A multi-aircraft-multi-target data association method based on photoelectric angle measurement information

Abstract

The application discloses a multi-aircraft-multi-target data correlation method based on photoelectric angle measurement information, and comprises the following steps: each aircraft adopts a photoelectric detector to measure initial positions of all targets, and uses an extended Kalman filter to filter measurement data to obtain an estimated position of each target by each aircraft; each aircraft matches and correlates estimated positions of all targets, and correlates same targets so that different aircrafts have same numbers for the same target. The method disclosed by the application has high target correlation accuracy and high robustness.

Description

Technical Field

[0001] This invention relates to a multi-aircraft-multi-target data association method based on photoelectric angle measurement information, belonging to the field of aircraft control technology. Background Technology

[0002] In many cases, it is necessary to use drone swarms to coordinate target strikes, i.e., aircraft coordinated guidance.

[0003] Cooperative guidance by aircraft, through information sharing and functional complementarity, can greatly improve the survivability of our aircraft and the damage effect on targets. In the process of multiple aircraft cooperating to strike multiple targets, there is the issue of target allocation, which presupposes that the observation data of the same targets among the aircraft can be correlated.

[0004] However, existing target association methods have limitations such as insufficient accuracy and complex calculations, and there is a lack of an engineering-friendly association method for scenarios with multiple aircraft and multiple targets.

[0005] Therefore, it is necessary to conduct more in-depth research on multi-vehicle multi-target association 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 multi-vehicle-multi-target data association method based on photoelectric angle measurement information, including the following steps:

[0007] S1. Each aircraft uses a photoelectric detector to measure the initial position of all targets, and uses an extended Kalman filter to filter the measurement data to obtain the estimated position of each aircraft for each target.

[0008] S2. Each aircraft matches and associates the estimated positions of all targets, and associates the same targets so that different aircraft have the same number for the same target.

[0009] In a preferred embodiment, in S1, the aircraft's photoelectric detector measures the initial position of the target to obtain the relative distance and relative speed between the aircraft and the target.

[0010] In a preferred embodiment, in the extended Kalman filter, the state variable x is set as: x = [r x ,r y ,r z ,v x ,v y ,v z ]

[0011] r x ,r y ,r zThe x, y, and z directions represent the relative distances between the aircraft and the target, respectively, and v represents the distance between the aircraft and the target. x ,v y ,v z These represent the relative velocities of the aircraft and the target in the x, y, and z directions, respectively.

[0012] In a preferred embodiment, the state transition matrix F(x) in the extended Kalman filter is set as follows:

[0013]

[0014] Where ΔT is the sampling time.

[0015] In a preferred embodiment, the observation equation h(x) in the extended Kalman filter is set as follows:

[0016]

[0017] The Jacobian matrix H(x) of the observation equation is set as follows:

[0018]

[0019] In a preferred embodiment, in S2, the Hungarian algorithm is used to associate the estimated positions of any two aircraft with all targets, and the target association is obtained by selecting the method with the minimum total cost.

[0020] In a preferred embodiment, the problem model of the Hungarian algorithm between any two aircraft is set as follows:

[0021]

[0022] a ij ∈{0,1}, i,j=1,2,…,N.

[0023] Among them, c ij Let a be the cost function, representing the degree of matching between the observations of the first aircraft to target i and the observations of the second aircraft to target j. ij This indicates whether the observations of the second aircraft on target j are assigned to the observations of the first aircraft on target i; if yes, it is 1, otherwise it is 0.

[0024] In a preferred embodiment, the cost function c ij Set to:

[0025]

[0026] in, This represents the relative estimated distance between the estimated position of the first aircraft relative to target i and the estimated position of the second aircraft relative to target j.

[0027] The beneficial effects of this invention include:

[0028] (1) High accuracy and robustness in target association;

[0029] (2) It has low computing power requirements, is more suitable for airborne aircraft, and has strong engineering application characteristics. Attached Figure Description

[0030] Figure 1 This diagram illustrates a preferred embodiment of a multi-vehicle-multi-target data association method based on photoelectric angle measurement information according to the present invention.

[0031] Figure 2 , Figure 3 The diagram illustrates the estimated position between two aircraft in a multi-aircraft-multi-target data association method based on photoelectric angle measurement information according to a preferred embodiment of the present invention.

[0032] Figure 4 The position error curves obtained from 50 Monte Carlo simulations in Example 1 are shown.

[0033] Figure 5 The target location estimation correlation diagram obtained in Example 1 is shown. Detailed Implementation

[0034] 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.

[0035] 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.

[0036] According to the present invention, a multi-vehicle-multi-target data association method based on photoelectric angle measurement information is provided, such as... Figure 1 As shown, it includes the following steps:

[0037] S1. Each aircraft uses a photoelectric detector to measure the initial position of all targets, and uses an extended Kalman filter to filter the measurement data to obtain the estimated position of each aircraft for each target.

[0038] S2. Each aircraft matches and associates the estimated positions of all targets, and associates the same targets so that different aircraft have the same number for the same target.

[0039] Preferably, the method also includes step S3, where each aircraft assigns a target based on the associated target number.

[0040] In S1, the aircraft's photoelectric detector measures the initial position of the target to obtain the relative distance and relative speed between the aircraft and the target.

[0041] Extended Kalman filtering is a filtering method for nonlinear systems. It estimates the state of the system by linearizing the nonlinear model and is widely used in the field of measurement and observation. Its filtering process includes state variable setting, nonlinear model establishment, linearization, initialization, prediction update and iteration.

[0042] In this invention, the extended Kalman filtering process is the same as the traditional extended Kalman filtering process, wherein,

[0043] The state variable x is set as: x = [r x ,r y ,r z ,v x ,v y ,v z ]

[0044] r x ,r y ,r z The x, y, and z directions represent the relative distances between the aircraft and the target, respectively, and v represents the distance between the aircraft and the target. x ,v y ,v z These represent the relative velocities of the aircraft and the target in the x, y, and z directions, respectively.

[0045] During the prediction process, the state transition matrix F(x) is set as follows:

[0046]

[0047] Where ΔT is the sampling time.

[0048] During the update process, the observation equation h(x) is set as follows:

[0049]

[0050] The Jacobian matrix H(x) of the observation equation is set as follows:

[0051]

[0052] In S2, the Hungarian algorithm is used to associate the estimated positions of any two aircraft with all targets, and the method with the minimum total cost is selected to obtain the target association.

[0053] The Hungarian Algorithm is an algorithm for solving linear programming problems, and it is particularly effective in solving assignment problems.

[0054] In this invention, the problem model for the Hungarian algorithm between any two aircraft is set as follows:

[0055]

[0056] a ij ∈{0,1}, i,j=1,2,…,N.

[0057] Among them, c ij Let a be the cost function, representing the degree of matching between the observations of the first aircraft to target i and the observations of the second aircraft to target j. ij This indicates whether to assign the observations of the second aircraft to the observations of the first aircraft to the target i; a value of 1 indicates yes, and a value of 0 indicates no. That is, a ij =1 indicates that target i observed by the first spacecraft and target j observed by the second spacecraft have the same number, a ij =0 indicates that the target i observed by the first aircraft and the target j observed by the second aircraft have different numbers.

[0058] According to the present invention, the higher the degree of matching, the better c ij The smaller.

[0059] Furthermore, the challenge of this invention lies in selecting a suitable cost function to obtain the optimal correlation.

[0060] The extended Kalman filter algorithm can filter out observation noise, but its filtering effect on initial errors is generally poor. When there is a large initial error, it is impossible to accurately locate the target position. However, since a photodetector is used, the error distance vector of each detected target relative to the true position is the same. Preferably, the relative distance between positions is estimated as the cost function.

[0061] However, the inventors discovered that when the estimated position between the two aircraft is, for example... Figure 2 , Figure 3 In both cases, if only relative distance is estimated... Quantitatively describing the degree of matching reveals that the optimal solution to the problem may not be unique, in which case the target data cannot be correctly associated.

[0062] in, Figure 2 This indicates whether the target coordinates are correctly associated. Figure 3 This indicates an incorrect target coordinate association. In the diagram, This indicates the estimated position of target number 1 under the first aircraft. This indicates the estimated location of target number 2 under the first aircraft. This indicates the estimated location of target number 3 under the first aircraft. This indicates the estimated location of target number 4 under the first aircraft. This indicates the estimated location of target number 1 under the second aircraft. This indicates the estimated location of target number 2 under the second aircraft. This indicates the estimated location of target number 3 under the second aircraft. This indicates the estimated location of target number 4 under the second aircraft.

[0063] against Figure 2 , Figure 3 In this scenario, if only the estimated relative distance between locations is used as the cost function, the costs in both cases will be the same, but obviously... Figure 3 The situation is incorrectly associated.

[0064] In a preferred embodiment, the cost function Set to:

[0065]

[0066] in, The relative estimated distance between the estimated position of the first aircraft relative to target i and the estimated position of the second aircraft relative to target j.

[0067] The aforementioned cost function can correct erroneous associations and improve robustness.

[0068] After being linked, each aircraft has the same target number. Based on this, target allocation can be performed on each aircraft, which can avoid the problem of some aircraft hitting the wrong target and losing some targets.

[0069] It should be understood that the various forms of processes shown above can be used to reorder, add, or delete steps. For example, the steps described in this invention disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this invention can be achieved, and this is not limited herein.

[0070] Example

[0071] Example 1

[0072] The simulation experiment includes the following steps:

[0073] S1. Each aircraft uses a photoelectric detector to measure the initial position of all targets, and uses an extended Kalman filter to filter the measurement data to obtain the estimated position of each aircraft for each target.

[0074] S2. Each aircraft matches and associates the estimated positions of all targets, and associates the same targets so that different aircraft have the same number for the same target.

[0075] In the extended Kalman filter, the state variable x is set as: x = [r x ,r y ,r z ,v x ,v y ,v z ]

[0076] The state transition matrix F(x) is set as follows:

[0077]

[0078] The observation equation h(x) is set as follows:

[0079]

[0080] The Jacobian matrix H(x) of the observation equation is set as follows:

[0081]

[0082] In S2, the Hungarian algorithm is used to associate the estimated positions of any two aircraft with all targets, and the target association is obtained by selecting the method with the minimum total cost. The problem model of the Hungarian algorithm between any two aircraft is set as follows:

[0083]

[0084] a ij ∈{0,1}, i,j=1,2,…,N.

[0085] The cost function is set as follows:

[0086]

[0087] During the simulation, four aircraft and four targets were set up. The initial positions and velocities of the aircraft and targets are shown in Table 1.

[0088] Table 1

[0089] Location / m Velocity (m / s) <![CDATA[Aircraft M1]]> (4000,25,1000) (-200,0,0) <![CDATA[Flight vehicle M2]]> (4050,25,1000) (-200,0,0) <![CDATA[Flight vehicle M3]]> (4050,-25,1000) (-200,0,0) <![CDATA[Flight vehicle M4]]> (4000,-25,1000) (-200,0,0) <![CDATA[Target T1]]> (0,50,0) (0,0,0) <![CDATA[Target T2]]> (-100,50,0) (0,0,0) <![CDATA[Target T3]]> (-100,-50,0) (0,0,0) <![CDATA[Target T4]]> (0,-50,0) (0,0,0)

[0090] During the simulation, the simulation step size in the extended Kalman filter algorithm was set to 0.01s, the initial value of the covariance matrix was P0, and the process noise variance Q and measurement noise variance R were set as follows:

[0091]

[0092] The position error curves obtained by performing 50 Monte Carlo simulations and comparing the estimated and actual positions are shown below. Figure 4 As shown, the optimal association relationship obtained is as follows: Figure 5 As shown.

[0093] from Figure 4 As can be seen, the extended Kalman algorithm can eliminate some position errors, but it cannot completely eliminate the initial position error caused by photoelectric detection errors.

[0094] Figure 5 In the middle, T i-j The figure shows the estimated position of aircraft i relative to target j. As can be seen from the figure, the four aircraft have the same number for the same target, realizing the accurate correlation of observation data of multiple aircraft to multiple targets.

[0095] 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 method for data association between multiple aircraft and multiple targets based on photoelectric angle measurement information, characterized in that, Includes the following steps: S1. Each aircraft uses a photoelectric detector to measure the initial position of all targets, and uses an extended Kalman filter to filter the measurement data to obtain the estimated position of each aircraft for each target. S2. Each aircraft matches and associates the estimated positions of all targets, and associates the same targets so that different aircraft have the same number for the same target; In S1, the aircraft's photoelectric detector measures the initial position of the target to obtain the relative distance and relative velocity between the aircraft and the target; In S2, the Hungarian algorithm is used to associate the estimated positions of any two aircraft with all targets, and the method with the minimum total cost is selected to obtain the target association.

2. The multi-vehicle-multi-target data association method based on photoelectric angle measurement information according to claim 1, characterized in that, In the extended Kalman filter, state variables are set. for: ; These represent the relative distances between the aircraft and the target in the x, y, and z directions, respectively. These represent the relative velocities of the aircraft and the target in the x, y, and z directions, respectively.

3. The multi-vehicle-multi-target data association method based on photoelectric angle measurement information according to claim 2, characterized in that, In the extended Kalman filter, the state transition matrix Set to: ； in, Sampling time.

4. The multi-vehicle-multi-target data association method based on photoelectric angle measurement information according to claim 2, characterized in that, In the extended Kalman filter, the observation equation Set to: ； Jacobian matrix of the observation equation Set to: 。 5. The multi-vehicle-multi-target data association method based on photoelectric angle measurement information according to claim 1, characterized in that, The problem model for the Hungarian algorithm between any two aircraft is set as follows: ； in, c ij Let be the cost function, representing the cost of the first of the two aircraft relative to the target. i The observation and the second aircraft's observation of the target j The degree of matching of the observations, a ij Indicate whether to engage the second aircraft with the target. j The observations were assigned to the first aircraft for the target. i The value is 1 if the observation is true, and 0 otherwise.

6. The multi-vehicle-multi-target data association method based on photoelectric angle measurement information according to claim 5, characterized in that, The cost function c ij Set to: ； in, This indicates that the first of the two aircraft is targeting the target. i The estimated position of the second aircraft relative to the target j The relative estimated distance of the estimated location.

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