Weighted fusion distributed target tracking method based on sensor network

Abstract

The invention discloses a weighted fusion distributed target tracking method based on a sensor network, and the method comprises the following steps: building a collaborative network which comprises a plurality of aircrafts which are in communication connection; setting a target motion model and an observation model, and establishing a fusion measurement model; the aircraft fuses the information received in the communication process based on a fusion measurement model, and obtains a calibration target state quantity in combination with the target state quantity measured by the aircraft; and each aircraft navigates and tracks the target according to the obtained calibration target state quantity. According to the weighted fusion distributed target tracking method based on the sensor network disclosed by the invention, the target state quantity estimation is more accurate, and the estimation error is smaller.

Description

Technical Field

[0001] The present 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] In the process of multi-aircraft collaboration, multi-aircraft can perform multi-level, multi-faceted, and multi-layered processing on data from multiple sensors through collaborative tracking methods, 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 optimal estimation performance, but it has weak robustness and higher requirements for communication network resources.

[0004] The distributed fusion method can save computational load and communication bandwidth, has stronger stability, and can also achieve the optimal estimation of the target under certain conditions. However, existing distributed target tracking methods rarely consider the balance of tracking result fusion and allocation, which may lead to a decrease in the tracking accuracy of some targets and an increase in the energy consumption of the system.

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

[0006] In order to overcome the above problems, the inventors have conducted in-depth research and proposed a weighted fusion distributed target tracking method based on a sensor network, including the following steps:

[0007] S1. Establish a collaborative network, where the collaborative network includes multiple aircraft, and the multiple aircraft are communicatively connected to each other;

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

[0009] S3. Based on the fusion measurement model, the aircraft fuses the information received during the communication process, and combines the target state quantity measured by itself to obtain a calibrated target state quantity;

[0010] S4. Each aircraft performs navigation tracking on the target according to the obtained calibrated target state quantity.

[0011] 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.

[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 expressed as:

[0014] X i,k+1 = F X i,k + G W k

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

[0016] The observation model is expressed as:

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

[0018] where Z i,k represents the measurement vector of aircraft i at the k-th moment, H i,k represents the observation matrix of aircraft i at the k-th moment, is the identity matrix, and V i,k represents the random noise vector of aircraft i at the k-th moment;

[0019] The fusion measurement model is expressed as:

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

[0021] where 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] wherein, 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.

[0026] In a preferred embodiment, in S3, the node aircraft receives the observed quantities transmitted by other observing aircraft, fuses them with the observed quantities detected by itself and the target predicted quantities transmitted by other node aircraft, obtains the fused observed quantity of the node aircraft, and transmits the fused observed quantity to the observing aircraft and other node aircraft that are communicatively connected to it.

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

[0028] 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.

[0029] 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 quantity at the previous moment, predict and obtain the posterior value of the target state quantity and the posterior value of the covariance matrix at the next moment.

[0030] S33. Each node aircraft transmits the obtained posterior value of the target state quantity and the posterior value of the covariance matrix at the next moment to other node aircraft.

[0031] S34. Fuse the posterior values of the target state quantities and the posterior values of the covariance matrices at the next moment obtained by all node aircraft to obtain the posterior value of the fused target state quantity and the posterior value of the fused covariance matrix at the next moment.

[0032] ​​S35. Any node aircraft obtains the prior value of the covariance matrix at the next moment based on the posterior value of the fused covariance matrix at the next moment and combines it with the posterior value of the covariance matrix at the next moment predicted by itself, and transmits the prior value of the covariance matrix at the next moment to other aircraft;

[0033] S36. Repeat S32 - S35 to predict subsequent moments, obtain the posterior value of the target state quantity of any node aircraft at subsequent moments, and use it as the calibrated target state quantity of this node aircraft at subsequent moments.

[0034] 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, expressed as:

[0035] The measurement vector in the initial state is used as the prior value of the measurement vector at the next moment, expressed as:

[0036] The covariance matrix in the initial state is used as the prior value of the covariance matrix at the previous moment, expressed as:

[0037] Among them, represents the target state quantity of aircraft i in the initial state, represents the prior value of the target state quantity of aircraft i at the (k - 1)th moment, represents the measurement vector of aircraft i in the initial state, represents the prior value of the measurement vector of aircraft i at the kth moment, represents the covariance matrix of aircraft i in the initial state, represents the prior value of the covariance matrix of aircraft i at the (k - 1)th moment.

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

[0039]

[0040]

[0041]

[0042]

[0043]

[0044] Among them, represents the prior value of the target state quantity of aircraft i at the kth moment, Q represents the noise covariance matrix, represents the prior value of the covariance matrix of aircraft i at the (k - 1)th moment, P i,kRepresents 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 quantity 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.

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

[0046]

[0047]

[0048] where l represents the total number of aircraft, Represents the posterior value of the fused covariance matrix, Represents the posterior value of the fused target state quantity.

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

[0050]

[0051]

[0052] where, Represents the prior value of the covariance matrix of aircraft i at time k, β i is the proportionality coefficient corresponding to aircraft i, and trace() is the trace function of the matrix.

[0053] The beneficial effects of the present invention include:

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

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

[0056] (3) The estimation convergence speed is faster and the tracking delay is low. Description of the Drawings

[0057] Figure 1 Shows 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;

[0058] Figure 2 Shows a schematic diagram of the communication connection of the aircraft in Embodiment 1;

[0059] Figure 3 Show the target tracking trajectory result diagram in Embodiment 1;

[0060] Figure 4 Show the comparison diagram of position estimation error results in Embodiment 1 and Comparative Examples 1 and 2;

[0061] Figure 5 Show the comparison diagram of velocity estimation error results in Embodiment 1 and Comparative Examples 1 and 2;

[0062] Figure 6 Show the comparison diagram of acceleration estimation error results in Embodiment 1 and Comparative Examples 1 and 2. Detailed implementation manners

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

[0064] The special word "exemplary" here means "serving as an example, embodiment or illustrative". Any embodiment described as "exemplary" here does not have to be construed as superior to or better than other embodiments. Although various aspects of the embodiments are shown in the drawings, unless otherwise specified, the drawings do not have to be drawn to scale.

[0065] A weighted fusion distributed target tracking method based on a sensor network provided by the present invention, as Figure 1 shown, includes the following steps:

[0066] S1. Establish a collaborative network, where the collaborative network includes multiple aircraft, and the multiple aircraft are communicatively connected;

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

[0068] S3. Based on the fusion measurement model, the aircraft fuses the information received during the communication process, and combines the target state quantity measured by itself to obtain a calibrated target state quantity;

[0069] S4. Each aircraft navigates and tracks the target according to the obtained calibrated target state quantity.

[0070] In S1, preferably, 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;

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

[0072] Measuring the target using different types of sensors can improve the adaptability to the environment and the measurement accuracy. As a result, the error fluctuation of the calibrated target state quantity obtained is smaller, and the measurement is more accurate.

[0073] For example, an infrared seeker is carried on the observation aircraft, and its measurement vectors for the target are the pitch angle and the offset angle;

[0074] A radar seeker is carried on the node aircraft, and its measurement vectors for the target are the relative distance, the pitch angle, and the offset angle.

[0075] In a preferred embodiment, any one of the node aircraft fuses the information received during the communication process and transmits the fusion result to other node aircraft, so that other node aircraft can combine the target state quantity measured by themselves to obtain the calibrated target state quantity. In this way, the computational load of the node aircraft can be further reduced, and the tracking delay can be reduced.

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

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

[0078] where the subscript i represents different aircraft, and the subscript k represents the k-th moment, is the target state quantity of aircraft i at the k-th moment, 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 aircraft i in the three-dimensional rectangular coordinate system, represents the acceleration of the target relative to aircraft i in the three-dimensional rectangular coordinate system; W k is a Gaussian white noise vector with a mean of 0, used to simulate the random change of the target acceleration; F represents the state transition matrix, and G represents the state quantity input matrix.

[0079] The observation model is expressed as:

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

[0081] where Z i,k represents the measurement vector of aircraft i at the k-th moment, H i,k represents the observation matrix of aircraft i at the k-th moment, which is the identity matrix, and V i,k represents the random noise vector of aircraft i at the k-th moment.

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

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

[0084] where 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] where 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, and X c k represents the set of target state quantities of different aircraft at time k.

[0089] According to the present invention, in S3, the node aircraft receives the target state quantities transmitted by other observing aircraft, fuses them with the target state quantities observed by itself and the target prediction quantities transmitted by other node aircraft, obtains the fusion quantity of this node aircraft, and transmits this fusion quantity to the observing aircraft and other node aircraft communicatively connected thereto.

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

[0091] ​​​​S31. For any aircraft, set the target state quantity, the measurement vector in the initial state, 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 quantity at the previous moment, predict the posterior value of the target state quantity and the posterior value of the covariance matrix at the next moment.

[0093] S33. Each observing aircraft transmits the posterior value of the target state quantity and the posterior value of the covariance matrix at the next moment obtained to the node aircraft communicating with it, and each node aircraft transmits the received information and the posterior value of the target state quantity and the posterior value of the covariance matrix at the next moment predicted by itself to other node aircraft.

[0094] S34. Through the node aircraft, fuse the posterior value of the target state quantity and the posterior value of the covariance matrix at the next moment obtained by all aircraft to obtain the fusion quantity at the next moment, and transmit the fusion quantity to all aircraft. The fusion quantity includes the posterior value of the fused target state quantity and the posterior value of the fused covariance matrix.

[0095] S35. Each aircraft, based on the posterior value of the fused covariance matrix at the next moment, combines it with the posterior value of the covariance matrix at the next moment predicted by itself to obtain the prior value of the covariance matrix at the next moment, and transmits the prior value of the covariance matrix at the next moment to the node aircraft communicating with the aircraft.

[0096] S36. Repeat S32 - S35 to predict subsequent moments, obtain the posterior value of the target state quantity of each aircraft at subsequent moments, and use it as the calibrated target state quantity of the aircraft at subsequent moments.

[0097] In S31, those skilled in the art can freely set the specific values of the prior value of the target state quantity and the measurement vector in the initial state. Preferably, the target state quantity and the measurement vector in the initial state are set such 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.

[0098] In S31, take the target state quantity in the initial state as the prior value of the target state quantity at the previous moment, expressed as:

[0099] Take the measurement vector in the initial state as the prior value of the measurement vector at the next moment, expressed as:

[0100] Take the covariance matrix in the initial state as the prior value of the covariance matrix at the previous moment, expressed as:

[0101] Among them, represents the target state quantity of the node aircraft i in the initial state, represents the prior value of the target state quantity of the aircraft i at the (k - 1)th moment, represents the measurement vector of the aircraft i in the initial state, represents the prior value of the measurement vector of the aircraft i at the kth moment, represents the covariance matrix of the aircraft i in the initial state, represents the prior value of the covariance matrix of the aircraft i at the (k - 1)th moment.

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

[0103]

[0104]

[0105]

[0106]

[0107]

[0108] Among them, represents the prior value of the target state quantity of the aircraft i at the kth moment, Q represents the noise covariance matrix, represents the prior value of the covariance matrix of the aircraft i at the (k - 1)th moment, P i,k represents the covariance matrix of the aircraft i at the kth moment, represents the observation noise variance of the aircraft i at the kth moment, represents the Kalman gain of the aircraft i at the kth moment, Z i,k represents the measurement vector of the aircraft i at the kth moment, represents the posterior value of the target state quantity of the aircraft i at the kth moment, I represents the identity vector, represents the posterior value of the covariance matrix of the aircraft i at the kth moment.

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

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

[0111]

[0112]

[0113] Among them, l represents the total number of aircraft, represents the posterior value of the fusion covariance matrix, Represents the posterior value of the fused target state quantity.

[0114] In S35, the prior value of the covariance matrix obtained is expressed as:

[0115]

[0116]

[0117] Wherein, represents the prior value of the covariance matrix of vehicle i at time k, and β i is the proportionality coefficient corresponding to vehicle i, and trace() is the trace function of the matrix.

[0118] Embodiment

[0119] Embodiment 1

[0120] A simulation experiment is carried out. 12 vehicles are used to track the target, and the initial parameters of the vehicle launch are shown in Table 1.

[0121] Table 1

[0122]

[0123] Set the time step T = 0.01 s, the speed of the cruise missile is 300 m / s, the initial ballistic inclination is 15°, the initial ballistic deflection angle is 0°, the maximum overload is 20 g, the target initial position is (8000 m, 8000 m, 8000 m), the target speed is 100 m / s, and it makes a slow turning motion with an acceleration of 2 m / s 2 Among the 12 vehicles, 3 are used as node vehicles, namely the vehicles numbered 1, 5, and 9. During the simulation process, it is set that they have radar seekers, and the radar scanning period is 0.01 s. The remaining 9 vehicles are used as observation vehicles and are set to have infrared seekers.

[0124] During the simulation process, it includes the following steps:

[0125] S1. Establish a cooperative network, which includes multiple vehicles and the multiple vehicles are communicatively connected;

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

[0127] S3. Based on the fusion measurement model, the vehicle fuses the information received during the communication process and combines the target state quantity measured by itself to obtain a calibrated target state quantity;

[0128] S4. Each vehicle navigates and tracks the target according to the obtained calibrated target state quantity.

[0129] The target motion model is expressed as:

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

[0131] The observation model is expressed as:

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

[0133] The fusion measurement model is expressed as:

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

[0135] Wherein, 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 quantity, the measurement vector in the initial state, and the covariance matrix in the initial state.

[0141] S32. For any node aircraft, according to the fusion measurement model, based on the measurement information of the next moment of this node aircraft and the prior value of the target state quantity of the previous moment, predict and obtain the posterior value of the target state quantity of the next moment and the posterior value of the covariance matrix.​​​​

[0142] S33. Each node aircraft transmits the posterior value of the target state quantity and the posterior value of the covariance matrix obtained at the next moment to other node aircraft;

[0143] S34. The posterior values of the target state quantities and the posterior values of the covariance matrices obtained by all node aircraft at the next moment are fused to obtain the posterior value of the fused target state quantity and the posterior value of the fused covariance matrix at the next moment;

[0144] S35. Based on the posterior value of the fused covariance matrix at the next moment, any node aircraft combines its own predicted posterior value of the covariance matrix at the next moment to obtain the prior value of the covariance matrix at the next moment, and transmits the prior value of the covariance matrix at the next moment to other aircraft;

[0145] S36. Repeat S32 - S35 to predict subsequent moments, obtain the posterior value of the target state quantity of any node aircraft at subsequent moments, and use it as the calibrated target state quantity of this node aircraft at subsequent moments.

[0146] 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, denoted as:

[0147] The measurement vector in the initial state is used as the prior value of the measurement vector at the next moment, denoted as:

[0148] The covariance matrix in the initial state is used as the prior value of the covariance matrix at the previous moment, denoted as:

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

[0150]

[0151]

[0152]

[0153]

[0154]

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

[0156]

[0157]

[0158] In S35, the obtained prior value of the covariance matrix is denoted as:

[0159]

[0160]

[0161] Among them, 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], the noise covariance matrix is set to Q = Δw·I, where Δw = e -10 ; the covariance matrix in the initial state is set 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 are carried out. During the simulation process, the target trajectories are consistent. Arbitrarily select an aircraft, and fit the trajectory mean of this aircraft during the 50 simulation processes as the tracking trajectory of this aircraft. The results are as Figure 3 shown. It can be seen from the figure that the aircraft can achieve accurate tracking of the target.

[0163] Comparative example

[0164] Comparative example 1

[0165] The same experiment as in Example 1 is carried out, except that the EKF method is used. The specific process of the EKF method can be referred to 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.

[0166] Comparative example 2

[0167] The same experiment as in Example 1 is carried out, except that the dual - mode seeker distributed filtering method is used. The specific process of the dual - mode seeker distributed filtering method can be referred to the literature Liu Guangzhe, Zhang Ke, Lü Meibo, et al. Simulation research on dual - mode guidance based on extended Kalman filter algorithm[J / OL]. Aero Weaponry, 2018(1):27 - 32.

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

[0169] Figure 5 the velocity estimation error result is shown;

[0170] Figure 6 the acceleration estimation error result is shown.

[0171] From Figures 4 - 6 it can be seen that for the method in Example 1, the estimation error of the target state quantity is significantly smaller than that of the method in the comparative example, and the convergence speed is faster.

[0172] The present invention has been described above in combination with preferred embodiments. However, these embodiments are merely exemplary and only serve an illustrative purpose. On this basis, various substitutions and improvements can be made to the present invention, and these all fall within the protection scope of the present invention.

Claims

1. A weighted fusion distributed target tracking method based on a sensor network, characterized in that, it includes the following steps: S1. Establish a collaborative network, where the collaborative network includes multiple aircraft, and the multiple aircraft are communicatively connected; S2. Set up a target motion model and an observation model, and establish a fusion measurement model; S3. Based on the fusion measurement model, the aircraft fuses the information received during the communication process, combines it with the target state quantity measured by itself, and obtains a calibrated target state quantity; S4. Each aircraft navigates and tracks the target according to the obtained calibrated target state quantity.

2. The weighted fusion distributed target tracking method based on a sensor network according to claim 1, characterized in that, 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.

3. The weighted fusion distributed target tracking method based on a sensor network 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 a sensor network according to claim 1, characterized in that, the target motion model is expressed as: X i,k+1 = FX i,k + GW k where the subscript \(i\) represents different aircraft, and the subscript \(k\) represents the \(k\)th moment, is the target state quantity of aircraft \(i\) at the \(k\)th moment, where \(x\), \(y\), and \(z\) represent the position of the target relative to aircraft \(i\) in a three-dimensional rectangular coordinate system, represents the velocity of the target relative to aircraft \(i\) in a three-dimensional rectangular coordinate system, represents the acceleration of the target relative to aircraft \(i\) in a three-dimensional rectangular coordinate system; \(W\) k is a Gaussian white noise vector with a mean of 0, used to simulate the random variation of the target acceleration; \(F\) represents the state transition 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 identity matrix, V i,k represents the random noise vector of aircraft i at time k; the fusion measurement model is expressed as: Among them, 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 weighted fusion distributed target tracking method based on a sensor network according to claim 2, characterized in that, in S3, the node aircraft receives the observed quantities transmitted by other observation aircraft, fuses them with the observed quantities detected by itself and the target predicted quantities transmitted by other node aircraft, obtains the fusion observed quantity of this node aircraft, and transmits the fusion observed quantity to the observation aircraft and other node aircraft communicatively connected to it.

6. The weighted fusion distributed target tracking method based on a sensor network according to claim 2, 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 this node aircraft at the next moment and the prior value of the target state quantity at the previous moment, predict and obtain the posterior value of the target state quantity and the posterior value of the covariance matrix at the next moment; S33. Each node aircraft transmits the obtained posterior value of the target state quantity and the posterior value of the covariance matrix at the next moment to other node aircraft; S34. Fuse the posterior values of the target state quantity and the posterior values of the covariance matrix at the next moment obtained by all node aircraft to obtain the posterior value of the fusion target state quantity and the posterior value of the fusion covariance matrix at the next moment; S35. Any node aircraft, based on the posterior value of the fusion covariance matrix at the next moment, combines it with the predicted posterior value of the covariance matrix at the next moment by itself, obtains the prior value of the covariance matrix at the next moment, and transmits the prior value of the covariance matrix at the next moment to other aircraft; S36. Repeat S32 - S35 to predict subsequent moments, obtain the posterior value of the target state quantity of any node aircraft at subsequent moments, and use it as the calibrated target state quantity of the node aircraft at subsequent moments.

7. The weighted fusion distributed target tracking method based on a sensor network according to claim 6, wherein, 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: 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 used as the prior value of the covariance matrix at the previous moment, denoted as: Among them, represents the target state quantity of the aircraft i in the initial state, represents the prior value of the target state quantity of the aircraft i at the (k - 1)th moment, represents the measurement vector of the aircraft i in the initial state, represents the prior value of the measurement vector of the aircraft i at the kth moment, represents the covariance matrix of the aircraft i in the initial state, represents the prior value of the covariance matrix of the aircraft i at the (k - 1)th moment.

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

9. The weighted fusion distributed target tracking method based on a sensor network according to claim 6, wherein, In S34, the fusion process is expressed as: where \(l\) represents the total number of aircraft, represents the posterior value of the fusion covariance matrix, represents the posterior value of the fusion target state quantity.

10. The weighted fusion distributed target tracking method based on a sensor network according to claim 6, wherein, In S35, the prior value of the covariance matrix obtained is expressed as: Among them, represents the prior value of the covariance matrix of the aircraft i at the k-th moment, and β i is the proportionality coefficient corresponding to the aircraft i, and trace() is the trace function of the matrix.

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