Sensor network-based weighted fusion distributed target tracking method

Abstract

Disclosed in the present invention is a sensor network-based weighted fusion distributed target tracking method, comprising the following steps: establishing a collaborative network, the collaborative network comprising a plurality of aircrafts, and the plurality of aircrafts being in communication connection with each other; setting a target motion model and an observation model, and establishing a fusion measurement model; on the basis of the fusion measurement model, the aircrafts fusing information received during a communication process and, in light of target state quantities measured by themselves, obtaining calibration target state quantities; and, on the basis of the obtained calibration target state quantities, each aircraft performing navigation tracking on a target. The sensor network-based weighted fusion distributed target tracking method disclosed in the present invention more accurately estimates target state quantities with smaller estimation errors.

Description

A weighted fusion distributed target tracking method based on sensor networks Technical Field

[0001] The invention relates to a weighted fusion distributed target tracking method based on a sensor network, and belongs to the field of aircraft control. Background Art

[0002] During the multi-aircraft collaboration process, multiple aircraft can perform multi-level, multi-faceted, and multi-layered processing of data from multiple sensors through collaborative tracking methods, thereby significantly improving 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 its robustness is weaker and it has higher requirements on communication network resources.

[0004] Distributed fusion methods can save computational effort and communication bandwidth, provide greater stability, and, under certain conditions, achieve optimal target estimation. However, existing distributed target tracking methods rarely consider the balance of tracking result fusion and distribution, which can lead to reduced tracking accuracy for certain targets and increased system energy consumption.

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

[0006] Summary of the Invention

[0007] In order to overcome the above problems, the inventors conducted in-depth research and proposed a weighted fusion distributed target tracking method based on a sensor network, which includes the following steps:

[0008] S1. Establishing a collaborative network, wherein the collaborative network includes multiple aircraft, and the multiple aircraft are communicatively connected;

[0009] S2. Set the target motion model and observation model, and establish a fusion measurement model;

[0010] S3. Based on the fusion measurement model, the aircraft fuses the information received during the communication process and combines it with the target state quantity measured by itself to obtain the calibration target state quantity;

[0011] S4. Each aircraft performs navigation and tracking on the target according to the obtained calibration target state quantity.

[0012] In a preferred embodiment, in S1, the collaborative network includes multiple node aircraft and multiple observation aircraft, the observation aircraft is communicatively connected to at least one node aircraft, and the multiple node aircraft are communicatively connected to each other.

[0013] In a preferred embodiment, in S1, the node aircraft and the observation aircraft respectively use different types of sensors to observe the target.

[0014] In a preferred embodiment, the target motion model is expressed as: i,k+1 =FX i,k +GW k

[0015] Among them, the subscript i represents different aircraft, the subscript k represents the kth moment, is the target state of aircraft i at time k, x, y, z represent the position of the target relative to aircraft i in the three-dimensional rectangular coordinate system, represents the velocity of the target relative to the aircraft i in the three-dimensional rectangular coordinate system, W represents the acceleration of the target relative to the aircraft i in the three-dimensional rectangular coordinate system; k is a Gaussian white noise vector with a mean of 0, which is used to simulate the random changes of the target acceleration; F represents the state transfer matrix, and G represents the state input matrix;

[0016] The observation model is expressed as: Z i,k =H i,k X i,k +V i,k

[0017] Among them, Z i,k represents the measurement vector of aircraft i at time k, H i,k Represents the observation matrix of aircraft i at time k, which is the unit matrix, V i,k represents the random noise vector of aircraft i at time k;

[0018] The fusion measurement model is expressed as:

[0019] Among them, Z c k =[Z 1,k ;…;Z L,k ] T

[0020] H c k =[h 1,k ;…;h L,k ] T

[0021] V c k =[V 1,k ;…;V L,k ] T

[0022] X c k =[X 1,k ;…;X L,k ] T

[0023] Among them, Z c k represents the set of measurement vectors of different aircraft at time k, H c k Represents the set of observation matrices of different aircraft at time k, V c k represents the set of random noise vectors of different aircraft at time k, X c k Represents the set of target state quantities of different aircraft at time k.

[0024] In a preferred embodiment, in S3, the node aircraft receives observation quantities transmitted by other observation aircraft, fuses them with the observation quantities detected by itself and the target prediction quantities transmitted by other node aircraft, obtains the fused observation quantities of the node aircraft, and transmits the fused observation quantities to the observation aircraft and other node aircraft that are communicatively connected to it.

[0025] In a preferred embodiment, in S3, the fusion comprises the following sub-steps:

[0026] S31. For any node aircraft, set the target state quantity in the initial state, the measurement vector in the initial state, and the covariance matrix in the initial state.

[0027] S32. For any node aircraft, based on the fusion measurement model, the target state quantity posterior value and the covariance matrix posterior value at the next moment are predicted based on the measurement information of the node aircraft at the next moment and the target state quantity prior value at the previous moment;

[0028] S33, each node aircraft transmits the obtained posterior value of the target state quantity at the next moment and the posterior value of the covariance matrix to other node aircraft;

[0029] S34, fusing the next moment target state quantity posterior value and the covariance matrix posterior value obtained by all node aircraft to obtain the next moment fused target state quantity posterior value and the fused covariance matrix posterior value;

[0030] S35. Any node aircraft obtains a priori value of the covariance matrix at the next moment based on the fused covariance matrix posterior value at the next moment and combines it with its own predicted covariance matrix posterior value at the next moment, and transmits the priori value of the covariance matrix at the next moment to other aircraft;

[0031] S36. Repeat S32 to S35 to predict the subsequent time, obtain the posterior value of the target state quantity of any node aircraft at the subsequent time, and use it as the calibration target state quantity of the node aircraft at the subsequent time.

[0032] In a preferred embodiment, in S31, the target state quantity in the initial state is used as the prior value of the target state quantity at the previous moment, which is expressed as:

[0033] The measurement vector in the initial state is used as the prior value of the measurement vector at the next moment, which is expressed as:

[0034] The covariance matrix in the initial state is taken as the prior value of the covariance matrix at the previous moment, which is expressed as:

[0035] in, represents the target state of aircraft i in the initial state, represents the prior value of the target state of aircraft i at time k-1, represents the measurement vector of aircraft i in the initial state, represents the prior value of the measurement vector of aircraft i at time k, represents the covariance matrix of aircraft i in the initial state, represents the prior value of the covariance matrix of aircraft i at time k-1.

[0036] In a preferred embodiment, in S32, the prediction process is expressed as:

[0037] in, represents the prior value of the target state of aircraft i at time k, Q represents the noise covariance matrix, represents the prior value of the covariance matrix of aircraft i at time k-1, P i,k represents the covariance matrix of aircraft i at time k, represents the observation noise variance of aircraft i at time k, represents the Kalman gain of aircraft i at time k, Z i,k represents the measurement vector of aircraft i at time k, represents the target state posterior value of aircraft i at time k, I represents the unit vector, represents the posterior value of the covariance matrix of aircraft i at time k.

[0038] In a preferred embodiment, in S34, the fusion process is represented as follows:

[0039] Where l represents the total number of aircraft, represents the fusion covariance matrix posterior value, Represents the posterior value of the fused target state.

[0040] In a preferred embodiment, in S35, the obtained covariance matrix prior value is expressed as:

[0041] in, represents the prior value of the covariance matrix of aircraft i at time k, β i is the proportional coefficient corresponding to aircraft i, and trace() is the trace function of the matrix.

[0042] The beneficial effects of the present invention include:

[0043] (1) It has strong robustness while achieving accurate estimation, which is beneficial for engineering applications;

[0044] (2) The target state quantity is estimated more accurately and the estimation error is smaller;

[0045] (3) The estimation convergence speed is faster and the tracking delay is low. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] FIG1 is a flow chart showing a weighted fusion distributed target tracking method based on a sensor network according to a preferred embodiment of the present invention; FIG2 is a schematic diagram showing a communication connection of an aircraft in Example 1;

[0047] Figure 3 shows the target tracking trajectory result diagram in Example 1; Figure 4 shows a comparison diagram of the position estimation error results in Example 1 and Comparative Examples 1 and 2; Figure 5 shows a comparison diagram of the velocity estimation error results in Example 1 and Comparative Examples 1 and 2; Figure 6 shows a comparison diagram of the acceleration estimation error results in Example 1 and Comparative Examples 1 and 2. DETAILED DESCRIPTION

[0048] The present invention will be described in further detail below with reference to the accompanying drawings and examples, through which the features and advantages of the present invention will become more clearly understood.

[0049] The word "exemplary" is used exclusively herein to mean "serving as an example, example, or illustration." Any embodiment described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments. Although various aspects of the embodiments are shown in the drawings, the drawings are not necessarily drawn to scale unless otherwise indicated.

[0050] A weighted fusion distributed target tracking method based on a sensor network provided by the present invention, as shown in FIG1 , includes the following steps:

[0051] S1. Establishing a collaborative network, wherein the collaborative network includes multiple aircraft, and the multiple aircraft are communicatively connected;

[0052] S2. Set the target motion model and observation model, and establish a fusion measurement model;

[0053] S3. Based on the fusion measurement model, the aircraft fuses the information received during the communication process and combines it with the target state quantity measured by itself to obtain the calibration target state quantity;

[0054] S4. Each aircraft performs navigation and tracking on the target according to the obtained calibration target state quantity.

[0055] In S1, preferably, the collaborative network includes a plurality of node aircraft and a plurality of observation aircraft, the observation aircraft is communicatively connected to at least one node aircraft, and the plurality of node aircraft are communicatively connected to each other;

[0056] In S1, the node aircraft and the observation aircraft respectively use different types of sensors to measure the target and obtain the target state quantity.

[0057] Using different types of sensors to measure the target can improve the adaptability to the environment and the measurement accuracy. This will result in smaller error fluctuations in the obtained calibration target state quantity and more accurate measurement.

[0058] For example, the observation aircraft is equipped with an infrared seeker, whose measurement vectors of the target are pitch angle and deviation angle;

[0059] The node aircraft is equipped with a radar seeker, whose measurement vectors for the target are relative distance, pitch angle and offset angle.

[0060] In a preferred embodiment, one of the node aircraft selects and fuses the information received during the communication process. This fusion result is then transmitted to the other node aircraft, allowing them to combine their own measured target state quantities to obtain a calibrated target state quantity. This approach further reduces the computational workload of the node aircraft and reduces tracking latency.

[0061] According to the present invention, in S2, the target motion model is expressed as: X i,k+1 =FX i,k +GW k

[0062] Among them, the subscript i represents different aircraft, the subscript k represents the kth moment, is the target state of aircraft i at time k, x, y, z represent the position of the target relative to aircraft i in the three-dimensional rectangular coordinate system, represents the velocity of the target relative to the aircraft i in the three-dimensional rectangular coordinate system, W represents the acceleration of the target relative to the aircraft i in the three-dimensional rectangular coordinate system; k is a Gaussian white noise vector with a mean of 0, which is used to simulate the random changes of the target acceleration; F represents the state transfer matrix, and G represents the state quantity input matrix.

[0063] The observation model is expressed as: Z i,k =H i,k X i,k +V i,k

[0064] Among them, Z i,k represents the measurement vector of aircraft i at time k, H i,k Represents the observation matrix of aircraft i at time k, which is the unit matrix, V i,k represents the random noise vector of aircraft i at time k.

[0065] In a preferred embodiment, the fusion measurement model is expressed as:

[0066] Among them, Z c k =[Z 1,k ;…;Z L,k ] T

[0067] H c k =[h 1,k ;…;h L,k ] T

[0068] V c k =[V 1,k ;…;V L,k ] T

[0069] X c k =[X 1,k ;…;X L,k ] T

[0070] Among them, Z c k represents the set of measurement vectors of different aircraft at time k, H c kRepresents the set of observation matrices of different aircraft at time k, V c k represents the set of random noise vectors of different aircraft at time k, X c k Represents the set of target state quantities of different aircraft at time k.

[0071] 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, obtains the fusion quantity of the node aircraft, and transmits the fusion quantity to the observation aircraft and other node aircraft connected to it for communication.

[0072] Further preferably, in S3, the fusion includes the following sub-steps:

[0073] S31. For any aircraft, set the target state quantity in the initial state, the measurement vector in the initial state, and the covariance matrix in the initial state.

[0074] S32: For any aircraft, based on the measurement information of the aircraft at the next moment and the prior value of the target state at the previous moment, predict the posterior value of the target state and the posterior value of the covariance matrix at the next moment;

[0075] S33, each observation aircraft transmits the obtained posterior value of the target state quantity at the next moment and the posterior value of the covariance matrix to the node aircraft communicating with it, and each node aircraft transmits the received information and its own predicted posterior value of the target state quantity at the next moment and the posterior value of the covariance matrix to other node aircraft;

[0076] S34, fusing the next moment target state quantity posterior value and the covariance matrix posterior value obtained by all aircraft through the node aircraft to obtain the next moment fusion quantity, and transmitting the fusion quantity to all aircraft, the fusion quantity including the fusion target state quantity posterior value and the fusion covariance matrix posterior value;

[0077] S35. Each aircraft obtains a priori value of the covariance matrix at the next moment based on the fused covariance matrix posterior value at the next moment and its own predicted covariance matrix posterior value at the next moment, and transmits the priori value of the covariance matrix at the next moment to the node aircraft communicating with the aircraft;

[0078] S36. Repeat S32 to S35 to predict the subsequent moments, obtain the posterior value of the target state quantity of each aircraft at the subsequent moment, and use it as the calibration target state quantity of the aircraft at the subsequent moment.

[0079] In S31, the specific setting values ​​of the prior value of the target state quantity and the measurement vector under the initial state can be freely set by those skilled in the art based on experience. Preferably, the target state quantity under the initial state and the measurement vector under the initial state are set so that the covariance matrix at the initial moment satisfies: the main diagonal elements of the covariance matrix at the initial moment are greater than the difference between the true value of the target state and the prior value of the target state.

[0080] In S31, the target state quantity in the initial state is taken as the prior value of the target state quantity at the previous moment, which is expressed as:

[0081] The measurement vector in the initial state is used as the prior value of the measurement vector at the next moment, which is expressed as:

[0082] The covariance matrix in the initial state is taken as the prior value of the covariance matrix at the previous moment, which is expressed as:

[0083] in, Represents the target state of node aircraft i in the initial state, represents the prior value of the target state of aircraft i at time k-1, represents the measurement vector of aircraft i in the initial state, represents the prior value of the measurement vector of aircraft i at time k, represents the covariance matrix of aircraft i in the initial state, represents the prior value of the covariance matrix of aircraft i at time k-1.

[0084] In S32, the prediction process is expressed as:

[0085] in, represents the prior value of the target state of aircraft i at time k, Q represents the noise covariance matrix, represents the prior value of the covariance matrix of aircraft i at time k-1, P i,k represents the covariance matrix of aircraft i at time k, represents the observation noise variance of aircraft i at time k, represents the Kalman gain of aircraft i at time k, Z i,k represents the measurement vector of aircraft i at time k, represents the target state posterior value of aircraft i at time k, I represents the unit vector, represents the posterior value of the covariance matrix of aircraft i at time k.

[0086] In S34, preferably, any node aircraft is selected for fusion.

[0087] In S34, the fusion process is expressed as:

[0088] Where l represents the total number of aircraft, represents the fusion covariance matrix posterior value, Represents the posterior value of the fused target state.

[0089] In S35, the obtained covariance matrix prior value is expressed as:

[0090] in, represents the prior value of the covariance matrix of aircraft i at time k, β i is the proportional coefficient corresponding to aircraft i, and trace() is the trace function of the matrix.

[0091] Example

[0092] Example 1

[0093] In the simulation experiment, 12 aircraft were used to track the target. The initial parameters of the aircraft launch are shown in Table 1.

[0094] Table 1

[0095] Set the time step T = 0.01s, the cruise missile speed to 300m / s, the initial trajectory inclination to 15°, the initial trajectory deviation to 0°, the maximum overload to 20g, the target initial position to (8000m, 8000m, 8000m), the target speed to 100m / s, and the acceleration to 2m / s 2 In the simulation, three of the 12 aircraft 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 nine aircraft are used as observation aircraft and are set to have infrared seekers.

[0096] The simulation process includes the following steps:

[0097] S1. Establishing a collaborative network, wherein the collaborative network includes multiple aircraft, and the multiple aircraft are communicatively connected;

[0098] S2. Set the target motion model and observation model, and establish a fusion measurement model;

[0099] S3. Based on the fusion measurement model, the aircraft fuses the information received during the communication process and combines it with the target state quantity measured by itself to obtain the calibration target state quantity;

[0100] S4. Each aircraft performs navigation and tracking on the target according to the obtained calibration target state quantity.

[0101] The target motion model is expressed as: X i,k+1 =FX i,k +GW k

[0102] The observation model is expressed as: Z i,k =H i,k X i,k +V i,k

[0103] The fusion measurement model is expressed as: c k =H c k X c k +V c k

[0104] Among them, Z c k =[Z 1,k ;…;Z L,k ] T

[0105] H c k =[h 1,k ;…;h L,k ] T

[0106] V c k =[V 1,k ;…;V L,k ] T

[0107] X c k =[X 1,k ;…;X L,k ] T

[0108] In S3, the fusion includes the following sub-steps:

[0109] S31. For any node aircraft, set the target state quantity in the initial state, the measurement vector in the initial state, and the covariance matrix in the initial state.

[0110] S32. For any node aircraft, based on the fusion measurement model, the target state quantity posterior value and the covariance matrix posterior value at the next moment are predicted based on the measurement information of the node aircraft at the next moment and the target state quantity prior value at the previous moment;

[0111] S33, each node aircraft transmits the obtained posterior value of the target state quantity at the next moment and the posterior value of the covariance matrix to other node aircraft;

[0112] S34, fusing the next moment target state quantity posterior value and the covariance matrix posterior value obtained by all node aircraft to obtain the next moment fused target state quantity posterior value and the fused covariance matrix posterior value;

[0113] S35. Any node aircraft obtains a priori value of the covariance matrix at the next moment based on the fused covariance matrix posterior value at the next moment and combines it with its own predicted covariance matrix posterior value at the next moment, and transmits the priori value of the covariance matrix at the next moment to other aircraft;

[0114] S36. Repeat S32 to S35 to predict the subsequent time, obtain the posterior value of the target state quantity of any node aircraft at the subsequent time, and use it as the calibration target state quantity of the node aircraft at the subsequent time.

[0115] In S31, the target state quantity in the initial state is taken as the prior value of the target state quantity at the previous moment, which is expressed as:

[0116] The measurement vector in the initial state is used as the prior value of the measurement vector at the next moment, which is expressed as:

[0117] The covariance matrix in the initial state is taken as the prior value of the covariance matrix at the previous moment, which is expressed as:

[0118] In S32, the prediction process is expressed as:

[0119] In S34, the fusion process is expressed as:

[0120] In S35, the obtained covariance matrix prior value is expressed as:

[0121] In S3, the target state quantity in the initial state is set to: [8500m 8500m 8500m 300m / s 100m / s 100m / s 0 1m / s 2 0], set the noise covariance matrix to Q = Δw·I, where Δw = e -10 ; Set the initial covariance matrix to diag(500 2 ,500 2 ,500 2 ,100 2 ,100 2 ,100 2 ,0.1,0.1,0.1).

[0122] In the simulation experiment, a total of 50 Monte Carlo simulations were performed. The target trajectory was consistent during the simulation process. An aircraft was randomly selected, and the mean trajectory of the aircraft during the 50 simulations was fitted as the tracking trajectory of the aircraft. The results are shown in Figure 3. It can be seen from the figure that the aircraft can accurately track the target.

[0123] Comparative Example

[0124] Comparative Example 1

[0125] The same experiment as in Example 1 was conducted, except that the EKF method was used. The specific process of the EKF method can be found in the literature ZHU P, CHEN B, J C.Extended Kalman filter using a kernel recursive least squares observer[C / OL] / / The 2011 International Joint Conference on Neural Networks.2011:1402-1408..

[0126] Comparative Example 2

[0127] The same experiment as in Example 1 was conducted, except that a dual-mode seeker distributed filtering method was used. The specific process of the dual-mode seeker distributed filtering method can be found in the literature Liu Guangzhe, Zhang Ke, Lv Meibai, et al. Simulation study of dual-mode guidance based on extended Kalman filter algorithm [J / OL]. Aviation Weaponry, 2018(1):27-32.

[0128] Figures 4-6 show the simulation results of Example 1 and Comparative Examples 1 and 2. During the comparison process, the average value of 12 aircraft after 50 simulations was taken as the final simulation result, where Figure 4 shows the position estimation error result;

[0129] Figure 5 shows the speed estimation error results;

[0130] FIG6 shows the acceleration estimation error results.

[0131] As can be seen from Figures 4-6, the method in Example 1 has a significantly smaller target state quantity estimation error than the method in the comparative example, and converges faster.

[0132] The present invention has been described above with reference to preferred embodiments, but these embodiments are merely exemplary and serve only as illustrations. On this basis, various replacements and improvements can be made to the present invention, 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: The following steps are involved: S1. Establishing a collaborative network, wherein the collaborative network includes a plurality of aircrafts, and the plurality of aircrafts are communicatively connected with each other; S2, setting the target motion model and observation model, and establishing a fusion measurement model; S3, based on the fusion measurement model, the aircraft fuses the information received during the communication process and combines it with the target state quantity measured by itself to obtain the calibration target state quantity; S4. Each aircraft performs navigation and tracking on the target according to the obtained calibration target state quantity.

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

3. The weighted fusion distributed target tracking method based on sensor network according to claim 2 is 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 network according to claim 1 is characterized in that: The target motion model is expressed as: i,k+1 =FX i,k +GW k Among them, the subscript i represents different aircraft, the subscript k represents the kth moment, is the target state of aircraft i at time k, x, y, z represent the position of the target relative to aircraft i in the three-dimensional rectangular coordinate system, represents the speed of the target relative to the aircraft i in the three-dimensional rectangular coordinate system, W represents the acceleration of the target relative to the aircraft i in the three-dimensional rectangular coordinate system; k is a Gaussian white noise vector with a mean of 0, which is used to simulate the random change of target acceleration; F represents the state transfer matrix, and G represents the state quantity input matrix; The observation model is expressed as: Z i,k =H i,k X i,k +V i,k Among them, Z i,k represents the measurement vector of aircraft i at time k, H i,k represents the observation matrix of aircraft i at time k, which is the unit matrix, V i,k represents the random noise vector of aircraft i at time k; The fusion measurement model is expressed as: in, Among them, Z c k represents the set of measurement vectors of different aircraft at time k, H c k represents the set of observation matrices of different aircraft at time k, V c k represents the set of random noise vectors of different aircraft at time k, X c k Represents the set of target state quantities of different aircraft at time k.

5. The sensor network-based weighted fusion distributed target tracking method according to claim 2, characterized in that: In S3, the node aircraft receives observations transmitted by other observation aircraft, and fuses them with the observations detected by itself and the 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 for communication.

6. The weighted fusion distributed target tracking method based on sensor network according to claim 2 is characterized in that: In S3, the fusion includes the following sub-steps: S31. For any node aircraft, set the target state quantity in the initial state, the measurement vector in the initial state, and the covariance matrix in the initial state. S32, for any node aircraft, according to the fusion measurement model, based on the measurement information of the node aircraft at the next moment and the prior value of the target state at the previous moment, predict the posterior value of the target state at the next moment and the posterior value of the covariance matrix; S33, each node aircraft transmits the obtained posterior value of the target state quantity at the next moment and the posterior value of the covariance matrix to other node aircraft; S34: The posterior values ​​and covariances of the target state quantities at the next moment obtained by all node aircraft The matrix posteriori values ​​are fused to obtain the posteriori value of the fused target state quantity and the posteriori value of the fused covariance matrix at the next moment; S35, any node aircraft obtains a priori value of the covariance matrix at the next moment based on the fusion covariance matrix posterior value at the next moment and the covariance matrix posterior value at the next moment predicted by itself, and transmits the priori value of the covariance matrix at the next moment to other aircraft; S36. Repeat S32 to S35 to predict the subsequent time, obtain the posterior value of the target state quantity of any node aircraft at the subsequent time, and use it as the calibration target state quantity of the node aircraft at the subsequent time.

7. The sensor network-based weighted fusion distributed target tracking method according to claim 6, characterized in that: In S31, the target state quantity in the initial state is taken as the prior value of the target state quantity at the previous moment, which is expressed as: The measurement vector in the initial state is used as the prior value of the measurement vector at the next moment, expressed as: The covariance matrix in the initial state is taken as the prior value of the covariance matrix at the previous moment, expressed as: in, represents the target state of aircraft i in the initial state, represents the prior value of the target state of aircraft i at time k-1, represents the measurement vector of aircraft i in the initial state, represents the prior value of the measurement vector of aircraft i at time k, represents the covariance matrix of aircraft i in the initial state, Represents the prior value of the covariance matrix of aircraft i at time k-1.

8. The sensor network-based weighted fusion distributed target tracking method according to claim 6, characterized in that: In S32, the prediction process is expressed as: in, represents the prior value of the target state of aircraft i at time k, Q represents the noise covariance matrix, represents the prior value of the covariance matrix of aircraft i at time k-1, P i,k represents the covariance matrix of aircraft i at time k, represents the observation noise variance of aircraft i at time k, represents the Kalman gain of aircraft i at time k, Z i,k represents the measurement vector of aircraft i at time k, represents the posterior value of the target state of aircraft i at time k, I represents the unit vector, represents the posterior value of the covariance matrix of aircraft i at time k.

9. The sensor network-based weighted fusion distributed target tracking method according to claim 6, characterized in that: In S34, the fusion process is expressed as: Where l represents the total number of aircraft, represents the fusion covariance matrix posterior value, Represents the posterior value of the fused target state.

10. The weighted fusion distributed target tracking method based on sensor network according to claim 6, characterized in that: In S35, the obtained covariance matrix prior value is expressed as: in, represents the prior value of the covariance matrix of aircraft i at time k, β i is the proportional coefficient corresponding to aircraft i, and trace() is the trace function of the matrix.

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