Task performance based multi-radar cooperative detection planning method

Abstract

The application discloses a kind of multi-machine radar cooperative detection planning methods based on task performance, comprising: the motion model of target and airborne radar is respectively constructed, and detection task model is constructed;Multiple task echo signal-to-noise ratio measurement index is constructed with task-radar node selection parameter as optimization variable;Multiple machine radar task allocation global utility function is constructed with task-radar node selection parameter as optimization variable as the measurement index of multiple task comprehensive execution performance;With the maximum available time resource of given multi-machine radar, echo signal-to-noise ratio and system performance limit as constraint condition, with the maximum multi-machine radar cooperative detection task allocation global utility function as optimization goal, establish multi-machine radar cooperative detection planning model based on task performance;The planning model is solved in combination with interior point method.The application realizes the optimal multi-machine radar task-radar node selection parameter optimization distribution, effectively improves the multi-task comprehensive execution performance of multi-machine radar.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of radar signal processing, and particularly relates to a multi-radar cooperative detection planning method based on task performance. BACKGROUND

[0002] In a complex battlefield environment, a single base radar is difficult to achieve flexible radiation resource management and control due to a small detection range, and the role and function that can be played are very limited. Compared with the traditional single base radar, the multi-radar network can fully mine target feature information from different frequency bands, different space-times, and different polarization modes, and can fully obtain the battlefield environment situation, can efficiently perform multiple tasks, suppress interference, effectively counter the dense clutter environment, and have stronger survivability in a complex electromagnetic scene, and has significant advantages in target search, tracking, confirmation, guidance and other applications.

[0003] In practical applications, due to the gradual diversification of task requirements in the battlefield, multi-task cooperation of radars is also of great significance. With the emergence and gradual maturity of phased array, frequency controlled array, digital array and other radar systems, the multi-radar network can realize different detection functions by adjusting its working mode and working parameters to complete search, tracking, confirmation, guidance and other diversified combat tasks. Therefore, the multi-radar has important application prospects.

[0004] At present, multi-task dynamic planning based on task performance has become a research hotspot, and many scholars at home and abroad have made remarkable achievements in this field. However, most of the existing researches are based on single radar node task scenarios, and there are few related researches on cooperative detection task allocation for general multi-radar and multi-task scenarios. In the multi-radar cooperative detection scenario based on task performance, the task dynamic planning method needs to adaptively optimize the detection task-radar node selection parameters under the condition of meeting the multi-task performance constraints, and the corresponding mathematical model is more complex, and the dimension of the optimization parameter also increases. It is a more complex and challenging problem. Therefore, from the perspective of improving the multi-task comprehensive execution performance of the multi-radar, it is of great significance to carry out research on the multi-radar cooperative detection planning method based on task performance.

[0005] In summary, there is no multi-radar cooperative detection planning method based on task performance in the prior art. SUMMARY

[0006] The purpose of the present application is to provide a multi-radar cooperative detection planning method capable of improving the multi-task comprehensive execution performance of the multi-radar.

[0007] Technical scheme: The multi-radar cooperative detection planning method based on task performance provided by the present application comprises the following steps:

[0008] The motion models of the target and the airborne radar are constructed respectively;

[0009] The detection task model is constructed by considering the multi-aircraft cooperative detection task as four types of tasks, i.e., target search, tracking, confirmation and guidance;

[0010] The multi-task echo signal-to-noise ratio measurement index is constructed with the task-radar node selection parameters as optimization variables;

[0011] The multi-aircraft radar task allocation global utility function is constructed with the task-radar node selection parameters as optimization variables, and is used as a measurement index of the multi-task comprehensive execution performance;

[0012] The multi-aircraft radar cooperative detection planning model based on task performance is established by taking the maximum available time resource, echo signal-to-noise ratio and system performance limit of the multi-aircraft radar as constraint conditions, and taking the maximization of the multi-aircraft radar cooperative detection task allocation global utility function as an optimization objective;

[0013] The multi-aircraft radar cooperative detection planning model based on task performance is solved by combining the interior point method.

[0014] Further, the motion models of the target and the airborne radar are as follows:

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

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

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

[0018] Further, the detection task model is constructed as follows:

[0019]

[0020] wherein, represents the detection task model attribute, χ m represents the spatial center position needed to be detected by the mth detection task, represents the type of the mth detection task, and η mdenotes the weight of the mth detection task, representing the priority of the task execution, τ m denotes the execution time of the mth detection task.

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

[0022]

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

[0024] Further, the measurement index of the multi-task comprehensive execution performance is:

[0025]

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

[0027] Further, the task performance-based multi-radar cooperative detection planning model established is:

[0028]

[0029]

[0030] wherein 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 to the detection task m, represents the echo signal-to-noise ratio requirement of the current task type , M represents the total number of tasks, and N represents the total number of radars.

[0031] Further, the task performance-based multi-radar cooperative detection planning model is solved by combining the interior point method, specifically:

[0032] (1) The weight coefficient optimization result of the matching of the detection task and the radar is solved by the interior point method, and is represented by an N x M matrix U 0 .

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

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

[0035] (4) The elements of all corresponding positions of the matrix U 1 and U 2 are logically ANDed to obtain the final task-radar node selection parameter optimization result;

[0036] (5) Secondary allocation is performed on the unallocated tasks; for the elements corresponding to the unallocated tasks in the task matrix, the maximum weight is set to 1, and it is judged whether the constraint condition is satisfied; if the constraint condition is satisfied, the result u n,m,k is output, otherwise the element is set to 0, and the step is repeated until the result satisfies the constraint condition.

[0037] Further, step (1) is specifically:

[0038] i) initialize the parameters, give the initial point x (0) , and the initial relaxation variable s (0), 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; wherein r d represents the dual residual, r p represents the original residual, r s represents the central residual, represents the gradient of the objective function , A represents the constraint matrix, A T represents the transpose 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 Newton direction Δx, Δy, Δs, solve the following system of equations:

[0041]

[0042] where Δx represents the directional increment of the variable , Δy represents the directional increment of the variable , Δs represents the directional increment of the variable , represents the second-order gradient derivative of the objective function under the th iteration, represents the solution point under the th iteration, represents the dual variable under the th iteration, represents the relaxation variable under the th iteration.

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

[0044] v) Check the convergence condition: if it is satisfied, the algorithm terminates, otherwise perform the next iteration: update the iteration index

[0045]

[0046] vi) Output the weight coefficient optimization result of the probe task matched with the radar, which is represented by an N×M U 0 :

[0047] ​

[0048] wherein, represents a 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 method corresponds to a system, comprising:

[0050] A motion model construction unit is configured to construct motion models of the target and the airborne radars respectively.

[0051] A detection task model construction unit is configured to consider classifying the multi-aircraft cooperative detection task into four types of tasks, i.e., target search, tracking, confirmation, and guidance, and construct a detection task model.

[0052] An index construction unit is configured to construct a multi-task echo signal-to-noise ratio measurement index with the task-radar node selection parameters as optimization variables, and construct a multi-aircraft radar task allocation global utility function as a measurement index of multi-task comprehensive execution performance with the task-radar node selection parameters as optimization variables.

[0053] A planning model construction unit is configured to establish a multi-aircraft radar cooperative detection planning model based on task performance with the given maximum available time resource, echo signal-to-noise ratio, and system performance limit as constraint conditions, and maximize the multi-aircraft radar cooperative detection task allocation global utility function as an optimization target.

[0054] A planning model solution unit is configured to solve the multi-aircraft radar cooperative detection planning model based on task performance by combining an interior point method.

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

[0056] A memory storing executable program codes;

[0057] A processor coupled with the memory;

[0058] The processor invokes the executable program codes stored in the memory to execute the steps of the multi-aircraft radar cooperative detection planning method based on task performance.

[0059] Beneficial effects: compared with the prior art, the significant technical effects of the present application are: an evaluation index characterizing the multi-task comprehensive execution efficiency is constructed, and the task-radar node selection parameters of the multi-radar also affect the index; on this basis, under the constraint conditions of the given maximum available time resources, echo signal-to-noise ratio and system performance limit of the multi-radar, the global utility function of the multi-radar cooperative detection task allocation is maximized as the optimization target, the multi-radar cooperative detection planning model based on task efficiency is established, the optimization allocation of the multi-radar task-radar node selection parameters is adaptively carried out, and the optimal optimization allocation of the multi-radar task-radar node selection parameters is realized, and the multi-task comprehensive execution efficiency of the multi-radar is effectively improved. BRIEF DESCRIPTION OF DRAWINGS

[0060] Figure 1 It is a multi-radar cooperative detection planning method flow chart based on task efficiency;

[0061] Figure 2 It is a multi-target motion trajectory and multi-radar position distribution map;

[0062] Figure 3 It is a space search unit division map;

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

[0064] Figure 5 It is a task allocation result schematic diagram of radar 2;

[0065] Figure 6 It is a task allocation result schematic diagram of radar 3;

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

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

[0068] Figure 9 It is a performance comparison result schematic diagram under different methods. DETAILED DESCRIPTION

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

[0070] The present application proposes a multi-radar cooperative detection planning method based on task efficiency from the actual combat scene, and under the constraint conditions of the given maximum available time resources, echo signal-to-noise ratio and system performance limit of the multi-radar, the global utility function of the multi-radar cooperative detection task allocation is maximized as the optimization target, the task-radar node selection parameters of the multi-radar are adaptively optimized and designed, and the multi-task comprehensive execution efficiency of the multi-radar is improved.

[0071] As Figure 1 shown, the method of the application comprises the following steps:

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

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

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

[0075] Wherein, 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 transition 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-aircraft cooperative detection task is classified into four types of tasks of target search, tracking, confirmation and guidance, construct the detection task model, as shown in formula (3):

[0077]

[0078] Wherein, represents the attribute of the detection task model, χ m represents the center position of the airspace 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 the task, τ m represents 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 a multi-aircraft radar multi-task cooperative detection scene.

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

[0081]

[0082] Wherein, denotes the multi-task SNR measure, SNR n,m,k denotes the received echo SNR of radar node n performing detection task m at time k, u n,m,k denotes the task-radar node selection parameter, T r denotes the radar pulse repetition period, P denotes the radar transmit power, G t and G r denotes the transmit antenna and receive antenna gain, respectively, RCS m denotes the radar node's radar cross section relative to the target in detection task m, λ denotes the transmit signal wavelength, G RP denotes the receiver processing gain, k B denotes the Boltzmann constant, T e denotes the radar receiver noise temperature, B r denotes the receiver matched filter bandwidth of each radar, F r denotes the receiver noise figure, R n,m,k denotes the distance between the spatial center of detection task m at time k and the location of radar node n.

[0083] 4. Constructing a multi-aircraft radar task allocation global utility function with the task-radar node selection parameter as the optimization variable as the measure of the multi-task comprehensive execution performance, as shown in equation (5):

[0084]

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

[0086] 5. Establishing a multi-aircraft radar cooperative detection planning model based on task performance;

[0087] A multi-radar cooperative detection planning model based on task performance is established, as shown in equation (6), with the maximum available time resource of the given multi-radar, the echo signal-to-noise ratio and the system performance limit as constraint conditions, and the maximization of the global utility function of the multi-radar cooperative detection task allocation as the optimization objective.

[0088]

[0089] wherein 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 to the detection task m, represents the echo signal-to-noise ratio requirement of the current task type , 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 execute different tasks; the third constraint represents that each task is executed only once; the fourth constraint represents the echo signal-to-noise ratio requirement of the airborne radar executing the current task; and the last constraint represents the allocation result u n,m,k of the task-radar node selection parameter.

[0091] 6. The optimization model (6) is solved in five steps by combining the interior point method:

[0092] Step 1: The weight coefficient optimization result of the matching of the detection task and the radar is solved by the interior point method:

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

[0094] ii) Calculate the residual r p = Ax (0) -b, r s =s (0) x (0) -μe. Wherein r d represents the dual residual, r p represents the original residual, r s represents the central residual, represents the gradient of the objective function , A represents the constraint matrix, and A Tdenotes the transpose of the constraint matrix A, b denotes the constant term of the constraint condition, and e denotes an all-one vector;

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

[0096]

[0097] where Δx denotes the directional increment of the variable , Δy denotes the directional increment of the variable , Δs denotes the directional increment of the variable , g denotes the second-order gradient derivative of the objective function under the i-th iteration, denotes the solution point under the i-th iteration, denotes the dual variable under the i-th iteration, denotes the relaxation variable under the i-th iteration.

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

[0099] v) Check the convergence condition: if it is satisfied, the algorithm terminates, otherwise perform the next iteration: update the iteration index

[0100]

[0101] vi) Output the weight coefficient optimization result of the detection task matching the radar, which is represented by an N×M U 0 :

[0102]

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

[0104] Step 2: Set the first L largest elements in each row of U 0 to 1 and the remaining elements to 0 to generate the beam allocation matrix U 1 :

[0105]

[0106] wherein, denotes the task-radar node selection parameter only satisfying the second constraint condition. ​​​​​

[0107] Step 3: Put U 0 In each column of N elements, the largest element is set to 1, and the remaining elements are set to 0, generating the beam assignment matrix U. 2 :

[0108]

[0109] in, This represents the task-radar node selection parameters when only the third constraint condition is met.

[0110] Step 4: Convert matrix U 1 and U 2 Perform a logical AND operation on all corresponding elements to obtain the final optimized parameters for the task-radar node selection:

[0111]

[0112] Step 5: Perform secondary allocation for unassigned tasks. For each element corresponding to an unassigned task in the task matrix, select the element with the largest weight and reset it to 1. Check if the constraints are met. If they are met, output the result u. n,m,k Otherwise, set the element to 0 and repeat this step until the result satisfies the constraints.

[0113] Simulation results:

[0114] To verify the feasibility and superiority of the method proposed in this chapter, the following simulation scenario is designed: Consider a radar network consisting of N=3 airborne radars, each capable of simultaneously generating a maximum of L=3 beams. The tasks performed can be categorized into four types: search, track, confirmation, and guidance. There are three moving targets in space that require tracking, confirmation, and guidance tasks respectively. The airspace can be divided into nine search units, meaning 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 resources defined by radar. Echo signal-to-noise ratio requirements for radar performing four tasks: search, track, confirmation, and guidance. The time τ for each of the four tasks is 20, 40, 30, and 20 respectively, and the execution time τ for each sampling is τ. m The weights η of the four task working modes on the task utility function are 0.12s, 0.2s, 0.08s, and 0.16s, respectively. m The values ​​are 0.2, 0.3, 0.1, and 0.4, respectively. Simulation data from 30 consecutive frames was used, with a sampling interval T0 = 1 second.

[0115] The multi-aircraft radar distribution and target trajectory settings are as follows: Figure 1 As shown in Table 1, the initial target motion state parameters are as follows:

[0116] Table 1 initial target motion state parameters

[0117]

[0118] The division of the search units and the numbering are shown in Fig. 1, in which the red pentagram represents the search center of the area. The above-mentioned 18 tasks are numbered as 1-18, in which 1-9 represent searching for each search center, 10-12 represent tracking each target, 13-15 represent confirming each target, and 16-18 represent guiding each target. Figure 2

[0119] The task allocation results of the airborne radars are given respectively, and it can be seen that the allocation results of the task-radar node selection parameters are relatively stable in this scenario. In combination with the analysis of Figures 4 to 8 and Figure 2 , it can be seen that at the beginning of the simulation, radar 1 and radar 2 start from search area 7, in which radar 2 mainly performs search tasks on search area 4, search area 5 and search area 8 which are relatively close, while radar 1 is farther away from these areas than radar 2, so it mainly performs search tasks on search area 1, search area 2 and search area 7 which are relatively close. In addition, as the simulation proceeds, the search benefit of radar 2 on search area 5 is less than performing task 16, that is, radar 2 is inclined to perform the guidance task for target 1. On the other hand, radar 3 to radar 5 are all located in the lower right of the airspace, in which radar 5 is closest to search center 6, so it mainly performs search on this area. Radar 4 is relatively close to search area 3, search area 5 and search area 9, so it performs search on these three areas. It is worth noting that although target 2 is closer to radar 3 than target 3, since radar 4 has higher benefit, from the global benefit, radar 4 performs the confirmation task for target 2, and radar 3 performs the tracking and confirmation tasks for target 3. Figure 3 In order to verify the superiority of the method proposed in this paper, under the premise of keeping other parameters unchanged, this paper compares it with other two methods, which are:

[0120] Method 1: On the basis of keeping the same radar parameters, the weight coefficients of the task-radar node allocation are evenly distributed, and the detection performance of the airborne radar is evaluated by the task effectiveness function;

[0121] Method 2: On the basis of keeping the same radar parameters, the weight coefficients of the task-radar node allocation are randomly distributed, and the detection performance of the airborne radar is evaluated by the task effectiveness function.

[0122]

[0123] Figure 9 ​The method comparison results are shown, and it can be seen from the figure that the objective function value of the method is much larger than that of other methods, proving that the method realizes efficient allocation of radar-tasks and can significantly improve the cooperative detection efficiency of multi-radar.

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

[0125] The present application assumes that several targets are scattered in space, and considers a multi-radar network with multi-transmission and multi-reception functions. At each measurement time, each airborne radar simultaneously performs multiple detection tasks on different targets. For this multi-radar multi-task cooperative detection scenario, first, based on the task quality framework, the multi-task echo signal-to-noise ratio measurement index is constructed with the task-radar node selection parameter as the optimization variable, and the multi-radar task allocation global utility function is constructed as the measurement index of the multi-radar multi-task comprehensive execution efficiency. Then, taking the given maximum available time resource of the multi-radar, the echo signal-to-noise ratio and the system performance limit as the constraint condition, and taking the maximization of the multi-radar cooperative detection task allocation global utility function as the optimization target, a multi-radar cooperative detection planning model based on task efficiency is established. Finally, the interior point method is used to solve the optimization model. Through solving the optimization model, the task-radar node selection parameter u n,m,k is obtained, which is the optimal solution of the model, so as to achieve the purpose of improving the multi-radar multi-task comprehensive execution efficiency.

Claims

1. A method for task performance-based multi-radar cooperative detection planning, characterized in that, The method comprises the following steps: motion models of the target and the airborne radar are respectively constructed; a detection task model is constructed by classifying the multi-aircraft cooperative detection task into four types of tasks, i.e., target search, tracking, confirmation and guidance; a multi-task echo signal-to-noise ratio measurement index is constructed with the task-radar node selection parameters as optimization variables; a multi-task comprehensive execution performance measurement index is constructed with the task-radar node selection parameters as optimization variables; a multi-aircraft radar cooperative detection planning model based on task performance is established by taking the maximum available time resource, echo signal-to-noise ratio and system performance limit of the multi-aircraft radar as constraint conditions and maximizing the multi-aircraft radar cooperative detection task allocation global utility function as an optimization target; the multi-aircraft radar cooperative detection planning model based on task performance is solved by combining an interior point method.

2. The task performance based multi-radar cooperative detection planning method according to claim 1, characterized in that, The motion models of the target and the airborne radar are as follows: ； ； in, express Momentary Goal The motion model, The state transition matrix represents the target. express Momentary Goal The motion model, express Airborne radar The motion model, Indicates airborne radar The state transition matrix, express Airborne radar The motion model.

3. The method of claim 1, wherein, The detection task model is as follows: ； wherein, represents a detection task model attribute, represents a center position of airspace needed to be detected by the th detection task, represents a type of the th detection task, represents a weight of the th detection task, representing a priority of execution of the task, represents an execution time consumption of the th detection task.

4. The task performance based multi-radar cooperative detection planning method according to claim 1, characterized in that, The multi-task echo signal-to-noise ratio measurement index is as follows: ； wherein denotes a multi-task signal-to-noise ratio measure, denotes a time instant radar node executing a probing task a received echo signal-to-noise ratio, denotes a task-radar node selection parameter, denotes an execution duration of the first probing task, denotes a radar pulse repetition period, denotes a radar transmit power, and denote a transmit antenna and a receive antenna gain, respectively, denotes a radar cross section of a target in a probing task with respect to a radar node, denotes a transmit signal wavelength, denotes a receiver processing gain, denotes a Boltzmann constant, denotes a radar receiver noise temperature, denotes a receiver matched filter bandwidth of each radar, denotes a receiver noise figure, denotes a distance between a spatial center position of a probing task at a time instant and a radar node position.

5. The method of claim 1, wherein, The multi-task comprehensive execution performance measurement index is as follows: ； wherein, represents the global utility function of multi-ship radar task allocation, represents represents the allocation vector of each radar node to the detection task at time , represents represents the minimum distance between the spatial center position of the detection task at time and the position of each radar node, represents the vector composed of the distance between all airborne radar nodes and the spatial center of the detection task , represents the weight of the th detection task.

6. The method of claim 1, wherein, The multi-aircraft radar cooperative detection planning model based on task performance is as follows: ； wherein, represents a global utility function of multi-radar task allocation, represents represents an allocation vector of each radar node to the detection task at time instant, represents a task-radar node selection parameter, represents an execution time consumption of the th detection task, represents a maximum available time resource defined by radar, represents a maximum number of beams generated by a single radar node, represents a return signal-to-noise ratio of each radar node to the detection task , and represents a return signal-to-noise ratio requirement of the current task type , and represents a total number of tasks, represents a total number of radars.

7. The method of claim 1, wherein, The multi-aircraft radar cooperative detection planning model based on task performance is solved by combining an interior point method, specifically as follows: (1) The weight coefficient optimization result of the detection task and the radar is solved by the interior point method, and is represented by a matrix , , represents the total number of tasks, represents the total number of radars; (2) All industries The largest among the elements Set one element to 1 and the rest to 0 to generate the beam assignment matrix. ; (3) set each column of the maximum element in the element , the remaining elements are set to , generate beam allocation matrix ; (4) performing logical AND operation on elements of all corresponding positions of the matrix and to obtain the final task-radar node selection parameter optimization result; (5) performing secondary allocation on the unallocated tasks; For the elements of the task matrix that are not assigned a task, the maximum weight is selected as 1, and it is judged whether the constraint condition is met; if it is met, the result is output , indicates the task-radar node selection parameter, otherwise the element is set to 0, and this step is repeated until the result meets the constraint condition.

8. The task performance based multi-radar cooperative detection planning method according to claim 7, characterized in that, Step (1) is specifically as follows: i) initialize parameters, given initial point , initial slack variable , initial dual variable , damping coefficient , convergence threshold , set iteration index ; ii) Calculate the residuals , , ;in, Represents the dual residual. Represents the original residual. Indicates the central residual. Describe the objective function gradient, This represents the global utility function for multi-aircraft radar task allocation. express Each radar node is constantly monitoring the detection mission. The assignment vector, Represents the constraint matrix. Represents the constraint matrix The transpose of the matrix, Represents the constant term of the constraint condition. Represents a vector consisting entirely of 1s; iii) Compute the Newton direction , solve the following system of equations: ； in, Representing variables directional increment, Representing variables directional increment, Representing variables directional increment, Indicates the first The objective function in the next iteration The second-order gradient derivative, Indicates the first Solution point in the next iteration Indicates the first Dual variable in the next iteration Indicates the first Slack variables in the next iteration; iv) Calculate step size by line search algorithm and update the residual ; v) check convergence condition: if satisfied then the algorithm terminates, otherwise perform next iteration: update iteration index ; vi) outputting the weight coefficient optimization result matched with the radar of the detection task, and outputting the weight coefficient optimization result matched with the radar of the detection task by one of indicates: ； wherein representing a radar performing a probing task weight coefficients, , , representing a total number of tasks, representing a total number of radars.

9. A multi-radar cooperative detection planning system based on mission effectiveness, characterized in that, The method comprises the following steps: a motion model construction unit, configured to construct motion models of the target and the airborne radar respectively; a detection task model construction unit, configured to construct a detection task model by classifying the multi-aircraft cooperative detection task into four types of tasks, i.e., target search, tracking, confirmation and guidance; an index construction unit, configured to construct a multi-task echo signal-to-noise ratio measurement index with the task-radar node selection parameters as optimization variables, and construct a multi-task comprehensive execution performance measurement index with the task-radar node selection parameters as optimization variables; a planning model construction unit, configured to establish a multi-aircraft radar cooperative detection planning model based on task performance by taking the maximum available time resource, echo signal-to-noise ratio and system performance limit of the multi-aircraft radar as constraint conditions and maximizing the multi-aircraft radar cooperative detection task allocation global utility function as an optimization target; a planning model solving unit, configured to solve the multi-aircraft radar cooperative detection planning model based on task performance by combining an interior point method.

10. An electronic device, comprising: The device comprises: a memory storing executable program codes; a processor coupled with the memory; the processor invokes the executable program codes stored in the memory to execute the steps of the multi-aircraft radar cooperative detection planning method based on task performance according to any one of claims 1-8.

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