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

Abstract

The invention discloses a multi-aircraft-multi-target data association method based on photoelectric angle measurement information, and the method comprises the following steps: each aircraft employs a photoelectric detector to measure the initial positions of all targets, carries out the filtering of the measurement data through extended Kalman filtering, and obtains the estimation position of each aircraft for each target; and each aircraft matches and associates the estimated positions of all the targets, and associates the same target, so that different aircrafts have the same number for the same target. The method disclosed by the invention is high in target association accuracy and high in robustness.

Description

Technical Field

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

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

[0003] Aircraft cooperative guidance can greatly improve the survival probability of our aircraft and the damage effect on the target through information sharing and functional complementarity. In the process of multiple aircraft coordinating to attack multiple targets, there is a problem of target allocation, and the premise of target allocation is that the observation data of the same target between aircraft can be associated.

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

[0005] Therefore, it is necessary to conduct more in-depth research on the multi-aircraft multi-target association method 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 multi-aircraft-multi-target data association method based on photoelectric angle measurement information, comprising 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 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 quantity x is set to: x = [r x ,r y ,r z ,v x ,v y ,v z ]

[0011] r x ,r y ,r zare the relative distances between the aircraft and the target in the x, y, and z directions, respectively. x ,v y ,v z They are the relative speeds between the aircraft and the target in the x, y, and z directions respectively.

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

[0013]

[0014] Where ΔT is the sampling time.

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

[0016]

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

[0018]

[0019] In a preferred embodiment, in S2, the estimated positions of all targets of any two aircraft are associated using the Hungarian algorithm, and the target association is obtained in a manner with the minimum total cost.

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

[0021]

[0022] a ij ∈{0,1},i,j=1,2,…,N.

[0023] Among them, c ij is the cost function, which represents the matching degree between the observation of target i by the first aircraft and the observation of target j by the second aircraft. ij Indicates whether the observation of the second aircraft on target j is assigned to the observation 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, It represents the relative estimated distance between the estimated position of target i by the first aircraft and the estimated position of target j by the second aircraft.

[0027] The beneficial effects of the present invention include:

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

[0029] (2) It has low computing power requirements and is more suitable for airborne aircraft, with strong engineering application potential. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 A schematic flow chart of a multi-aircraft-multi-target data association method based on photoelectric angle measurement information according to a preferred embodiment of the present invention is shown;

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

[0032] Figure 4 The position error curve obtained by 50 Monte Carlo simulations in Example 1 is shown;

[0033] Figure 5 The target position estimation correlation map obtained in Example 1 is shown. DETAILED DESCRIPTION

[0034] The present invention will be further described in detail below through the accompanying drawings and embodiments. Through these descriptions, the characteristics and advantages of the present invention will become more clear and distinct.

[0035] The word "exemplary" is used exclusively herein to mean "serving as an example, embodiment, 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 noted.

[0036] According to a multi-aircraft-multi-target data association method based on photoelectric angle measurement information provided by the present invention, Figure 1 As shown, the following steps are included:

[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, there is also step S3, in which each aircraft performs target allocation based on the associated target number.

[0040] In S1, the aircraft 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. It is widely used in measurement observation neighborhoods. Its filtering process includes state quantity setting, nonlinear model establishment, linearization, initialization, prediction update and iteration process.

[0042] In the present invention, the extended Kalman filter process is the same as the traditional extended Kalman filter process, wherein:

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

[0044] r x ,r y ,r z are the relative distances between the aircraft and the target in the x, y, and z directions, respectively. x ,v y ,v z They are the relative speeds between the aircraft and the target in the x, y, and z directions respectively.

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

[0046]

[0047] Where ΔT is the sampling time.

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

[0049]

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

[0051]

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

[0053] The Hungarian Algorithm is an algorithm for solving linear programming problems, especially for solving assignment problems.

[0054] In the present invention, the problem model of the Hungarian algorithm between any two aircraft is set as:

[0055]

[0056] a ij ∈{0,1},i,j=1,2,…,N.

[0057] Among them, c ij is the cost function, which represents the matching degree between the observation of target i by the first aircraft and the observation of target j by the second aircraft. ij Indicates whether the observation of the second aircraft on target j is assigned to the observation of the first aircraft on target i. If yes, it is 1, otherwise it is 0; that is, a ij =1 means that the target i observed by the first aircraft and the target j observed by the second aircraft have the same number, a ij =0 means 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 matching degree, the ij The smaller.

[0059] Furthermore, how to select a suitable cost function to obtain the best optimal association relationship is the difficulty of the present invention.

[0060] The extended Kalman filter algorithm can filter out observation noise, but the filtering effect on the initial error is general. When there is a large initial error, the target position cannot be accurately located. However, due to the use of photoelectric detectors, the error distance vector of each detected target relative to the true position is the same. Preferably, the relative distance between the estimated positions is used as the cost function.

[0061] However, the inventors have found that when the estimated position between the two aircraft is, for example Figure 2 , Figure 3 In both cases, if only the relative distance is estimated If the degree of matching is described quantitatively, there may be a situation where the optimal solution to the problem is not unique, and the target data cannot be correctly associated.

[0062] in, Figure 2 Indicates the correct association of target coordinates. Figure 3 Indicates the target coordinates are incorrectly associated. Indicates the estimated position of the target numbered 1 under the first aircraft. Indicates the estimated position of the target numbered 2 under the first aircraft. Indicates the estimated position of the target numbered 3 under the first aircraft. Indicates the estimated position of target number 4 under the first aircraft. Indicates the estimated position of the target numbered 1 under the second aircraft. Indicates the estimated position of the target numbered 2 under the second aircraft. Indicates the estimated position of target number 3 under the second aircraft. Indicates the estimated position of target number 4 under the second aircraft.

[0063] against Figure 2 , Figure 3 If only the relative distance between the estimated positions is used as the cost function, the final cost of the two cases is the same, but obviously Figure 3 There is an association error in the situation.

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

[0065]

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

[0067] Through the above cost function, the wrong association can be corrected and the robustness can be improved.

[0068] After association, each aircraft has the same target number. On this basis, targets are assigned to each aircraft, which can avoid the problem of individual aircraft hitting the wrong target and causing the loss of 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 the disclosure of the present invention can be performed in parallel, sequentially or in different orders, as long as the desired results of the technical solution disclosed in the present invention can be achieved, and this document does not limit this.

[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 quantity x is set to: x = [r x ,r y ,r z ,v x ,v y ,v z ]

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

[0077]

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

[0079]

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

[0081]

[0082] In S2, the Hungarian algorithm is used to associate the estimated positions of all targets of any two aircraft, 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:

[0083]

[0084] a ij ∈{0,1},i,j=1,2,…,N.

[0085] The cost function is set as:

[0086]

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

[0088] Table 1

[0089] Position / m Speed / (m / s) <![CDATA[Aircraft M1]]> (4000,25,1000) (-200,0,0) <![CDATA[Aircraft M2]]> (4050,25,1000) (-200,0,0) <![CDATA[Aircraft M3]]> (4050,-25,1000) (-200,0,0) <![CDATA[Aircraft 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 is taken as 0.01s, and the initial value of the covariance matrix P0, the process noise variance Q and the measurement noise variance R are set as follows:

[0091]

[0092] After 50 Monte Carlo simulations, the position error curve between the estimated position and the actual position is as follows: Figure 4 As shown in the figure, the optimal correlation relationship is obtained as Figure 5 shown.

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

[0094] Figure 5 In, T i-j It represents 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 association of multi-aircraft observation data for multiple targets.

[0095] The present invention has been described above in conjunction with preferred embodiments, but these embodiments are only exemplary and serve only as an illustration. On this basis, the present invention may be subjected to a variety of substitutions and improvements, all of which fall within the scope of protection of the present invention.

Claims

1. A multi-aircraft-multi-target data association method based on photoelectric angle measurement information, characterized in that: The following steps are involved: 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.

2. The multi-aircraft-multi-target data association method based on photoelectric angle measurement information according to claim 1 is characterized in that: In S1, the aircraft photoelectric detector measures the initial position of the target to obtain the relative distance and relative speed between the aircraft and the target.

3. The multi-aircraft-multi-target data association method based on photoelectric angle measurement information according to claim 1, characterized in that: In the extended Kalman filter, the state quantity x is set to: x = [r x ,r y ,r z ,v x ,v y ,v z ]r x ,r y ,r z are the relative distances between the aircraft and the target in the x, y, and z directions, respectively. x ,v y ,v z They are the relative speeds between the aircraft and the target in the x, y, and z directions respectively.

4. The multi-aircraft-multi-target data association method based on photoelectric angle measurement information according to claim 3 is characterized in that: In the extended Kalman filter, the state transfer matrix F(x) is set as: Where ΔT is the sampling time.

5. The multi-aircraft-multi-target data association method based on photoelectric angle measurement information according to claim 3 is characterized in that: In the extended Kalman filter, the observation equation h(x) is set as: The Jacobian matrix H(x) of the observation equation is set to:

6. The multi-aircraft-multi-target data association method based on photoelectric angle measurement information according to claim 1, characterized in that: In S2, the Hungarian algorithm is used to associate the estimated positions of any two aircraft for all targets, and the target association is obtained by choosing the method with the minimum total cost.

7. The multi-aircraft-multi-target data association method based on photoelectric angle measurement information according to claim 6 is characterized in that: The problem model of the Hungarian algorithm between any two aircraft is set as: Among them, c ij is the cost function, which represents the matching degree between the observation of target i by the first aircraft and the observation of target j by the second aircraft. ij Indicates whether the observation of the second aircraft on target j is assigned to the observation of the first aircraft on target i, if yes, it is 1, otherwise it is 0.

8. The multi-aircraft-multi-target data association method based on photoelectric angle measurement information according to claim 7 is characterized in that: The cost function c ij Set to: in, It represents the relative estimated distance between the estimated position of target i by the first aircraft and the estimated position of target j by the second aircraft.

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