Passive cooperative tracking method based on Sage-Husa adaptive filtering

Abstract

The present invention provides a passive collaborative tracking method based on Sage-Husa adaptive filtering, which constructs a system recursive model and an observation model, obtains the collaborative positioning target position as the initial value of the system state; generates a Sigma point set, obtains a one-step prediction value of the state quantity and the state covariance; generates a state prediction Sigma point set, obtains the measurement prediction value and measurement covariance of the sensor; obtains the residual amount, and stores the residual amount in a storage pool; determines whether the current storage pool is full, and if the current storage pool is full, estimates the measurement noise according to the residual amount to obtain the measurement noise covariance, and updates the measurement covariance; if the current storage pool is not full, updates the measurement covariance according to the initialized measurement noise covariance; obtains the filter gain, updates the state quantity and state covariance, enters the next moment, and returns to generate the Sigma point set according to the state quantity at the previous moment. The technical solution of the present invention can solve the technical problem of limited passive collaborative tracking performance in the prior art.

Description

Technical Field

[0001] The present invention relates to the technical field of passive collaborative tracking, and in particular to a passive collaborative tracking method based on Sage-Husa adaptive filtering. Background Art

[0002] Passive collaborative positioning, with its advantages of good concealment and high reliability, has attracted increasing attention in modern military warfare. Target tracking is a crucial component of passive positioning systems and a crucial guarantee for accurately locating target radiation sources. The complex and changing battlefield environment places higher demands on the tracking performance of passive positioning systems. Traditional tracking methods based on ideal systems assume that the system noise characteristics are known and statistically stable. However, the variability of the natural electromagnetic environment, the unpredictability of environmental disturbances, and the limitations of measurement methods all make it impossible to accurately obtain prior information on noise. When the actual noise characteristics do not match the model assumptions, tracking filter performance degrades, and even filter divergence occurs. Summary of the Invention

[0003] The present invention aims to solve at least one of the technical problems existing in the prior art.

[0004] In one aspect of the present invention, a passive collaborative tracking method based on Sage-Husa adaptive filtering is provided, and the passive collaborative tracking method based on Sage-Husa adaptive filtering comprises: constructing a recursive model and an observation model of a dual-sensor passive collaborative tracking filtering system, obtaining the collaborative positioning target position as the initial value of the system state according to the sensor measurement value; generating a Sigma point set according to the state quantity at the previous moment, obtaining a one-step prediction value of the state quantity and a one-step prediction value of the state covariance according to the Sigma point set; generating a state prediction Sigma point set according to the one-step prediction value of the state quantity and the one-step prediction of the state covariance, mapping the state prediction Sigma point set to the measurement domain to obtain the measurement prediction value of the sensor, and obtaining the quantity according to the measurement prediction value of the sensor. The method adopts the method of measuring the covariance of the sensor's observation and measurement prediction values, obtaining the residual value and storing the residual value in the storage pool; judging whether the current storage pool is full, if the current storage pool is full, estimating the measurement noise according to the residual value by using the nonlinear system noise estimator based on the residual statistics to obtain the measurement noise covariance, and updating the measurement covariance according to the measurement noise covariance; if the current storage pool is not full, updating the measurement covariance according to the initialized measurement noise covariance; obtaining the filter gain according to the updated measurement covariance, updating the state value and state covariance according to the filter gain, outputting the target position to be tracked at the current moment and entering the next moment, returning to generating the Sigma point set according to the state value at the previous moment, so as to complete the passive collaborative tracking based on the Sage-Husa adaptive filter.

[0005] Further, according to Generate k-1 time Sigma point set χ k-1 , where λ = α 2 (L+κ)-L, L is the dimension of the state quantity, α and κ determine the degree of dispersion of Sigma points; P k-1 is the state covariance at time k-1, X k-1 is the state quantity of the system at time k-1.

[0006] Further, according to χ i,k / k-1 =f(χ i,k-1 ) obtains the one-step prediction value of the state quantity, where is the one-step prediction value of the state quantity, W i m is the weight coefficient of Sigma point, f(·) is the state transfer function, χ i,k-1 is the Sigma point set χ k-1 The i-th Sigma point; according to Get the one-step forecast value of the state covariance, where P k / k-1 is the one-step forecast value of the state covariance, β is used to adjust the accuracy of the Sigma point variance, and Q is the system process noise covariance.

[0007] Further, according to Generate state prediction Sigma point set χ k / k-1 .

[0008] Further, according to Z i,k / k-1 =h k (χ i,k / k-1 ) obtains the sensor's measured predicted value, where and are the measured predicted values ​​of the first sensor and the second sensor respectively, i,k / k-1 Sigma point set χ is the state prediction k / k-1 The i-th Sigma point, h k (·) is the observation function of the sensor at time k.

[0009] Further, according to Get the measurement covariance, where P zz,k / k-1 is the measurement covariance.

[0010] Furthermore, if the current storage pool is full, Get the measurement noise covariance, where is the measurement noise covariance at time k, is the k-1 moment measurement noise covariance, d kis the weighting coefficient at time k, 0<b<1,P εε is the residual covariance, j=[k-N+1,…,k-1,k],ε k is the residual at time k, Z k is the observed value of the sensor at time k; the residual value set in the storage pool is ξ={ε k-N+1 ,…,ε j ,…,ε k-1 ,ε k}, N is an integer.

[0011] Furthermore, if the current storage pool is full, Update the measurement covariance, where is the updated measurement covariance.

[0012] Furthermore, if the current storage pool is not full, Update the measurement covariance, where is the initial measurement noise covariance.

[0013] Further, according to Get the filter gain, where K k is the filter gain at time k; according to Update the state according to Update the state covariance, where is the updated state quantity, P k is the updated state covariance.

[0014] The technical solution of the present invention provides a passive collaborative tracking method based on Sage-Husa adaptive filtering. This passive collaborative tracking method applies the Sage-Husa adaptive filtering method to the traditional unscented Kalman filter, improves the adaptive noise estimator, estimates the measurement noise based on the residual using a nonlinear system noise estimator based on residual statistics, and updates the measurement covariance based on the storage of the residual. This method can enhance the adaptability of the target tracking system to the environment and improve tracking performance. Compared with the existing technology, the technical solution of the present invention can solve the technical problem of limited passive collaborative tracking performance in the existing technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The accompanying drawings are included to provide a further understanding of the embodiments of the present invention, constitute a part of the specification, illustrate the embodiments of the present invention, and together with the description, explain the principles of the present invention. Obviously, the drawings described below are only some embodiments of the present invention, and those skilled in the art can derive other drawings based on these drawings without inventive effort.

[0016] Figure 1 A schematic flow chart of a passive collaborative tracking method based on Sage-Husa adaptive filtering according to a specific embodiment of the present invention is shown;

[0017] Figure 2 A schematic diagram of a simulation of a test verification scenario provided according to a specific embodiment of the present invention is shown;

[0018] Figure 3 A comparison chart of the results of various tracking filtering algorithms provided according to a specific embodiment of the present invention is shown. DETAILED DESCRIPTION

[0019] It should be noted that, in the absence of conflict, the embodiments in this application and the features in the embodiments can be combined with each other. The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. The following description of at least one exemplary embodiment is actually only illustrative and is in no way intended to limit the present invention and its application or use. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0020] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present application. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, they indicate the presence of features, steps, operations, devices, components and / or combinations thereof.

[0021] Unless otherwise specifically stated, the relative arrangement of the parts and steps, numerical expressions and numerical values ​​set forth in these embodiments do not limit the scope of the present invention. At the same time, it should be understood that, for ease of description, the sizes of the various parts shown in the accompanying drawings are not drawn according to actual proportional relationships. The technology, methods and equipment known to those of ordinary skill in the relevant art may not be discussed in detail, but in appropriate cases, the technology, methods and equipment should be considered as a part of the specification. In all examples shown and discussed here, any specific value should be interpreted as being merely exemplary, rather than as a limitation. Therefore, other examples of the exemplary embodiments can have different values. It should be noted that similar numbers and letters represent similar items in the following drawings, and therefore, once an item is defined in an accompanying drawing, it does not need to be further discussed in subsequent drawings.

[0022] like Figure 1 As shown, according to a specific embodiment of the present invention, a passive collaborative tracking method based on Sage-Husa adaptive filtering is provided, and the passive collaborative tracking method based on Sage-Husa adaptive filtering includes: constructing a recursive model and an observation model of a dual-sensor passive collaborative tracking filtering system, obtaining the collaborative positioning target position as the initial value of the system state according to the sensor measurement value; generating a Sigma point set according to the state quantity at the previous moment, obtaining a one-step prediction value of the state quantity and a one-step prediction value of the state covariance according to the Sigma point set; generating a state prediction Sigma point set according to the one-step prediction value of the state quantity and the one-step prediction of the state covariance, mapping the state prediction Sigma point set to the measurement domain to obtain the measurement prediction value of the sensor, and obtaining the measurement prediction value of the sensor according to the measurement prediction value of the sensor. Obtain measurement covariance; obtain residuals based on the sensor's observed values ​​and measurement prediction values, and store the residuals in a storage pool; determine whether the current storage pool is full. If the current storage pool is full, estimate the measurement noise based on the residual using a nonlinear system noise estimator based on residual statistics to obtain measurement noise covariance, and update the measurement covariance based on the measurement noise covariance; if the current storage pool is not full, update the measurement covariance based on the initialized measurement noise covariance; obtain the filter gain based on the updated measurement covariance, update the state and state covariance based on the filter gain, output the target position tracked at the current moment and enter the next moment, and return to generate a Sigma point set based on the state at the previous moment to complete passive collaborative tracking based on Sage-Husa adaptive filtering.

[0023] This configuration method provides a passive collaborative tracking method based on Sage-Husa adaptive filtering. This method applies the Sage-Husa adaptive filtering method to the traditional unscented Kalman filter, improves the adaptive noise estimator, estimates the measurement noise based on the residual using a nonlinear system noise estimator based on residual statistics, and updates the measurement covariance based on the stored residual. This method can enhance the adaptability of the target tracking system to the environment and improve tracking performance. Compared with the prior art, the technical solution of the present invention can solve the technical problem of limited passive collaborative tracking performance in the prior art.

[0024] In the present invention, in order to improve the tracking filtering performance in an environment with unknown noise characteristics, a recursive model and an observation model of a dual-sensor passive collaborative tracking filtering system are first constructed, and the collaborative positioning target position is obtained as the initial value of the system state according to the sensor measurement value.

[0025] As a specific embodiment of the present invention, according to X k =F·X k-1 +W k Construct a recursive model of a dual-sensor passive cooperative tracking filtering system, where X k is the state quantity of the system at time k, X k-1 is the state quantity of the system at time k-1, F is the state transfer matrix, W k is the system process noise at time k, and its covariance is Q.

[0026] In this embodiment, it is further possible to k =h k (X k )+V k Construct an observation model, where Z k is the sensor observation at time k, and are the observations of the first sensor and the second sensor at time k, h k (·) is the observation function of the sensor at time k, V k is the observation noise of the sensor at time k, and are the observation noises of the first and second sensors at time k, respectively, with a covariance of R.

[0027] In the present invention, after obtaining the initial value of the system state, a Sigma point set is generated based on the state quantity at the previous moment, and the one-step prediction value of the state quantity and the one-step prediction value of the state covariance are obtained based on the Sigma point set. In the present invention, the Sigma point set is used for state transfer.

[0028] As a specific embodiment of the present invention, Generate k-1 time Sigma point set χ k-1 , where λ = α 2 (L+κ)-L, L is the dimension of the state quantity, α and κ determine the degree of dispersion of Sigma points; P k-1 is the state covariance at time k-1. In this embodiment, α is usually a small positive value, and κ is usually 0. Sigma point set χ k-1 Includes 2L+1 Sigma points.

[0029] In this embodiment, further χ i,k / k-1 =f(χ i,k-1 ) obtains the one-step prediction value of the state quantity, where is the one-step prediction value of the state quantity, W i m is the weight coefficient of Sigma point, f(·) is the state transfer function, χ i,k-1 is the Sigma point set χ k-1 The i-th Sigma point.

[0030] In this embodiment, further Get the one-step forecast value of the state covariance, where P k / k-1 is the one-step forecast value of the state covariance, β is used to adjust the accuracy of the Sigma point variance. The optimal value of β under Gaussian distribution is 2.

[0031] In the present invention, after obtaining the one-step prediction value of the state quantity and the one-step prediction value of the state covariance, a state prediction Sigma point set is generated based on the one-step prediction value of the state quantity and the one-step prediction of the state covariance, the state prediction Sigma point set is mapped to the measurement domain to obtain the measurement prediction value of the sensor, and the measurement covariance is obtained based on the measurement prediction value of the sensor.

[0032] As a specific embodiment of the present invention, Generate state prediction Sigma point set χ k / k-1 .

[0033] In this embodiment, further Get the sensor's measured predicted value, where and are the measured predicted values ​​of the first sensor and the second sensor respectively, i,k / k-1 Sigma point set χ is the state prediction k / k-1 The i-th Sigma point.

[0034] In this embodiment, further Get the measurement covariance, where P zz,k / k-1 is the measurement covariance.

[0035] In the present invention, after obtaining the measurement covariance, a residual is obtained according to the observed value and the measurement prediction value of the sensor, and the residual is stored in a storage pool.

[0036] As a specific embodiment of the present invention, Get the residual, where ε k The residual at time k is stored in the current processing cycle position in the storage pool. In the present invention, a storage pool with a capacity of N is created, and each position stores the residual at a time. Once full, the storage pool is dynamically managed according to the "first in, first out" principle.

[0037] In the present invention, after the residual amount is stored in a storage pool, it is determined whether the current storage pool is full. If the current storage pool is full, the measurement noise is estimated according to the residual amount using a nonlinear system noise estimator based on residual statistics to obtain the measurement noise covariance, and the measurement covariance is updated according to the measurement noise covariance; if the current storage pool is not full, the measurement covariance is updated according to the initialized measurement noise covariance.

[0038] As a specific embodiment of the present invention, when the storage pool is full, the measurement noise is estimated using a nonlinear system noise estimator based on residual statistics, and the Sage-Husa nonlinear system measurement noise estimator is improved using the residual statistical covariance.

[0039] Specifically, when the storage pool is full, the residual amount set in the storage pool is ξ={ε k-N+1 ,…,ε j ,…,ε k-1 ,ε k}, N is an integer. Get the measurement noise covariance, where is the measurement noise covariance at time k, is the k-1 moment measurement noise covariance, d k is the weighting coefficient at time k, 0<b<1,P εε is the residual covariance, j=[k-N+1,…,k-1,k].

[0040] In order to ensure the positive definiteness of the noise covariance matrix, the noise update adopts the adjustment criterion based on the matching of the covariance autocorrelation. When diag(P εε )>diag(P zz), the noise covariance is updated using the estimator; otherwise,

[0041] In this embodiment, after obtaining the measurement noise covariance, further analysis can be performed based on Update the measurement covariance, where is the updated measurement covariance.

[0042] As another specific embodiment of the present invention, if the storage pool is not full, Update the measurement covariance, where is the initial measurement noise covariance.

[0043] In the present invention, after updating the measurement covariance, the filter gain is obtained according to the updated measurement covariance, the state quantity and state covariance are updated according to the filter gain, the target position tracked at the current moment is output and enters the next moment, and the Sigma point set is generated according to the state quantity at the previous moment to complete the passive collaborative tracking based on Sage-Husa adaptive filtering.

[0044] As a specific embodiment of the present invention, Get the filter gain, where K k is the filter gain at time k.

[0045] In this embodiment, further Update the state according to Update the state covariance, where is the updated state quantity, P k is the updated state covariance.

[0046] The present invention provides a passive collaborative tracking method based on Sage-Husa adaptive filtering. The method has the following advantages: The Sage-Husa noise estimator is applied to the UKF (Unscented Kalman Filter) filter. The proposed nonlinear system noise estimator based on residual statistics can effectively estimate measurement noise and improve the accuracy of online noise estimation by performing local dynamic statistics on the residuals. Furthermore, the method overcomes the filtering divergence problem caused by the non-positive definiteness of the variance matrix by implementing a covariance autocorrelation matching criterion, thereby ensuring the positive definiteness of the noise variance matrix. This method can enhance the adaptability of the target tracking system to the environment and improve tracking performance.

[0047] The passive collaborative tracking filtering method provided by the present invention can estimate noise characteristics in real time and stably, so that the target tracking system can respond to changes in noise characteristics in a timely manner and adjust filtering parameters to prevent filtering divergence and improve tracking performance.

[0048] In order to have a further understanding of the present invention, the following Figures 1 to 3 The passive collaborative tracking method based on Sage-Husa adaptive filtering of the present invention is described in detail.

[0049] like Figures 1 to 3 As shown, according to a specific embodiment of the present invention, a passive collaborative tracking method based on Sage-Husa adaptive filtering is provided, which includes the following steps.

[0050] Step 1: According to X k =F·X k-1 +W k Construct a recursive model of dual-sensor passive cooperative tracking filtering system, according to Z k =h k (X k )+V k An observation model is constructed to obtain the collaborative positioning target position as the initial value of the system state based on the sensor measurement value.

[0051] Step 2: According to Generate k-1 time Sigma point set χ k-1 ,according to Get the one-step prediction value of the state quantity, according to Get the one-step-ahead forecast of the state covariance.

[0052] Step 3: According to Generate state prediction Sigma point set χ k / k-1 ,according to Z i,k / k-1 =h k (χ i,k / k-1 ) obtain the sensor's measured predicted value, according to Get the measurement covariance.

[0053] Step 4: According to Obtain the residual amount and store the residual amount in the storage pool.

[0054] Step 5: Determine whether the current storage pool is full. If the current storage pool is full, Get the measurement noise covariance according to Update the measurement covariance; if the current storage pool is not full, then Update the measurement covariance.

[0055] Step 6: According to Get the filter gain according to Update the state according to Update state covariance.

[0056] Output the target position at the current moment and enter the next moment, return to generate the Sigma point set based on the state quantity of the previous moment to complete the passive collaborative tracking based on Sage-Husa adaptive filtering.

[0057] To prove the effectiveness of the present invention, the following experiments were conducted. Figure 2 As shown. The two sensors are fixed at (0km, 0km) and (10km, 0km) respectively. The initial position of the target is (5km, 70km). It approaches the sensor at a speed of 200m / s and applies time-varying measurement noise. The experiment uses the relative root mean square error (RMSE) of the tracking result as the evaluation criterion for the system tracking performance. By comparing the tracking performance of the present invention (UKF with Improved Sage-Husa, UKF-ISH) with the results of traditional fixed parameter filtering (UKF) and traditional adaptive filtering (UKF with Sage-Husa, UKF-SH), the effectiveness of the present invention in noise adaptive estimation is analyzed. Figure 3 It can be seen that the present invention has obvious advantages in target tracking stability and tracking accuracy.

[0058] In summary, the present invention provides a passive collaborative tracking method based on Sage-Husa adaptive filtering. This passive collaborative tracking method applies the Sage-Husa adaptive filtering method to the traditional unscented Kalman filter, improves the adaptive noise estimator, estimates the measurement noise based on the residual amount using a nonlinear system noise estimator based on the residual statistics, and updates the measurement covariance based on the storage of the residual amount. The present invention can enhance the adaptability of the target tracking system to the environment and improve the tracking performance. Compared with the prior art, the technical solution of the present invention can solve the technical problem of limited passive collaborative tracking performance in the prior art.

[0059] In addition, it should be noted that the use of terms such as "first" and "second" to limit components is only for the convenience of distinguishing the corresponding components. Unless otherwise stated, the above terms have no special meaning and therefore cannot be understood as limiting the scope of protection of the present invention.

[0060] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be within the scope of protection of the present invention.

Claims

1. A passive collaborative tracking method based on Sage-Husa adaptive filtering, characterized in that: The passive collaborative tracking method based on Sage-Husa adaptive filtering includes: Construct a recursive model and observation model for the dual-sensor passive cooperative tracking filtering system, and obtain the cooperative positioning target position as the initial value of the system state based on the sensor measurement value; Generate a Sigma point set based on the state quantity at the previous moment, and obtain the one-step prediction value of the state quantity and the one-step prediction value of the state covariance based on the Sigma point set; According to the one-step prediction value of the state quantity and the one-step prediction of the state covariance, a state prediction Sigma point set is generated, the state prediction Sigma point set is mapped to the measurement domain to obtain the measurement prediction value of the sensor, and the measurement covariance is obtained according to the measurement prediction value of the sensor; Obtain residuals based on the sensor's observed values ​​and predicted values, and store the residuals in a storage pool; Determine whether the current storage pool is full. If the current storage pool is full, estimate the measurement noise according to the residual amount using a nonlinear system noise estimator based on residual statistics to obtain the measurement noise covariance, and update the measurement covariance according to the measurement noise covariance; if the current storage pool is not full, update the measurement covariance according to the initialized measurement noise covariance; specifically, if the current storage pool is full, Get the measurement noise covariance, where is the measurement noise covariance at time k, is the k-1 moment measurement noise covariance, d k is the weighting coefficient at time k, P εε is the residual covariance, ε k is the residual at time k, Z k is the sensor observation at time k, and are the measured predicted values ​​of the first sensor and the second sensor respectively; the residual amount set in the storage pool is ξ={ε k-N+1 ,…,ε j ,…,ε k-1 ,ε k }, N is an integer, P zz,k / k-1 To measure the covariance; if the current storage pool is full, according to Update the measurement covariance, where is the updated measurement covariance; if the current storage pool is not full, according to Update the measurement covariance, where is the initialization measurement noise covariance; The filter gain is obtained according to the updated measurement covariance, the state quantity and state covariance are updated according to the filter gain, the target position tracked at the current moment is output and the next moment is entered, and the Sigma point set is generated according to the state quantity at the previous moment to complete the passive collaborative tracking based on Sage-Husa adaptive filtering.

2. The passive collaborative tracking method based on Sage-Husa adaptive filtering according to claim 1 is characterized in that: according to Generate k-1 time Sigma point set χ k-1 , where λ = α 2 (L+κ)-L, L is the dimension of the state quantity, α and κ determine the degree of dispersion of Sigma points; P k-1 is the state covariance at time k-1, X k-1 is the state quantity of the system at time k-1.

3. The passive collaborative tracking method based on Sage-Husa adaptive filtering according to claim 2 is characterized in that: according to χ i,k / k-1 =f(χ i,k-1 ) obtains the one-step prediction value of the state quantity, where is the one-step prediction value of the state quantity, W i m is the weight coefficient of Sigma point, f(·) is the state transfer function, χ i,k-1 is the Sigma point set χ k-1 The i-th Sigma point; according to Get the one-step forecast value of the state covariance, where P k / k-1 is the one-step forecast value of the state covariance, β is used to adjust the accuracy of the Sigma point variance, and Q is the system process noise covariance.

4. The passive collaborative tracking method based on Sage-Husa adaptive filtering according to claim 3 is characterized in that: according to Generate state prediction Sigma point set χ k / k-1 .

5. The passive collaborative tracking method based on Sage-Husa adaptive filtering according to claim 3 is characterized in that: according to Z i,k / k-1 =h k (χ i,k / k-1 ) obtains the sensor's measured predicted value, where χ i,k / k-1 Sigma point set χ is the state prediction k / k-1 The i-th Sigma point, h k (·) is the observation function of the sensor at time k.

6. The passive collaborative tracking method based on Sage-Husa adaptive filtering according to claim 5 is characterized in that: according to Get the measurement covariance.

7. The passive collaborative tracking method based on Sage-Husa adaptive filtering according to claim 5 or 6, characterized in that: according to Get the filter gain, where K k is the filter gain at time k; according to Update the state according to Update the state covariance, where is the updated state quantity, P k is the updated state covariance.

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