Multi-radar cooperative detection planning method based on task efficiency

Abstract

The invention discloses a multi-radar cooperative detection planning method based on task efficiency, and the method comprises the steps: constructing a motion model of a target and an airborne radar, and constructing a detection task model; constructing a multi-task echo signal-to-noise ratio measurement index taking task-radar node selection parameters as optimization variables; constructing a multi-machine radar task allocation global utility function taking task-radar node selection parameters as optimization variables as a measurement index of multi-task comprehensive execution efficiency; establishing a multi-radar cooperative detection planning model based on task efficiency by taking given multi-radar maximum available time resources, echo signal-to-noise ratio and system performance limitation as constraint conditions and taking maximization of a multi-radar cooperative detection task allocation global utility function as an optimization target; and solving the planning model in combination with an interior point method. According to the method, the optimal distribution of the optimal task-radar node selection parameters of the multi-machine radar is realized, and the multi-task comprehensive execution efficiency of the multi-machine radar is effectively improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of radar signal processing, and in particular relates to a multi-machine radar collaborative detection planning method based on mission effectiveness. Background Art

[0002] In a complex battlefield environment, single-base radars have a small detection range and are difficult to achieve flexible radiation resource management, so the role they can play and the functions they can achieve are very limited. Compared with traditional single-base radars, multi-machine radar networks can fully mine target feature information from different frequency bands, different time and space, and different polarization modes, and can fully obtain battlefield environment situations. They can efficiently perform multiple tasks, suppress interference, effectively fight against dense clutter environments, and have stronger survivability in complex electromagnetic scenarios. They have significant advantages in applications such as target search, tracking, confirmation, and guidance.

[0003] In practical applications, as battlefields gradually highlight diversified mission requirements, radar multi-task coordination is also of great significance. With the emergence and gradual maturity of radar systems such as phased arrays, frequency-controlled arrays, and digital arrays, multi-machine radar networks can achieve different detection functions by adjusting their working modes and working parameters to complete diversified combat tasks such as search, tracking, confirmation, and guidance. Therefore, there is an important application prospect for improving multi-machine radars.

[0004] At present, multi-task dynamic programming based on task effectiveness has become a hot topic of research, and many scholars at home and abroad have achieved remarkable results in this regard. However, most of the existing studies are based on single radar node task scenarios, and there are few studies on collaborative detection task allocation for general multi-radar and multi-task scenarios. In the multi-radar collaborative detection scenario based on task effectiveness, the task dynamic programming method also needs to adaptively optimize the detection task-radar node selection parameters under the condition of meeting the multi-task performance constraints. The corresponding mathematical model is more complex, and the dimension of the optimization parameters also increases accordingly, which is a more complex and challenging problem. Therefore, from the perspective of improving the comprehensive execution efficiency of multi-tasks of multi-radars, it is of great significance to carry out research on multi-radar collaborative detection planning based on task effectiveness.

[0005] In summary, there is no multi-radar collaborative detection planning method based on mission effectiveness in the prior art. Summary of the invention

[0006] Purpose of the invention: The purpose of the present invention is to provide a multi-radar collaborative detection planning method that can improve the multi-task comprehensive execution efficiency of multi-radars.

[0007] Technical solution: The multi-radar collaborative detection planning method based on mission effectiveness of the present invention comprises the following steps:

[0008] Construct motion models of the target and airborne radar respectively;

[0009] Considering that the multi-machine cooperative detection tasks are classified into four types of tasks: target search, tracking, confirmation, and guidance, a detection task model is constructed;

[0010] Construct a multi-task echo signal-to-noise ratio measurement index with the task-radar node selection parameter as the optimization variable;

[0011] A global utility function for multi-aircraft radar task allocation with task-radar node selection parameters as optimization variables is constructed as a measure of the comprehensive execution efficiency of multi-tasks.

[0012] Taking the given maximum available time resources, echo signal-to-noise ratio and system performance limitations of multiple radars as constraints, and maximizing the global utility function of the multi-radar collaborative detection task allocation as the optimization goal, a multi-radar collaborative detection planning model based on task effectiveness is established.

[0013] The interior point method is used to solve the multi-radar collaborative detection planning model based on mission effectiveness.

[0014] Furthermore, the motion model of the target and the airborne radar is:

[0015] X m,k =F m X m,k-1

[0016] X n,k =F n X n,k-1

[0017] Among them, X m,k represents the motion model of target m at time k, F m represents the state transition matrix of the target, X m,k-1 represents the motion model of target m at time k-1, X n,k represents the motion model of airborne radar n at time k, F n represents the state transfer matrix of airborne radar n, X n,k-1 Represents the motion model of airborne radar n at time k-1.

[0018] Furthermore, the constructed detection task model is:

[0019]

[0020] in, represents the detection task model attributes, χ m Indicates the center position of the airspace that needs to be detected by the mth detection task, represents the type of the mth detection task, η mrepresents the weight of the mth detection task, representing the priority of the execution of this task, τ m Indicates the execution time of the mth detection task.

[0021] Furthermore, the multi-task echo signal-to-noise ratio measurement index is:

[0022]

[0023] in, Represents the multi-task signal-to-noise ratio measurement indicator, SNR n,m,k represents the echo signal-to-noise ratio received by radar node n when performing detection task m at time k, u n,m,k represents the task-radar node selection parameter, T r represents the radar pulse repetition period, P represents the radar transmission power, G t and G r Represents the transmit antenna and receive antenna gain, RCS m represents the radar scattering cross-section of the radar node relative to the target in the detection task m, λ represents the wavelength of the transmitted signal, G RP represents the receiver processing gain, k B represents the Boltzmann constant, T e represents the radar receiver noise temperature, B r represents the receiver matched filter bandwidth of each radar, F r Represents the noise figure of the receiver, R n,m,k Represents the distance between the spatial center position of detection task m and the position of radar node n at time k.

[0024] Furthermore, the measurement indicators of multi-task comprehensive execution performance are:

[0025]

[0026] in, represents the global utility function of multi-radar task allocation, u m,k =[u 1,m,k ,u 2,m,k ,…,u N,m,k ] T represents the allocation vector of each radar node to the detection task m at time k, represents the minimum distance between the spatial center position of the detection task m at time k and the position of each radar node, R m,k =[R 1.m,k ,R 2,m,k ,…,R N,m,k ] T represents the vector composed of the distances between all airborne radar nodes and the airspace center of the detection task m, η m represents the weight of the mth detection task.

[0027] Furthermore, the multi-radar cooperative detection planning model based on mission effectiveness is established as follows:

[0028]

[0029]

[0030] Among them, τ m represents the execution time of the mth detection task, represents the maximum available time resource defined by the radar, L represents the maximum number of beams generated by a single radar node, represents the echo signal-to-noise ratio of each radar node for detection task m, Indicates the current task type The echo signal-to-noise ratio requirement is M, M represents the total number of tasks, and N represents the total number of radars.

[0031] Furthermore, the interior point method is combined to solve the multi-radar cooperative detection planning model based on mission effectiveness, specifically:

[0032] (1) The optimization result of the weight coefficient matching between the detection task and the radar is solved by the interior point method, and the N×M matrix U 0 express;

[0033] (2) Put U 0 The largest first L elements of the M elements in each row are set to 1, and the remaining elements are set to 0 to generate the beam allocation matrix U 1 ;

[0034] (3) Put U 0 The largest element in each column of N elements is set to 1, and the remaining elements are set to 0 to generate the beam allocation matrix U 2 ;

[0035] (4) The matrix U 1 and U 2 Perform logical AND operation on all elements at corresponding positions to obtain the final task-radar node selection parameter optimization result;

[0036] (5) Perform secondary allocation on the unassigned tasks; for the elements corresponding to the unassigned tasks in the task matrix, select the maximum weight and reset it to 1 to determine whether the constraint conditions are met; if so, output the result u n,m,k , otherwise the element is set to 0 and this step is repeated until the result satisfies the constraint.

[0037] Furthermore, step (1) is specifically as follows:

[0038] i) Initialize parameters, given the initial point x (0) , initial slack variable s (0), the initial dual variable y (0) , damping coefficient μ, convergence threshold ε, set iteration index

[0039] ii) Calculate the residual r p =Ax (0) -b,r s =s (0) x (0) -μe; where r d represents the dual residual, r p represents the original residual, r s represents the central residual, Represents the objective function The gradient of A represents the constraint matrix, A T represents the transposed matrix of the constraint matrix A, b represents the constant term of the constraint condition, and e represents the all-1 vector;

[0040] iii) Calculate the Newtonian directions Δx, Δy, Δs and solve the following system of equations:

[0041]

[0042] Among them, Δx represents the variable The increment in the direction of The increment in the direction of The direction increment, Indicates The objective function at the iteration The second-order gradient derivative of Indicates The solution point at the iteration is Indicates The dual variable at the iteration, Indicates The slack variables at the iteration;

[0043] iv) Calculate the step size α through the line search algorithm and update the residual r d ;

[0044] v) Check convergence conditions: if they are met, the algorithm terminates, otherwise execute the next iteration: update the iteration index

[0045]

[0046] vi) Output the optimization result of the weight coefficient matching between the detection task and the radar, and a N×M U 0 express:

[0047]

[0048] in, It represents the weight coefficient of radar n performing detection task m, m=1,2,…,M, n=1,2,…,N, M represents the total number of tasks, and N represents the total number of radars.

[0049] The system corresponding to the method includes:

[0050] A motion model building unit, used to build motion models of the target and the airborne radar respectively;

[0051] The detection task model building unit is used to classify the multi-machine collaborative detection tasks into four types of tasks: target search, tracking, confirmation, and guidance, and construct the detection task model;

[0052] The indicator construction unit is used to construct a multi-task echo signal-to-noise ratio measurement indicator with the task-radar node selection parameter as the optimization variable; construct a multi-machine radar task allocation global utility function with the task-radar node selection parameter as the optimization variable as a measurement indicator of the comprehensive execution efficiency of multiple tasks;

[0053] The planning model building unit is used to establish a multi-radar cooperative detection planning model based on task effectiveness, taking the given maximum available time resources, echo signal-to-noise ratio and system performance limit of the multi-radar as constraints and maximizing the global utility function of the multi-radar cooperative detection task allocation as the optimization goal;

[0054] The planning model solving unit is used to solve the multi-radar cooperative detection planning model based on mission effectiveness in combination with the interior point method.

[0055] An electronic device for storing and executing the method, the device comprising:

[0056] A memory storing executable program code;

[0057] a processor coupled to the memory;

[0058] The processor calls the executable program code stored in the memory to execute the steps of the multi-aircraft radar collaborative detection planning method based on mission effectiveness.

[0059] Beneficial effects: Compared with the prior art, the significant technical effects of the present invention are: an evaluation index characterizing the comprehensive execution efficiency of multiple tasks is constructed, and at the same time, the task-radar node selection parameters of the multi-machine radar will also affect the index; on this basis, with the given maximum available time resources of the multi-machine radar, the echo signal-to-noise ratio and the system performance limitation as constraints, and with maximizing the global utility function of the collaborative detection task allocation of the multi-machine radar as the optimization goal, a multi-machine radar collaborative detection planning model based on task efficiency is established, and the task-radar node selection parameters of the multi-machine radar are adaptively optimized, thereby achieving the optimal optimization allocation of the task-radar node selection parameters of the multi-machine radar, and effectively improving the comprehensive execution efficiency of multiple tasks of the multi-machine radar. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 The flowchart of the multi-radar cooperative detection planning method based on mission effectiveness is provided;

[0061] Figure 2 It is the motion trajectory of multiple targets and the position distribution map of multiple radars;

[0062] Figure 3 Delineating the airspace search unit;

[0063] Figure 4 This is a schematic diagram of the task allocation result for radar 1;

[0064] Figure 5 This is a schematic diagram of the task allocation result for Radar 2;

[0065] Figure 6 This is a schematic diagram of the task allocation results for Radar 3;

[0066] Figure 7 This is a schematic diagram of the task allocation result of radar 4;

[0067] Figure 8 This is a schematic diagram of the task allocation result of radar 5;

[0068] Fig. 9 Schematic diagram of performance comparison results under different methods. DETAILED DESCRIPTION

[0069] The structure and working process of the present invention will be further described below in conjunction with the accompanying drawings.

[0070] Starting from the actual combat scenario, the present invention proposes a multi-radar collaborative detection planning method based on task effectiveness. Under the given constraints of the maximum available time resources, echo signal-to-noise ratio and system performance limit of the multi-radar collaborative detection, the global utility function of the multi-radar collaborative detection task allocation is maximized as the optimization goal, and the task-radar node selection parameters of the multi-radar are adaptively optimized to improve the multi-task comprehensive execution efficiency of the multi-radar.

[0071] like Figure 1 As shown, the method of the present invention comprises the following steps:

[0072] 1. Construct the motion models of target m and airborne radar n at time k respectively, as shown in equations (1) and (2):

[0073] X m,k =F m X m,k-1 (1)

[0074] X n,k =F n X n,k-1 (2)

[0075] Among them, X m,k represents the motion model of target m at time k, F m represents the state transition matrix of the target, X m,k-1 represents the motion model of target m at time k-1, X n,k represents the motion model of airborne radar n at time k, F n represents the state transfer matrix of airborne radar n, X n,k-1 Represents the motion model of airborne radar n at time k-1.

[0076] 2. Considering that the multi-machine collaborative detection tasks are classified into four types of tasks: target search, tracking, confirmation, and guidance, the detection task model is constructed as shown in formula (3):

[0077]

[0078] in, represents the detection task model attributes, χ m Indicates the center position of the airspace that needs to be detected by the mth detection task, represents the type of the mth detection task, η m represents the weight of the mth detection task, representing the priority of the execution of this task, τ m Indicates the execution time of the mth detection task.

[0079] The motion model constructed in step 1 and the four task models constructed in step 2 constitute the multi-radar multi-task collaborative detection scenario.

[0080] 3. Construct a multi-task echo signal-to-noise ratio measurement index with the task-radar node selection parameter as the optimization variable, as shown in formula (4):

[0081]

[0082] in, Represents the multi-task signal-to-noise ratio measurement indicator, SNR n,m,k represents the echo signal-to-noise ratio received by radar node n when performing detection task m at time k, u n,m,k represents the task-radar node selection parameter, T r represents the radar pulse repetition period, P represents the radar transmission power, G t and G r Represents the transmit antenna and receive antenna gain, RCS m represents the radar scattering cross-section of the radar node relative to the target in the detection task m, λ represents the wavelength of the transmitted signal, G RP represents the receiver processing gain, k B represents the Boltzmann constant, T e represents the radar receiver noise temperature, B r represents the receiver matched filter bandwidth of each radar, F r Represents the noise figure of the receiver, R n,m,k Represents the distance between the spatial center position of detection task m and the position of radar node n at time k.

[0083] 4. Construct a global utility function for multi-machine radar task allocation with task-radar node selection parameters as optimization variables as a measure of the comprehensive execution efficiency of multiple tasks, as shown in formula (5):

[0084]

[0085] in, represents the global utility function of multi-radar task allocation, u m,k =[u 1,m,k ,u 2,m,k ,…,u N,m,k ] T represents the allocation vector of each radar node to the detection task m at time k, R n,m,kmin represents the minimum distance between the spatial center position of the detection task m at time k and the position of each radar node, R m,k =[R 1.m,k ,R 2,m,k ,…,R N,m,k ] T represents the vector composed of the distances between all airborne radar nodes and the airspace center of the detection task m, η m represents the weight of the mth detection task.

[0086] 5. Establish a multi-radar collaborative detection planning model based on mission effectiveness;

[0087] Taking the given maximum available time resources, echo signal-to-noise ratio and system performance limit of multiple radars as constraints, and maximizing the global utility function of the multi-radar collaborative detection task allocation as the optimization goal, a multi-radar collaborative detection planning model based on task effectiveness is established, as shown in formula (6):

[0088]

[0089] Among them, τ m represents the execution time of the mth detection task, represents the maximum available time resource defined by the radar, L represents the maximum number of beams generated by a single radar node, represents the echo signal-to-noise ratio of each radar node for detection task m, Indicates the current task type The echo signal-to-noise ratio requirement is M, M represents the total number of tasks, and N represents the total number of radars.

[0090] The first constraint in the optimization model (6) represents the maximum available time resource limit of the airborne radar; the second constraint represents that each radar can generate at most L beams, which are used to perform different tasks; the third constraint represents that each task is only performed once; the fourth constraint represents the echo signal-to-noise ratio requirement for the airborne radar to perform the current task; the last constraint represents the allocation result u of the task-radar node selection parameter n,m,k is a binary variable.

[0091] 6. Combine the interior point method to solve the optimization model (6) in five steps:

[0092] Step 1: Use the interior point method to solve the optimization result of the weight coefficient matching between the detection task and the radar:

[0093] i) Initialize parameters, given the initial point x (0) , initial slack variable s (0) , the initial dual variable y (0) , damping coefficient μ, convergence threshold ε, set iteration index

[0094] ii) Calculate the residual r p =Ax (0) -b,r s =s (0) x (0) -μe. Among them, r d represents the dual residual, r p represents the original residual, r s represents the central residual, Represents the objective function The gradient of A represents the constraint matrix, A Trepresents the transposed matrix of the constraint matrix A, b represents the constant term of the constraint condition, and e represents the all-1 vector;

[0095] iii) Calculate the Newtonian directions Δx, Δy, Δs and solve the following system of equations:

[0096]

[0097] Among them, Δx represents the variable The increment in the direction of The increment in the direction of The direction increment, Indicates The objective function at the iteration The second-order gradient derivative of Indicates The solution point at the iteration is Indicates The dual variable at the iteration, Indicates The slack variables at iterations.

[0098] iv) Calculate the step size α through the line search algorithm and update the residual r d ;

[0099] v) Check convergence conditions: if they are met, the algorithm terminates, otherwise execute the next iteration: update the iteration index

[0100]

[0101] vi) Output the optimization result of the weight coefficient matching between the detection task and the radar, and a N×M U 0 express:

[0102]

[0103] in, It represents the weight coefficient of radar n performing detection task m, m=1,2,…,M, n=1,2,…,N, M represents the total number of tasks, and N represents the total number of radars.

[0104] Step 2: Connect the U 0 The largest first L elements of the M elements in each row are set to 1, and the remaining elements are set to 0 to generate the beam allocation matrix U 1 :

[0105]

[0106] in, represents the task-radar node selection parameter when only the second constraint is satisfied.

[0107] Step 3: Connect the U 0 The largest element in each column of N elements is set to 1, and the remaining elements are set to 0 to generate the beam allocation matrix U 2 :

[0108]

[0109] in, represents the task-radar node selection parameter when only the third constraint is satisfied.

[0110] Step 4: Transform the matrix U 1 and U 2 Perform logical AND operations on all elements at corresponding positions to obtain the final task-radar node selection parameter optimization result:

[0111]

[0112] Step 5: Perform secondary allocation on the unassigned tasks. For the elements of the task matrix corresponding to the unassigned tasks, select the maximum weight and reset it to 1 to determine whether the constraint conditions are met. If so, output the result u n,m,k , otherwise the element is set to 0 and this step is repeated until the result satisfies the constraint.

[0113] Simulation results:

[0114] In order to verify the feasibility and superiority of the method proposed in this chapter, the following simulation scenario is designed: Consider a radar network composed of N = 3 airborne radars. Each radar can generate up to L = 3 beams at the same time. The tasks can be divided into four types: search, tracking, confirmation, and guidance. There are 3 moving targets in the space that need to be tracked, confirmed, and guided respectively. The airspace can be divided into 9 search units, that is, a total of M = 18 tasks need to be performed. At all times, the RCS of each target relative to each radar is 1m 2 . The maximum available time resource defined by the radar Echo signal-to-noise ratio requirements for radar to perform four tasks: search, tracking, confirmation, and guidance They are 20, 40, 30 and 20 respectively. The execution time of the four tasks in each sampling is τ m They are 0.12s, 0.2s, 0.08s and 0.16s respectively. The weights η of the four task working modes on the task utility function are m They are 0.2, 0.3, 0.1 and 0.4 respectively. 30 consecutive frames of simulation data are used, and the sampling interval T0 = 1s.

[0115] The distribution of multiple radars and target motion trajectories are as follows: Figure 1 As shown, the initial target motion state parameters are shown in Table 1:

[0116] Table 1 Initial target motion state parameters

[0117]

[0118] Divide the search units and number them as follows Figure 2 As shown in the figure, the red five-pointed star represents the search center of the area. The above 18 tasks are numbered 1-18, where 1-9 represent searching for each search center, 10-12 represent tracking of each target, 13-15 represent confirmation of each target, and 16-18 represent guidance of each target.

[0119] Figures 4 to 8 The task allocation results of airborne radar are given respectively, which shows that the allocation results of task-radar node selection parameters in this scenario are relatively stable. Figure 2 and Figure 3 Analysis shows that at the beginning of the simulation, radar 1 and radar 2 start from search area 7, where radar 2 mainly searches for search areas 4, 5 and 8, which are relatively close. Radar 1 is farther away from these areas than radar 2, so it mainly searches nearby search areas 1, 2 and 7. In addition, as the simulation proceeds, the search benefit of radar 2 for search area 5 is less than that for task 16, that is, radar 2 tends to perform the guidance task for target 1. On the other hand, radars 3 to 5 are all located in the lower right corner of the airspace, where radar 5 is closest to search center 6 and has the highest benefit for searching it, so it mainly searches this area. Radar 4 is closer to search area 3, 5 and 9, so it searches these three areas. It is worth noting that although target 2 is closer to radar 3 than target 3, radar 4 has a higher benefit, so from the perspective of global benefit, radar 4 performs the confirmation task of target 2, and radar 3 performs the tracking and confirmation task of target 3.

[0120] In order to verify the superiority of the method proposed in this paper, this paper compares it with two other methods while keeping other parameters unchanged, namely:

[0121] Method 1: On the basis of keeping the radar parameters the same, the weight coefficients of the task-radar node allocation are evenly distributed, and the detection performance of the airborne radar is evaluated through the task effectiveness function;

[0122] Method 2: On the basis of keeping the radar parameters the same, the weight coefficients of the task-radar node assignment are randomly assigned, and the detection performance of the airborne radar is evaluated through the task effectiveness function.

[0123] Fig. 9The figure shows the comparison results of the methods. It can be seen from the figure that the objective function value of the proposed method is much larger than that of other methods, which proves that the method realizes efficient radar-task allocation and can significantly improve the collaborative detection efficiency of multiple radars.

[0124] The working principle and working process of the invention are as follows:

[0125] The present invention assumes that several targets are dispersedly deployed in space, and considers a multi-machine radar network with multiple transmission and multiple reception functions. At each measurement moment, each airborne radar performs multiple detection tasks for different targets at the same time. For this scenario of multi-machine radar multi-task collaborative detection, firstly, based on the task quality framework, the multi-task echo signal-to-noise ratio measurement index and the multi-machine radar task allocation global utility function are constructed respectively with the task-radar node selection parameter as the optimization variable as the measurement index of the multi-task comprehensive execution efficiency of the multi-machine radar. Then, with the given maximum available time resources of the multi-machine radar, the echo signal-to-noise ratio and the system performance limit as the constraint conditions, and with the maximization of the multi-machine radar collaborative detection task allocation global utility function as the optimization goal, a multi-machine radar collaborative detection planning model based on task efficiency is established. Finally, the interior point method is used to solve the optimization model. By solving the optimization model, the task-radar node selection parameter u that maximizes the comprehensive execution efficiency of the multi-task of the multi-machine radar under the given maximum available time resources of the multi-machine radar, the echo signal-to-noise ratio and the system performance limit as the constraint conditions is obtained. n,m,k The optimal solution for the model is obtained to achieve the purpose of improving the comprehensive execution performance of multi-tasks of multi-aircraft radars.

Claims

1. A multi-radar collaborative detection planning method based on mission effectiveness, characterized in that: The following steps are involved: Construct motion models of the target and airborne radar respectively; Considering that the multi-machine cooperative detection tasks are classified into four types of tasks: target search, tracking, confirmation, and guidance, a detection task model is constructed; Construct a multi-task echo signal-to-noise ratio measurement index with the task-radar node selection parameter as the optimization variable; A global utility function for multi-aircraft radar task allocation with task-radar node selection parameters as optimization variables is constructed as a measure of the comprehensive execution efficiency of multi-tasks. Taking the given maximum available time resources, echo signal-to-noise ratio and system performance limitations of multiple radars as constraints, and maximizing the global utility function of the multi-radar collaborative detection task allocation as the optimization goal, a multi-radar collaborative detection planning model based on task effectiveness is established. The interior point method is used to solve the multi-radar collaborative detection planning model based on mission effectiveness.

2. The multi-radar collaborative detection planning method based on mission effectiveness according to claim 1 is characterized in that: The motion model of the target and the airborne radar is: X m,k =F m X m,k-1 X n,k =F n X n,k-1 Among them, X m,k represents the motion model of target m at time k, F m represents the state transition matrix of the target, X m,k-1 represents the motion model of target m at time k-1, X n,k represents the motion model of airborne radar n at time k, F n represents the state transfer matrix of airborne radar n, X n,k-1 Represents the motion model of airborne radar n at time k-1.

3. The multi-radar collaborative detection planning method based on mission effectiveness according to claim 1 is characterized in that: The constructed detection task model is: in, represents the detection task model attributes, χ m Indicates the center position of the airspace that needs to be detected by the mth detection task, represents the type of the mth detection task, η m represents the weight of the mth detection task, representing the priority of the execution of this task, τ m Indicates the execution time of the mth detection task.

4. The multi-radar collaborative detection planning method based on mission effectiveness according to claim 1 is characterized in that: The multi-task echo signal-to-noise ratio measurement index is: in, Represents the multi-task signal-to-noise ratio measurement indicator, SNR n,m,k represents the echo signal-to-noise ratio received by radar node n when performing detection task m at time k, u n,m,k represents the task-radar node selection parameter, T r represents the radar pulse repetition period, P represents the radar transmission power, G t and G r Represents the transmit antenna and receive antenna gain, RCS m represents the radar scattering cross-section of the radar node relative to the target in the detection task m, λ represents the wavelength of the transmitted signal, G RP represents the receiver processing gain, k B represents the Boltzmann constant, T e represents the radar receiver noise temperature, B r represents the receiver matched filter bandwidth of each radar, F r Represents the noise figure of the receiver, R n,m,k Represents the distance between the spatial center position of detection task m and the position of radar node n at time k.

5. The multi-radar collaborative detection planning method based on mission effectiveness according to claim 1 is characterized in that: The measurement indicators of multi-task comprehensive execution performance are: in, represents the global utility function of multi-radar task allocation, u m,k =[u 1,m,k ,u 2,m,k ,…,u N,m,k ] T represents the allocation vector of each radar node to the detection task m at time k, R n,m,kmin represents the minimum distance between the spatial center position of the detection task m at time k and the position of each radar node, R m,k =[R 1.m,k ,R 2,m,k ,…,R N,m,k ] T represents the vector composed of the distances between all airborne radar nodes and the airspace center of the detection task m, η m represents the weight of the mth detection task.

6. The multi-radar collaborative detection planning method based on mission effectiveness according to claim 1 is characterized in that: The multi-radar cooperative detection planning model based on mission effectiveness is established as follows: Among them, τ m represents the execution time of the mth detection task, t nmax represents the maximum available time resource defined by the radar, L represents the maximum number of beams generated by a single radar node, represents the echo signal-to-noise ratio of each radar node for detection task m, Indicates the current task type The echo signal-to-noise ratio requirement is M, M represents the total number of tasks, and N represents the total number of radars.

7. The multi-radar collaborative detection planning method based on mission effectiveness according to claim 1 is characterized in that: The interior point method is used to solve the multi-radar cooperative detection planning model based on mission effectiveness, specifically: (1) The optimization result of the weight coefficient matching between the detection task and the radar is solved by the interior point method, and the N×M matrix U 0 express; (2) Put U 0 The largest first L elements of the M elements in each row are set to 1, and the remaining elements are set to 0 to generate the beam allocation matrix U 1 ; (3) Put U 0 The largest element in each column of N elements is set to 1, and the remaining elements are set to 0 to generate the beam allocation matrix U 2 ; (4) The matrix U 1 and U 2 Perform logical AND operation on all elements at corresponding positions to obtain the final task-radar node selection parameter optimization result; (5) Perform secondary allocation on the unassigned tasks; for the elements corresponding to the unassigned tasks in the task matrix, select the maximum weight and reset it to 1 to determine whether the constraint conditions are met; if so, output the result u n,m,k , otherwise the element is set to 0 and this step is repeated until the result satisfies the constraint.

8. The multi-radar collaborative detection planning method based on mission effectiveness according to claim 7 is characterized in that: Step (1) is specifically as follows: i) Initialize parameters, given the initial point x (0) , initial slack variable s (0) , the initial dual variable y (0) , damping coefficient μ, convergence threshold ε, set iteration index ii) Calculate the residual r p =Ax (0) -b,r s =s (0) x (0) -μe; Among them, r d represents the dual residual, r p represents the original residual, r s represents the central residual, Represents the objective function The gradient of A represents the constraint matrix, A T represents the transposed matrix of the constraint matrix A, b represents the constant term of the constraint condition, and e represents the all-1 vector; iii) Calculate the Newtonian directions Δx, Δy, Δs and solve the following system of equations: Among them, Δx represents the variable The increment in the direction of The increment in the direction of The direction increment, Indicates The objective function at the iteration The second-order gradient derivative of Indicates The solution point at the iteration is Indicates The dual variable at the iteration, Indicates The slack variables at the iteration; iv) Calculate the step size α through the line search algorithm and update the residual r d ; v) Check convergence conditions: if they are met, the algorithm terminates, otherwise execute the next iteration: update the iteration index vi) Output the optimization result of the weight coefficient matching between the detection task and the radar, and a N×M U 0 express: in, It represents the weight coefficient of radar n performing detection task m, m=1,2,…,M, n=1,2,…,N, M represents the total number of tasks, and N represents the total number of radars.

9. The multi-radar cooperative detection planning system based on mission effectiveness is characterized by: include: A motion model building unit, used to build motion models of the target and the airborne radar respectively; The detection task model building unit is used to classify the multi-machine collaborative detection tasks into four types of tasks: target search, tracking, confirmation, and guidance, and construct the detection task model; The indicator construction unit is used to construct a multi-task echo signal-to-noise ratio measurement indicator with the task-radar node selection parameter as the optimization variable; construct a multi-machine radar task allocation global utility function with the task-radar node selection parameter as the optimization variable as a measurement indicator of the comprehensive execution efficiency of multiple tasks; The planning model building unit is used to establish a multi-radar cooperative detection planning model based on task effectiveness, taking the given maximum available time resources, echo signal-to-noise ratio and system performance limit of the multi-radar as constraints and maximizing the global utility function of the multi-radar cooperative detection task allocation as the optimization goal; The planning model solving unit is used to solve the multi-radar cooperative detection planning model based on mission effectiveness in combination with the interior point method.

10. An electronic device, characterized in that: The device comprises: A memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the steps of the multi-aircraft radar collaborative detection planning method based on mission effectiveness as described in any one of claims 1-8.

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