Distributed adaptive target tracking method with communication protection

Abstract

A distributed adaptive target tracking method with communication protection comprises the following steps: after each observation node executes an information filtering algorithm, calculating an adaptive weight coefficient according to a tracking result, and performing information interaction by using a consistency algorithm with communication protection to complete distributed target tracking. According to the invention, higher cooperative target tracking precision can be obtained when the number of communication times is small, and communication protection of a target tracking result is realized.

Description

Technical Field

[0001] The invention belongs to the technical field of target tracking, and in particular to a distributed adaptive target tracking method with communication protection. Background Art

[0002] Target tracking technology is an important field in today's scientific research. It currently plays an important role and has a wide range of applications in the military, industry, and life, and is also a hot topic for scholars. For example, in the military, when executing the task of intercepting enemy aircraft, the target will be located, navigated, and attacked. If the target's motion state information cannot be effectively obtained, subsequent interception will be out of the question; the opponent usually uses equipment with eavesdropping functions to obtain data such as communication information between our observation nodes, which leads to the failure of our interception mission. Therefore, it is necessary to protect the information in the information exchange process between observation nodes to prevent our effective observation data from being obtained by the enemy.

[0003] Injected noise and cryptography are the two most commonly studied communication protection mechanisms, but they are easily affected by the complex noise of the interference battlefield, and the information processing process is relatively complicated for the performance of airborne computers. Therefore, an algorithm that is easier to implement and more robust is needed. Summary of the invention

[0004] The technical problem solved by the present invention is: to overcome the deficiencies of the prior art and provide a distributed adaptive target tracking method with communication protection, which can realize communication protection of target tracking results without affecting the convergence accuracy of the consistency algorithm in distributed filtering.

[0005] The technical solution of the present invention is:

[0006] A distributed adaptive target tracking method with communication protection, comprising:

[0007] 1) Establish a discrete linear model of distributed target tracking system;

[0008] 2) using the discrete linear model of the distributed target tracking system in step 1), obtaining a measurement vector of the observed target obtained by each observation node i;

[0009] 3) Each observation node i obtains the prediction result of the observation value at time k+1, the prediction result of the information vector and the prediction result of the information matrix;

[0010] 4) For each observation node i, using the measurement vector of the observation target obtained by each observation node i in step 2), and according to the observation value prediction result, information vector prediction result and information matrix prediction result at time k+1 obtained in step 3), calculate the information vector correction result and information matrix correction result at time k+1;

[0011] 5) Determine the fusion result according to the information vector correction result and the information matrix correction result at time k+1 obtained in step 4);

[0012] 6) Repeat step 5) L times to obtain the L-iteration fusion results corresponding to time k+1. According to the L-iteration fusion results and the information vector correction results and information matrix correction results obtained in step 4) at time k+1, determine the tracking result corresponding to observation node i at time k+1.

[0013] Preferably, the discrete linear model of the distributed target tracking system in step 1) is specifically:

[0014] x k =A k-1 x k-1 +ω k-1

[0015]

[0016] Wherein, i=1,2,...,N; N is a positive integer;

[0017] Corresponding to the kth sampling moment, x k is the observed target state vector, is the measurement vector of observation node i, ω k is the process noise of the observed target, is the measurement noise of observation node i, is the observation matrix of observation node i;

[0018] A k-1 is the state transfer matrix at the k-1th sampling moment.

[0019] Preferably, the method for obtaining the prediction result of the observation value at time k+1 in step 3) is specifically as follows:

[0020]

[0021] in, is the correction step information at the kth moment, The state value of the observation target at the k+1 moment prediction step; is the prediction result of the observation value of the correction step at time k+1.

[0022] Preferably, the method for obtaining the prediction result of the information vector at time k+1 in step 3) is specifically as follows:

[0023]

[0024] in, is the information matrix of the observed target at the k+1 prediction step and serves as the information vector prediction result at the k+1 moment; Ω k is the information matrix of the correction step of the observed target at the kth moment.

[0025] Preferably, the method for obtaining the prediction result of the information matrix at time k+1 in step 3) is specifically as follows:

[0026]

[0027] in, The information vector of the prediction step of the observed target at time k+1 is used as the prediction result of the information matrix at time k+1.

[0028] Preferably, the method for calculating the information vector correction result at time k+1 in step 4) is specifically as follows:

[0029]

[0030] in, is the information vector of the observation target at the k+1 time point correction step, and is used as the information vector correction result at the k+1 time point;

[0031] i k+1 is the information vector contribution of the observation node i at time k+1; is the inverse matrix of the measurement noise covariance matrix at the kth sampling moment.

[0032] Preferably, the method for calculating the correction result of the information matrix at time k+1 in step 4) is specifically as follows:

[0033]

[0034] in, is the information matrix of the correction step of the observed target at the k+1 moment, and is used as the correction result of the information matrix at the k+1 moment; I k+1 Contribution to the information matrix of observation node i at time k+1.

[0035] Preferably, the method for determining the fusion result in step 5) is specifically:

[0036] Initialize the initial value of the consistency algorithm for communication protection:

[0037]

[0038]

[0039] Where rand(1) is any random number in the range of (0, 1);

[0040] The information vector and information matrix are fused according to the consistency algorithm:

[0041]

[0042] in,

[0043] represents the α sub-state of the information vector of the observation node i at the time k+1 and the lth iteration;

[0044] represents the β sub-state of the information vector of the observation node i at the time k+1 and the lth iteration;

[0045] represents the α sub-state of the information matrix of the observation node i at the time k+1 and the lth iteration;

[0046] represents the β sub-state of the information matrix of the observation node i at the time k+1 and the lth iteration;

[0047] l=0,1,...,L-1; L is the total number of iterations at each moment, L iterations at each moment, and the value range of L is 20 to 50 times;

[0048] ω ij (l) is the value of the i-th row and j-th column in the consistency weight matrix of the distributed target tracking system at the l-th iteration; the consistency weight matrix of the distributed target tracking system is an N-row and N-column matrix, j = 1, 2, ..., N; that is, the element ω in the consistency weight matrix of the distributed target tracking system ij (l) represents the consistency correlation weight between observation node i and observation node j at the lth iteration;

[0049] a i,αβ is the correlation coefficient of the sub-state of the observed node i, a i,αβ ∈(0,1];

[0050] Update the consistency weight matrix coefficient e i (l):

[0051]

[0052] Preferably, the step 6) determines the tracking result corresponding to the observation node i at time k+1 The method is as follows:

[0053]

[0054] The advantages of the present invention compared with the prior art are:

[0055] The present invention adopts a consistency algorithm based on state decomposition, which can realize communication protection of target tracking results without affecting the tracking accuracy, ensuring that the target information is not eavesdropped. In addition, the present invention designs an adaptive consistency weight, and the consistency weight calculated by the fastest gradient descent method can effectively improve the convergence speed of information interaction in the collaborative target tracking method, and obtain higher collaborative target tracking accuracy when the number of communications is small. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 It is a simulation diagram of the communication protection effect;

[0057] Figure 2 is a tracking error curve of target tracking of the present invention;

[0058] Figure 3 Tracking the error covariance curve of each sensor node for the purpose of the present invention;

[0059] Figure 4 The figure is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0060] The present invention discloses a distributed adaptive target tracking method with communication protection. By exchanging only some sub-states in the real state between different observation nodes, the target tracking accuracy can be ensured while protecting the communication between sensors. Simulation results verify that the method improves the consistency iteration convergence speed without losing the tracking accuracy, and can obtain higher collaborative target tracking accuracy when the number of communications is small.

[0061] In order to better describe the present invention, the present invention is described in detail below with the help of schematic diagrams and examples. Figure 4 As shown, a distributed adaptive target tracking method with communication protection of the present invention comprises the following steps:

[0062] Step 1: Establish a discrete linear model of the distributed target tracking system (target motion model and sensor observation model). The distributed target tracking system includes N observation nodes, each of which is equipped with a sensor, such as microwave radar or lidar.

[0063] Consider the following discrete linear model of a distributed target tracking system consisting of N observation nodes:

[0064] x k =A k-1 x k-1 +ω k-1

[0065]

[0066] Wherein, i=1,2,...,N; N is a positive integer;

[0067] Corresponding to the kth sampling moment, x k is the observed target state vector, is the measurement vector of observation node i (i.e., the output information of the sensor), ω k is the process noise of the observed target, is the measurement noise of observation node i. Here, both process noise and measurement noise are considered as Gaussian white noise. The corresponding covariance matrices are Q k and R k , i.e. Q k is the covariance matrix of process noise, R k is the covariance matrix of the measurement noise; is the observation matrix of observation node i.

[0068] A k-1 is the state transfer matrix at the k-1th sampling moment;

[0069] Defining the Matrix

[0070] W k is the kth sampling moment, the process noise covariance matrix Q k The inverse matrix of

[0071] is the kth sampling moment, the measurement noise covariance matrix R k The inverse matrix of .

[0072] Step 2: The measurement vector of the observed target obtained by each observation node i Take samples and store them;

[0073] Step 3: For each observation node i, execute the prediction step of the distributed filtering algorithm to obtain the prediction results of the observation value at time k+1, the information vector prediction results, and the information matrix prediction results respectively;

[0074] Status prediction:

[0075]

[0076] Observed value prediction:

[0077]

[0078] Information vector prediction:

[0079]

[0080] Information Matrix Prediction:

[0081]

[0082] in, is the correction step information at the kth moment, The state value of the observation target at the k+1 moment prediction step;

[0083] is the prediction result of the observation value of the correction step at time k+1;

[0084] Ω k =W k is the information matrix of the correction step of the observed target at the kth moment, is the information matrix of the prediction step of the observed target at time k+1 and is used as the information vector prediction result at time k+1;

[0085] The information vector of the prediction step of the observed target at time k+1 is used as the prediction result of the information matrix at time k+1.

[0086] Step 4: For each observation node i, execute the correction step of the distributed filtering algorithm, using the measurement vector of the observation target obtained by each observation node i obtained in step 2, and according to the observation value prediction result, information vector prediction result and information matrix prediction result at time k+1 obtained in step 3, calculate the information vector correction result and information matrix correction result at time k+1;

[0087] 41) Information contribution vector:

[0088]

[0089] 42) Information vector correction:

[0090]

[0091] 43) Information contribution matrix:

[0092]

[0093] 44) Information matrix correction:

[0094]

[0095] Among them, i k+1 I is the information vector contribution of the observation node i at time k+1; k+1 Contribution to the information matrix of observation node i at time k+1;

[0096] is the information vector of the observation target at the k+1 time point correction step, and is used as the information vector correction result at the k+1 time point;

[0097] is the information matrix of the correction step of the observed target at the k+1 moment, and is used as the correction result of the information matrix at the k+1 moment.

[0098] Step 5: Determine the fusion result according to the k+1 time information vector correction result and the information matrix correction result obtained in step 4; the fusion result of step 5 includes:

[0099] Observe node i at time k+1, l The α substate of the information vector at the iteration

[0100] Observe node i at time k+1, l The β substate of the information vector at the iteration

[0101] Observe node i at time k+1, l The α substate of the information matrix at the iteration

[0102] Observe node i at time k+1, l The β substate of the information matrix at the iteration

[0103] The method for determining the fusion result in step 5 is specifically as follows:

[0104] 51) Initialize the initial value of the consistency algorithm for communication protection:

[0105]

[0106] Here, rand(1) is any random number in the range of (0, 1).

[0107] 52) The information vector and information matrix are fused according to the consistency algorithm:

[0108]

[0109]

[0110] in,

[0111] represents the α sub-state of the information vector of the observation node i at the time k+1 and the lth iteration;

[0112] represents the β sub-state of the information vector of the observation node i at the time k+1 and the lth iteration;

[0113] represents the α sub-state of the information matrix of the observation node i at the time k+1 and the lth iteration;

[0114] represents the β sub-state of the information matrix of the observation node i at the time k+1 and the lth iteration;

[0115] Wherein, l=0,1,...,L-1; L is the total number of iterations at each moment, each moment is iterated L times, and the value range of L is 20 to 50 times.

[0116] ω ij (l) is the value of the i-th row and j-th column in the consistency weight matrix of the distributed target tracking system at the l-th iteration; the consistency weight matrix of the distributed target tracking system is an N-row and N-column matrix, j = 1, 2, ..., N; that is, the element ω in the consistency weight matrix of the distributed target tracking system ij (l) represents the consistency correlation weight between observation node i and observation node j at the lth iteration;

[0117] a i,αβ is the correlation coefficient of the sub-state of the observed node i, a i,αβ ∈(0,1]; a i,αβ is a constant related to the observation node and has nothing to do with the number of iterations and the sampling time.

[0118] 53) After each consistency iteration, update the consistency weight matrix coefficient e i (l):

[0119]

[0120] Step 6: Repeat step 5 L times to obtain the L-iteration fusion results corresponding to time k+1. According to the L-iteration fusion results and the information vector correction results and information matrix correction results obtained in step 4 at time k+1, determine the tracking result corresponding to observation node i at time k+1.

[0121] 61) Consensus algorithm iteration results:

[0122]

[0123] 62) Obtain the distributed target tracking result at time k+1:

[0124]

[0125] Wherein, L is the total number of iterations at each moment; each moment is iterated L times, and the value range of L is 20 to 50 times.

[0126] Example

[0127] The present invention proposes a distributed adaptive target tracking filtering algorithm with communication protection, which can protect the communication between sensors of each observation node while ensuring the target tracking accuracy, thereby completing the target tracking task.

[0128] In order to prove the effectiveness of the present invention, the proposed distributed adaptive target tracking filtering algorithm with communication protection is simulated and verified. The simulation results of the communication protection effect are shown in the figure. Figure 1 As shown in the figure, the observation states of all observation nodes converge to the expected average state, the observation node states tend to be consistent within a certain number of iterations, and the sub-states of information interaction during the algorithm are always far away from the actual observation states of each observation node, protecting the communication security of the observation information in the consistency algorithm. Figure 1 The green curve in is the α substate of the information matrix in step 5 above.

[0129] For the verification of target tracking accuracy, the estimated results are as follows Figure 2 As shown, the tracking error comparison results of the observation quantity of the observation node, the volume information filtering of the local single node and the distributed consistent volume information filtering with communication protection are shown. Figure 2 The DCIF curve in the figure is the tracking result output in step 6 above.

[0130] Figure 3 The RMSE comparison chart of different filtering methods is shown in Figure 2. From the simulation results, it can be seen that the adaptive distributed target tracking filtering method with communication protection has higher tracking accuracy and convergence speed, which illustrates the feasibility and effectiveness of this method. Figure 3 The black curve in the figure is the tracking result output from step 6 above.

[0131] Although the present invention has been disclosed as above with preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make possible changes and modifications to the technical solution of the present invention by using the above disclosed methods and technical contents without departing from the spirit and scope of the present invention. Therefore, any simple modification, equivalent change and modification made to the above embodiments according to the technical essence of the present invention without departing from the content of the technical solution of the present invention shall belong to the protection scope of the technical solution of the present invention. In the absence of conflict, the embodiments of the present application and the technical features in the embodiments can be combined with each other.

[0132] The contents not described in detail in the specification of the present invention belong to the common knowledge of those skilled in the art.

Claims

1. A distributed adaptive target tracking method with communication protection, characterized in that: include: 1) Establish a discrete linear model of the distributed target tracking system; 2) using the discrete linear model of the distributed target tracking system in step 1), obtaining a measurement vector of the observed target obtained by each observation node i; 3) Each observation node i obtains the prediction result of the observation value at time k+1, the prediction result of the information vector and the prediction result of the information matrix; 4) For each observation node i, using the measurement vector of the observation target obtained by each observation node i in step 2), and according to the observation value prediction result, information vector prediction result and information matrix prediction result at time k+1 obtained in step 3), calculate the information vector correction result and information matrix correction result at time k+1; 5) Determine the fusion result according to the information vector correction result and the information matrix correction result at time k+1 obtained in step 4); 6) Repeat step 5) L times to obtain the L-iteration fusion results corresponding to time k+1. According to the L-iteration fusion results and the information vector correction results and information matrix correction results obtained in step 4) at time k+1, determine the tracking result corresponding to observation node i at time k+1.

2. A distributed adaptive target tracking method with communication protection according to claim 1, characterized in that: The discrete linear model of the distributed target tracking system in step 1) is specifically: x k =A k-1 x k-1 +oh k-1 Wherein, i=1,2,...,N; N is a positive integer; Corresponding to the kth sampling moment, x k is the observed target state vector, is the measurement vector of observation node i, ω k is the process noise of the observed target, is the measurement noise of observation node i, is the observation matrix of observation node i; A k-1 is the state transfer matrix at the k-1th sampling moment.

3. A distributed adaptive target tracking method with communication protection according to claim 2, characterized in that: The method for obtaining the prediction result of the observation value at time k+1 in step 3) is specifically as follows: in, is the correction step information at the kth moment, The state value of the observation target at the k+1 moment prediction step; is the prediction result of the observation value of the correction step at time k+1.

4. A distributed adaptive target tracking method with communication protection according to claim 3, characterized in that: The method for obtaining the prediction result of the information vector at time k+1 in step 3) is specifically as follows: in, is the information matrix of the observed target at the k+1 prediction step and serves as the information vector prediction result at the k+1 moment; Ω k is the information matrix of the correction step of the observed target at the kth moment.

5. A distributed adaptive target tracking method with communication protection according to claim 3 or 4, characterized in that: The method for obtaining the prediction result of the information matrix at time k+1 in step 3) is specifically as follows: in, The information vector of the prediction step of the observed target at time k+1 is used as the prediction result of the information matrix at time k+1.

6. A distributed adaptive target tracking method with communication protection according to claim 5, characterized in that: The method of calculating the information vector correction result at time k+1 in step 4) is specifically as follows: in, is the information vector of the observation target at the k+1 time point correction step, and is used as the information vector correction result at the k+1 time point; i k+1 is the information vector contribution of the observation node i at time k+1; is the inverse matrix of the measurement noise covariance matrix at the kth sampling moment.

7. A distributed adaptive target tracking method with communication protection according to claim 6, characterized in that: The method of calculating the correction result of the information matrix at time k+1 in step 4) is specifically as follows: in, is the information matrix of the correction step of the observed target at the k+1 moment, and is used as the correction result of the information matrix at the k+1 moment; I k+1 Contribution to the information matrix of observation node i at time k+1.

8. A distributed adaptive target tracking method with communication protection according to claim 7, characterized in that: The method of determining the fusion result in step 5) is specifically as follows: Initialize the initial value of the consistency algorithm for communication protection: Where rand(1) is any random number in the range of (0, 1); The information vector and information matrix are fused according to the consistency algorithm: in, Indicates that the observation node i is at time k+1, l The α substate of the information vector at the iteration; Indicates that the observation node i is at time k+1, l The β substate of the information vector at the iteration; Indicates that the observation node i is at time k+1, l The α sub-state of the information matrix at the iteration; Indicates that the observation node i is at time k+1, l The β substate of the information matrix at the iteration; l=0,1,...,L-1; L is the total number of iterations at each moment, L iterations at each moment, and the value range of L is 20 to 50 times; ω ij (l) is the value of the i-th row and j-th column in the consistency weight matrix of the distributed target tracking system at the l-th iteration; the consistency weight matrix of the distributed target tracking system is an N-row and N-column matrix, j = 1, 2, ..., N; that is, the element ω in the consistency weight matrix of the distributed target tracking system ij (l) represents the consistency correlation weight between observation node i and observation node j at the lth iteration; a i,αβ is the correlation coefficient of the sub-state of the observed node i, a i,αβ ∈(0,1]; Update the consistency weight matrix coefficient e i (l):

9. A distributed adaptive target tracking method with communication protection according to claim 8, characterized in that: Step 6) Determine the tracking result corresponding to the observation node i at time k+1 The method is as follows:

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