A weighted fusion distributed target tracking method based on a sensor network

Abstract

This invention discloses a weighted fusion distributed target tracking method based on sensor networks, comprising the following steps: establishing a cooperative network including multiple aircraft connected by communication links; setting a target motion model and an observation model, and establishing a fusion measurement model; each aircraft fusing information received during communication based on the fusion measurement model, and combining this with its own measured target state variables to obtain calibrated target state variables; each aircraft navigating and tracking the target based on the obtained calibrated target state variables. The weighted fusion distributed target tracking method based on sensor networks disclosed in this invention provides more accurate target state variable estimation with smaller estimation errors.

Description

Technical Field

[0001] This invention relates to a weighted fusion distributed target tracking method based on sensor networks, belonging to the field of aircraft control. Background Technology

[0002] In the process of multi-aircraft collaboration, the multi-aircraft collaborative tracking method can process data from multiple sensors in a multi-level, multi-faceted, and multi-layered manner, thereby significantly improving the filtering accuracy and stability.

[0003] Existing collaborative target tracking methods mainly have two structures: distributed and centralized. The centralized fusion method has the best estimation performance, but it is less robust and has higher requirements for communication network resources.

[0004] Distributed fusion methods can save computational resources and communication bandwidth, are more stable, and can achieve optimal target estimation under certain conditions. However, existing distributed target tracking methods rarely consider the balance of tracking result fusion and distribution, which may lead to a decrease in tracking accuracy for some targets and an increase in system energy consumption.

[0005] Therefore, it is necessary to conduct in-depth research on existing distributed cooperative target tracking methods to solve the above problems. Summary of the Invention

[0006] To overcome the above problems, the inventors conducted in-depth research and proposed a weighted fusion distributed target tracking method based on sensor networks, comprising the following steps:

[0007] S1. Establish a collaborative network, which includes multiple aircraft and communication connections between the multiple aircraft;

[0008] S2. Set up the target motion model and observation model, and establish a fusion measurement model;

[0009] S3. The aircraft uses a fusion measurement model to fuse the information received during the communication process and combine it with the target state quantities measured by itself to obtain the calibration target state quantities.

[0010] S4. Each aircraft navigates and tracks the target based on the obtained calibration target state variables.

[0011] In a preferred embodiment, in S1, the cooperative network includes multiple node aircraft and multiple observation aircraft, wherein the observation aircraft is communicatively connected to at least one node aircraft, and the multiple node aircraft are communicatively connected to each other.

[0012] In a preferred embodiment, in S1, the node aircraft and the observation aircraft respectively use different types of sensors to observe the target.

[0013] In a preferred embodiment, the target motion model is represented as:

[0014] X i,k+1 =FX i,k +GW k

[0015] In this context, the subscript i represents different aircraft, and the subscript k represents time k. Let x, y, z represent the target state variables of aircraft i at time k, where x, y, z represent the position of the target relative to aircraft i in a three-dimensional Cartesian coordinate system. This represents the velocity of the target relative to aircraft i in a three-dimensional Cartesian coordinate system. W represents the acceleration of the target relative to the aircraft i in a three-dimensional Cartesian coordinate system. k It is a Gaussian white noise vector with a mean of 0, used to simulate the random changes in the target's acceleration; F represents the state transition matrix, and G represents the state input matrix;

[0016] The observation model is expressed as follows:

[0017] Z i,k =H i,k X i,k +V i,k

[0018] Among them, Z i,k H represents the measurement vector of aircraft i at time k. i,k V represents the observation matrix of spacecraft i at time k, and is the identity matrix. i,k Let represent the random noise vector of aircraft i at time k;

[0019] The fusion measurement model is expressed as follows:

[0020] Z c k =H c k X c k +V c k

[0021] Among them, Z c k =[Z 1,k ;…;Z L,k ] T

[0022] H c k =[h 1,k ;…;h L,k ] T

[0023] V c k =[V 1,k ;…;V L,k ] T

[0024] X c k =[X 1,k ;…;X L,k ] T

[0025] Among them, Z c k H represents the set of measurement vectors of different aircraft at time k. c k V represents the set of observation matrices of different aircraft at time k. c k Let X represent the set of random noise vectors from different aircraft at time k. c k This represents the set of target state variables for different aircraft at time k.

[0026] In a preferred embodiment, in S3, the node aircraft receives observations transmitted by other observation aircraft, fuses them with its own detected observations and target predictions transmitted by other node aircraft to obtain the fused observations of the node aircraft, and transmits the fused observations to the observation aircraft and other node aircraft with which it is communicatively connected.

[0027] In a preferred embodiment, in step S3, the fusion includes the following sub-steps:

[0028] S31. For any node aircraft, set the target state variables, the measurement vector, and the covariance matrix in the initial state.

[0029] S32. For any node aircraft, based on the fusion measurement model, and using the measurement information of the node aircraft at the next moment and the prior value of the target state variable at the previous moment, predict the posterior value of the target state variable and the posterior value of the covariance matrix at the next moment.

[0030] S33. Each node aircraft will transmit the posterior values ​​of the target state variables and the covariance matrix obtained at the next moment to other node aircraft.

[0031] S34. Fuse the posterior values ​​of the target state variables and the posterior values ​​of the covariance matrix obtained by all node aircraft at the next time step to obtain the fused posterior values ​​of the target state variables and the fused covariance matrix at the next time step.

[0032] S35. Any node aircraft obtains the prior value of the covariance matrix at the next time step based on the posterior value of the fused covariance matrix at the next time step, combined with its own predicted posterior value of the covariance matrix at the next time step, and transmits the prior value of the covariance matrix at the next time step to other aircraft.

[0033] S36. Repeat S32 to S35 to predict subsequent time steps and obtain the posterior value of the target state quantity of the spacecraft at any node time step. Use this value as the calibration target state quantity of the spacecraft at that node time step.

[0034] In a preferred embodiment, in S31, the target state quantity in the initial state is taken as the prior value of the target state quantity in the previous moment, and is expressed as:

[0035] Using the measurement vector in the initial state as the prior value of the measurement vector in the next moment, it can be expressed as:

[0036] Using the covariance matrix in the initial state as the prior value of the covariance matrix in the previous time step, it can be expressed as:

[0037] in, This represents the target state quantity of aircraft i in its initial state. This represents the prior value of the target state variable of aircraft i at time k-1. This represents the measurement vector of aircraft i in its initial state. Let represent the prior value of the measurement vector of aircraft i at time k. Let i represent the covariance matrix of aircraft i in its initial state. Let represent the prior value of the covariance matrix of aircraft i at time k-1.

[0038] In a preferred embodiment, in S32, the prediction process is represented as follows:

[0039]

[0040]

[0041]

[0042]

[0043]

[0044] in, Let Q represent the prior value of the target state variable of aircraft i at time k, and let Q represent the noise covariance matrix. Let P represent the prior value of the covariance matrix of aircraft i at time k-1. i,kLet i represent the covariance matrix of aircraft i at time k. This represents the variance of the observation noise of aircraft i at time k. Z represents the Kalman gain of aircraft i at time k. i,k Let represent the measurement vector of aircraft i at time k. Let I represent the posterior value of the target state variable of aircraft i at time k, and let I denote the unit vector. Let represent the posterior value of the covariance matrix of aircraft i at time k.

[0045] In a preferred embodiment, in S34, the fusion process is represented as follows:

[0046]

[0047]

[0048] Where l represents the total number of aircraft. Represents the posterior value of the fused covariance matrix. This represents the posterior value of the fused target state variable.

[0049] In a preferred embodiment, in S35, the prior value of the obtained covariance matrix is ​​expressed as:

[0050]

[0051]

[0052] in, Let β represent the prior value of the covariance matrix of aircraft i at time k. i Let i be the scaling factor corresponding to aircraft i, and trace() be the trace function of the matrix.

[0053] The beneficial effects of this invention include:

[0054] (1) It has strong robustness while achieving accurate estimation, which is beneficial for engineering applications;

[0055] (2) The estimation of target state variables is more accurate and the estimation error is smaller;

[0056] (3) The estimated convergence speed is faster and the tracking delay is lower. Attached Figure Description

[0057] Figure 1 A schematic flowchart of a weighted fusion distributed target tracking method based on a sensor network according to a preferred embodiment of the present invention is shown.

[0058] Figure 2 This diagram illustrates the aircraft communication connection in Embodiment 1.

[0059] Figure 3 The target tracking trajectory result diagram in Example 1 is shown;

[0060] Figure 4 The figure shows a comparison of the position estimation error results in Example 1 and Comparative Examples 1 and 2;

[0061] Figure 5 The graph shows a comparison of the velocity estimation error results in Example 1 and Comparative Examples 1 and 2.

[0062] Figure 6 The graph shows a comparison of the acceleration estimation error results in Example 1 and Comparative Examples 1 and 2. Detailed Implementation

[0063] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Through these descriptions, the features and advantages of the present invention will become clearer and more apparent.

[0064] The term “exemplary” as used herein means “serving as an example, embodiment, or illustration.” Any embodiment illustrated herein as “exemplary” is not necessarily to be construed as superior to or better than other embodiments. Although various aspects of embodiments are shown in the accompanying drawings, the drawings are not necessarily drawn to scale unless specifically indicated otherwise.

[0065] According to the present invention, a weighted fusion distributed target tracking method based on sensor networks is provided, such as... Figure 1 As shown, it includes the following steps:

[0066] S1. Establish a collaborative network, which includes multiple aircraft and communication connections between the multiple aircraft;

[0067] S2. Set up the target motion model and observation model, and establish a fusion measurement model;

[0068] S3. The aircraft uses a fusion measurement model to fuse the information received during the communication process and combine it with the target state quantities measured by itself to obtain the calibration target state quantities.

[0069] S4. Each aircraft navigates and tracks the target based on the obtained calibration target state variables.

[0070] In S1, preferably, the cooperative network includes multiple node aircraft and multiple observation aircraft, wherein the observation aircraft is communicatively connected to at least one node aircraft, and the multiple node aircraft are communicatively connected to each other.

[0071] In S1, the node aircraft and the observation aircraft use different types of sensors to measure the target and obtain the target state variables.

[0072] Using different types of sensors to measure targets can improve adaptability to the environment and measurement accuracy. This results in smaller fluctuations in the error of the obtained calibration target state quantities and more accurate measurements.

[0073] For example, the observation aircraft is equipped with an infrared seeker, whose measurement vectors for the target are pitch angle and offset angle;

[0074] The node aircraft is equipped with a radar seeker, which measures the relative distance, pitch angle, and offset angle of the target.

[0075] In a preferred embodiment, one node aircraft is randomly selected to fuse the information received during the communication process, and the fusion result is transmitted to other node aircraft. This allows the other node aircraft to combine the target state variables they have measured to obtain the calibration target state variables. This method further reduces the computational load on the node aircraft and lowers tracking latency.

[0076] According to the present invention, in S2, the target motion model is represented as:

[0077] X i,k+1 =FX i,k +GW k

[0078] In this context, the subscript i represents different aircraft, and the subscript k represents time k. Let x, y, z represent the target state variables of aircraft i at time k, where x, y, z represent the position of the target relative to aircraft i in a three-dimensional Cartesian coordinate system. This represents the velocity of the target relative to aircraft i in a three-dimensional Cartesian coordinate system. W represents the acceleration of the target relative to the aircraft i in a three-dimensional Cartesian coordinate system. k It is a Gaussian white noise vector with a mean of 0, used to simulate the random changes in the target acceleration; F represents the state transition matrix, and G represents the state input matrix.

[0079] The observation model is expressed as follows:

[0080] Z i,k =H i,k X i,k +V i,k

[0081] Among them, Z i,k H represents the measurement vector of aircraft i at time k. i,k V represents the observation matrix of spacecraft i at time k, and is the identity matrix. i,k Let represent the random noise vector of aircraft i at time k.

[0082] In a preferred embodiment, the fusion measurement model is represented as follows:

[0083] Z c k =H c k X c k +V c k

[0084] Among them, Z c k =[Z 1,k ;…;Z L,k ] T

[0085] H c k =[h 1,k ;…;h L,k ] T

[0086] V c k =[V 1,k ;…;V L,k ] T

[0087] X c k =[X 1,k ;…;X L,k ] T

[0088] Among them, Z c k H represents the set of measurement vectors of different aircraft at time k. c k V represents the set of observation matrices of different aircraft at time k. c k Let X represent the set of random noise vectors from different aircraft at time k. c k This represents the set of target state variables for different aircraft at time k.

[0089] According to the present invention, in S3, the node aircraft receives the target state quantity transmitted by other observation aircraft, fuses it with the target state quantity observed by itself and the target prediction quantity transmitted by other node aircraft to obtain the fused quantity of the node aircraft, and transmits the fused quantity to the observation aircraft and other node aircraft connected to it in communication.

[0090] More preferably, in S3, the fusion includes the following sub-steps:

[0091] S31. For any aircraft, set the target state variables, the measurement vector, and the covariance matrix in the initial state.

[0092] S32. For any aircraft, based on the measurement information of the aircraft at the next moment and the prior value of the target state variable at the previous moment, predict the posterior value of the target state variable and the posterior value of the covariance matrix at the next moment.

[0093] S33. Each observation vehicle will transmit the posterior value of the target state quantity and the posterior value of the covariance matrix obtained at the next moment to the node vehicle it communicates with. Each node vehicle will transmit the received information and its own predicted posterior value of the target state quantity and the posterior value of the covariance matrix at the next moment to other node vehicles.

[0094] S34. The node aircraft fuses the posterior values ​​of the target state variables and the posterior values ​​of the covariance matrix obtained by all aircraft at the next time step to obtain the fused quantity at the next time step, and transmits the fused quantity to all aircraft. The fused quantity includes the fused posterior values ​​of the target state variables and the fused posterior values ​​of the covariance matrix.

[0095] S35. Each aircraft obtains the prior value of the next time covariance matrix based on the posterior value of the fused covariance matrix at the next time moment, combined with its own predicted posterior value of the next time covariance matrix, and transmits the prior value of the next time covariance matrix to the node aircraft communicating with the aircraft.

[0096] S36. Repeat S32 to S35 to predict subsequent time steps and obtain the posterior value of the target state quantity for each aircraft at subsequent time steps. Use this value as the calibration target state quantity for the aircraft at subsequent time steps.

[0097] In S31, the specific settings of the prior value of the target state quantity and the measurement vector in the initial state can be freely set by those skilled in the art based on experience. Preferably, the target state quantity and the measurement vector in the initial state are set such that the covariance matrix at the initial time satisfies the following: the diagonal elements of the covariance matrix at the initial time are greater than the difference between the true value of the target state and the prior value of the target state.

[0098] In S31, the target state variable in the initial state is taken as the prior value of the target state variable in the previous time step, and is expressed as:

[0099] Using the measurement vector in the initial state as the prior value of the measurement vector in the next moment, it can be expressed as:

[0100] Using the covariance matrix in the initial state as the prior value of the covariance matrix in the previous time step, it can be expressed as:

[0101] in, This represents the target state quantity of node i in its initial state. This represents the prior value of the target state variable of aircraft i at time k-1. This represents the measurement vector of aircraft i in its initial state. Let represent the prior value of the measurement vector of aircraft i at time k. Let i represent the covariance matrix of aircraft i in its initial state. Let represent the prior value of the covariance matrix of aircraft i at time k-1.

[0102] In S32, the prediction process is represented as follows:

[0103]

[0104]

[0105]

[0106]

[0107]

[0108] in, Let Q represent the prior value of the target state variable of aircraft i at time k, and let Q represent the noise covariance matrix. Let P represent the prior value of the covariance matrix of aircraft i at time k-1. i,k Let i represent the covariance matrix of aircraft i at time k. This represents the variance of the observation noise of aircraft i at time k. Z represents the Kalman gain of aircraft i at time k. i,k Let represent the measurement vector of aircraft i at time k. Let I represent the posterior value of the target state variable of aircraft i at time k, and let I denote the unit vector. Let represent the posterior value of the covariance matrix of aircraft i at time k.

[0109] In S34, preferably, one node aircraft is selected for fusion.

[0110] In S34, the fusion process is represented as:

[0111]

[0112]

[0113] Where l represents the total number of aircraft. Represents the posterior value of the fused covariance matrix. This represents the posterior value of the fused target state variable.

[0114] In S35, the prior values ​​of the covariance matrix obtained are expressed as follows:

[0115]

[0116]

[0117] in, Let β represent the prior value of the covariance matrix of aircraft i at time k. i Let i be the scaling factor corresponding to aircraft i, and trace() be the trace function of the matrix.

[0118] Example

[0119] Example 1

[0120] A simulation experiment was conducted, using 12 aircraft to track the target. The initial launch parameters of the aircraft are shown in Table 1.

[0121] Table 1

[0122]

[0123] The time step is set to T = 0.01s, the loitering munition velocity is 300m / s, the initial trajectory inclination is 15°, the initial trajectory deviation is 0°, the maximum overload is 20g, the initial target position is (8000m, 8000m, 8000m), the target velocity is 100m / s, and the acceleration is 2m / s². 2 In the slow turning motion, among the 12 aircraft, 3 are used as node aircraft, namely aircraft numbered 1, 5 and 9. During the simulation, they are set to have radar seekers with a radar scanning period of 0.01s. The remaining 9 aircraft are used as observation aircraft and are set to have infrared seekers.

[0124] The simulation process includes the following steps:

[0125] S1. Establish a collaborative network, which includes multiple aircraft and communication connections between the multiple aircraft;

[0126] S2. Set up the target motion model and observation model, and establish a fusion measurement model;

[0127] S3. The aircraft uses a fusion measurement model to fuse the information received during the communication process and combine it with the target state quantities measured by itself to obtain the calibration target state quantities.

[0128] S4. Each aircraft navigates and tracks the target based on the obtained calibration target state variables.

[0129] The target motion model is represented as follows:

[0130] X i,k+1 =FX i,k +GW k

[0131] The observation model is expressed as follows:

[0132] Z i,k =H i,k X i,k +V i,k

[0133] The fusion measurement model is expressed as follows:

[0134] Z c k =H c k X c k +V c k

[0135] Among them, Z c k =[Z 1,k ;…;Z L,k ] T

[0136] H c k =[h 1,k ;…;h L,k ] T

[0137] V c k =[V 1,k ;…;V L,k ] T

[0138] X c k =[X 1,k ;…;X L,k ] T

[0139] In S3, the fusion includes the following sub-steps:

[0140] S31. For any node aircraft, set the target state variables, the measurement vector, and the covariance matrix in the initial state.

[0141] S32. For any node aircraft, based on the fusion measurement model, and using the measurement information of the node aircraft at the next moment and the prior value of the target state variable at the previous moment, predict the posterior value of the target state variable and the posterior value of the covariance matrix at the next moment.

[0142] S33. Each node aircraft will transmit the posterior values ​​of the target state variables and the covariance matrix obtained at the next moment to other node aircraft.

[0143] S34. Fuse the posterior values ​​of the target state variables and the posterior values ​​of the covariance matrix obtained by all node aircraft at the next time step to obtain the fused posterior values ​​of the target state variables and the fused covariance matrix at the next time step.

[0144] S35. Any node aircraft obtains the prior value of the covariance matrix at the next time step based on the posterior value of the fused covariance matrix at the next time step, combined with its own predicted posterior value of the covariance matrix at the next time step, and transmits the prior value of the covariance matrix at the next time step to other aircraft.

[0145] S36. Repeat S32 to S35 to predict subsequent time steps and obtain the posterior value of the target state quantity of the spacecraft at any node time step. Use this value as the calibration target state quantity of the spacecraft at that node time step.

[0146] In S31, the target state variable in the initial state is taken as the prior value of the target state variable in the previous time step, and is expressed as:

[0147] Using the measurement vector in the initial state as the prior value of the measurement vector in the next moment, it can be expressed as:

[0148] Using the covariance matrix in the initial state as the prior value of the covariance matrix in the previous time step, it can be expressed as:

[0149] In S32, the prediction process is represented as follows:

[0150]

[0151]

[0152]

[0153]

[0154]

[0155] In S34, the fusion process is represented as:

[0156]

[0157]

[0158] In S35, the prior values ​​of the covariance matrix obtained are expressed as follows:

[0159]

[0160]

[0161] In S3, the target state variables in the initial state are set as: [8500m 8500m 8500m 300m / s 100m / s 100m / s 0 1m / s] 2 0], set the noise covariance matrix as Q = Δw·I, where Δw = e -10 Set the covariance matrix in the initial state to diag(500). 2 500 2 500 2 100 2 100 2 100 2 ,0.1,0.1,0.1).

[0162] In the simulation experiment, a total of 50 Monte Carlo simulations were conducted. The target trajectory remained consistent throughout the simulations. For any given aircraft, the mean trajectory from all 50 simulations was fitted to represent its tracking trajectory. The results are as follows: Figure 3 As shown in the figure, the aircraft is able to accurately track the target.

[0163] Comparative Example

[0164] Comparative Example 1

[0165] The same experiment as in Example 1 was conducted, except that the EKF method was used. For details of the EKF method, please refer to the literature ZHU P, CHEN B. J C.Extended Kalman filter using a kernelrecursive least squares observer[C / OL] / / The 2011 International Joint Conference on Neural Networks.2011:1402-1408..

[0166] Comparative Example 2

[0167] The same experiment as in Example 1 was conducted, except that a dual-mode seeker distributed filtering method was used. For details of the dual-mode seeker distributed filtering method, please refer to the literature Liu Guangzhe, Zhang Ke, Lü Meibo, et al. Simulation study of dual-mode guidance based on extended Kalman filter algorithm [J / OL]. Aviation Weapons, 2018(1):27-32.

[0168] Figure 4-6 The simulation results of Example 1, Comparative Examples 1 and 2 are shown. In the comparison, the average value of 12 aircraft after 50 simulations was taken as the final simulation result. Figure 4 The position estimation error results are shown;

[0169] Figure 5 The results of the velocity estimation error are shown;

[0170] Figure 6 The results of the acceleration estimation error are shown.

[0171] from Figure 4-6 As can be seen from the example, the method in Example 1 has a significantly smaller estimation error for the target state variables than the method in the comparative example, and the convergence speed is faster.

[0172] The present invention has been described above with reference to preferred embodiments; however, these embodiments are merely exemplary and illustrative. Various substitutions and modifications can be made to the present invention based on these embodiments, all of which fall within the scope of protection of the present invention.

Claims

1. A weighted fusion distributed target tracking method based on sensor networks, characterized in that, Includes the following steps: S1. Establish a collaborative network, which includes multiple aircraft and communication connections between them; the collaborative network includes multiple node aircraft and multiple observation aircraft. S2. Set up the target motion model and observation model, and establish a fusion measurement model; S3. The aircraft uses a fusion measurement model to fuse the information received during the communication process and combine it with the target state quantities measured by itself to obtain the calibration target state quantities. S4. Each aircraft navigates and tracks the target based on the obtained calibration target state variables. The target motion model is represented as follows: ， Among them, subscript To indicate different aircraft, subscript express time, For aircraft exist The target state quantity at time t. Indicates the target relative to the aircraft Position in a three-dimensional Cartesian coordinate system Indicates the target relative to the aircraft Velocity in a three-dimensional Cartesian coordinate system Indicates the target relative to the aircraft Acceleration in a three-dimensional Cartesian coordinate system; It is a Gaussian white noise vector with a mean of 0, used to simulate the random variation of the target's acceleration; Represents the state transition matrix. Represents the state input matrix; The observation model is expressed as follows: ， in, Indicates aircraft exist The measurement vector at time, Indicates aircraft exist The observation matrix at time t is an identity matrix. Indicates aircraft exist The random noise vector at time step; The fusion measurement model is expressed as follows: ， in, , ， ， ， in, Indicates different aircraft in The set of measurement vectors at time points. Indicates different aircraft in The set of observation matrices at time points. Indicates different aircraft in The set of random noise vectors at time points. This represents the set of target state variables for different aircraft at any given time. In S3, the node aircraft receives observations transmitted by other observation aircraft, fuses them with its own detected observations and target predictions transmitted by other node aircraft to obtain the fused observations of the node aircraft, and transmits the fused observations to the observation aircraft and other node aircraft that are connected to it in communication. In S3, the fusion includes the following sub-steps: S31. For any node aircraft, set the target state variables, the measurement vector, and the covariance matrix in the initial state. S32. For any node aircraft, based on the fusion measurement model, and using the measurement information of the node aircraft at the next moment and the prior value of the target state variable at the previous moment, predict the posterior value of the target state variable and the posterior value of the covariance matrix at the next moment. S33. Each node aircraft will transmit the posterior values ​​of the target state variables and the covariance matrix obtained at the next moment to other node aircraft. S34. Fuse the posterior values ​​of the target state variables and the posterior values ​​of the covariance matrix obtained by all node aircraft at the next time step to obtain the fused posterior values ​​of the target state variables and the fused covariance matrix at the next time step. S35. Any node aircraft obtains the prior value of the covariance matrix at the next time step based on the posterior value of the fused covariance matrix at the next time step, combined with its own predicted posterior value of the covariance matrix at the next time step, and transmits the prior value of the covariance matrix at the next time step to other aircraft. S36. Repeat S32~S35 to predict subsequent time steps and obtain the posterior value of the target state quantity of the spacecraft at any node time step. Use this value as the calibration target state quantity of the spacecraft at that node time step.

2. The weighted fusion distributed target tracking method based on sensor networks according to claim 1, characterized in that, In S1, the observation aircraft is communicatively connected to at least one node aircraft, and multiple node aircraft are communicatively connected to each other.

3. The weighted fusion distributed target tracking method based on sensor networks according to claim 2, characterized in that, In S1, the node aircraft and the observation aircraft respectively use different types of sensors to observe the target.

4. The weighted fusion distributed target tracking method based on sensor networks according to claim 1, characterized in that, In S31, the target state variable in the initial state is taken as the prior value of the target state variable in the previous time step, and is expressed as: , Using the measurement vector in the initial state as the prior value of the measurement vector in the next moment, it can be expressed as: , Using the covariance matrix in the initial state as the prior value of the covariance matrix in the previous time step, it can be expressed as: , in, Indicates aircraft The target state quantity in the initial state. Indicates aircraft exist Prior value of the target state variable at time t. Indicates aircraft The measurement vector in the initial state, Indicates aircraft exist Prior values ​​of the measurement vector at time t. Indicates aircraft The covariance matrix in the initial state, Indicates aircraft exist Prior values ​​of the covariance matrix at time t.

5. The weighted fusion distributed target tracking method based on sensor networks according to claim 1, characterized in that, In S32, the prediction process is represented as follows: ， ， ， ， ， in, Indicates aircraft exist Prior value of the target state variable at time t. Represents the noise covariance matrix. Indicates aircraft exist Prior values ​​of the covariance matrix at time t. Indicates aircraft exist The covariance matrix at time t, Indicates aircraft exist The variance of observation noise at time step, Indicates aircraft exist Kalman gain at time step Indicates aircraft exist The measurement vector at time, Indicates aircraft exist The posterior value of the target state variable at time t. Represents a unit vector. Indicates aircraft exist The posterior value of the covariance matrix at time t.

6. The weighted fusion distributed target tracking method based on sensor networks according to claim 1, characterized in that, In S34, the fusion process is represented as: ， ， in, Indicates the total number of aircraft. Represents the posterior value of the fused covariance matrix. This represents the posterior value of the fused target state variable.

7. The weighted fusion distributed target tracking method based on sensor networks according to claim 1, characterized in that, In S35, the prior values ​​of the covariance matrix obtained are expressed as follows: ， ， in, Indicates aircraft exist Prior values ​​of the covariance matrix at time t. For aircraft The corresponding proportionality coefficient, Let be the trace function of the matrix.

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