensor configuration shown in Table 1, the above method is run with the simulation parameters shown in Table 2. After the simulation experiment, the cooperative navigation filtering results that can be obtained are as follows Figure 4 The communication times of different filtering methods are shown in Table 3.

[0029] Table 1 Sensor Configuration Parameters value Standard deviation of positioning noise of satellite navigation receiver of information sender (m) [3,3] Information receiving satellite navigation receiver positioning noise standard deviation (m) [6,6] Vision sensor orientation measurement noise standard deviation (deg) 1 UWB ranging noise standard deviation (m) 1

[0030] Table 2

[0031] Table 3 Filtering method Number of communications Periodic filtering 100 Static event triggered filtering 33 Collaborative navigation filtering based on dynamic event triggering conditions 23

[0032] As shown in Table 3, compared with the periodic filtering method and the static event triggered filtering method, the present invention can significantly reduce the collaborative navigation communication frequency, and the navigation performance is basically the same.

[0033] Compared with the prior art, the present method selectively communicates through dynamic event trigger conditions, which can significantly reduce the frequency of collaborative navigation communication and enhance the system's adaptability to dynamic scenes. Steps seven and eight use event trigger estimation and collaborative navigation filtering algorithms to fuse relative observations using event trigger estimation when the event trigger conditions are not met, thereby ensuring that the collaborative navigation performance will not be significantly reduced due to the reduction in communication frequency.

[0034] The above-mentioned specific implementation can be partially adjusted in different ways by those skilled in the art without departing from the principle and purpose of the present invention. The protection scope of the present invention shall be based on the claims and shall not be limited by the above-mentioned specific implementation. Each implementation scheme within its scope shall be subject to the constraints of the present invention.

Claims

1. A collaborative navigation filtering method based on dynamic event triggering conditions, characterized in that: include: 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; 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; 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. The collaborative navigation filtering method based on dynamic event triggering conditions according to claim 1 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 differs greatly from the predicted estimate, the trigger condition is met.

3. A collaborative navigation system for implementing the method of claim 1 or 2, characterized in that: include: The information sender includes a prediction estimator, a posteriori estimator and an event trigger condition judgment module, and the information receiver includes a posteriori estimator and a collaborative navigation filtering module, wherein: the information sender and the information receiver respectively use the posteriori estimator to calculate their respective posteriori estimates at each sampling moment based on the prior state estimate and absolute observation, the information sender uses the prediction estimator to calculate the prediction estimate at each sampling moment using the system model, the event trigger condition judgment module judges whether the trigger condition is met based on the prediction estimate and the posteriori estimate, and sends the posteriori estimate to the information receiver when the trigger condition is met; when the trigger condition is not met, the posteriori 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 the relative observation and the event trigger estimate to perform collaborative navigation solution and update its own posteriori estimate.

4. The collaborative navigation system according to claim 3, 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 according to the weighted Euclidean norm and the dynamic trigger threshold, and sends information to the information receiver when the trigger condition is met.

5. The collaborative navigation system according to claim 3, 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 event trigger condition judgment result, and obtains 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, and obtains the collaborative navigation filtering result.

6. A collaborative navigation filtering method based on the system described in any one of claims 3 to 6, characterized in that: include: 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 of information 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 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 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; 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 whose value range is 1-10, ρe -σt is a dynamic variable, ρ is a constant, its value range is 1-10, σ is a dynamic factor, its value range is 0-1, and t is time; Step 5: The trigger condition judgment unit of the event trigger condition judgment module judges the trigger condition γ according to the Euclidean norm and the dynamic trigger threshold k Whether it is satisfied, and send the posterior estimate to the information receiver when the trigger condition is met, specifically: 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 Step 7: The event trigger estimation unit of the collaborative navigation filtering module determines the trigger condition γ based on whether the information is received k The value of 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; 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.

Images