A resource scheduling management method for phased array radar based on greedy algorithm

Abstract

The present invention discloses a method for resource scheduling and management of a phased array radar based on a greedy algorithm, the method comprising: firstly constructing a phased array radar work task model, weighting the various parameters constituting the phased array radar work task model, obtaining a weighted coefficient and a priority function for a specific task; then using the greedy algorithm to sort priorities, outputting a task priority sequence, and executing the target search and tracking work tasks of the phased array radar, obtaining a target parameter matrix, feeding back to the task parameters, and then using the greedy algorithm to optimize and update the task priority sequence, and performing the next target search and tracking work tasks of the phased array radar; after the phased array radar works for a specific time, analyzing the resource utilization of the phased array radar, if the expected effect is not met, adjusting the task parameters and the working parameters, and using the greedy algorithm again to sort the task priorities. The present invention improves the time utilization and energy utilization of the phased array radar.

Description

Technical Field

[0001] The present invention relates to the technical field of phased array radar resource management, and in particular to a phased array radar resource scheduling management method based on a greedy algorithm. Background Art

[0002] In recent years, phased array radar technology has been increasingly used, and its application scenarios have also increased. Phased array radar resource scheduling management is to schedule radar resources in real time according to task requirements within the mission range specified by the radar, and to achieve the purpose of completing various practical tasks by allocating reasonable time and energy to maximize radar performance under resource constraints.

[0003] Domestic and foreign scholars have conducted a lot of research on the optimization of phased array radar resource scheduling and achieved many results. In the early days, template scheduling was widely used due to the constraints of radar design hardware and software conditions. Template scheduling is divided into fixed templates, partial templates and multiple templates, which have the advantages of simple methods, simple engineering implementation and low resource occupation, but also have disadvantages such as low scheduling efficiency, poor adaptability and low resource utilization. With the improvement of computer processing power and the continuous improvement of radar task requirements, adaptive scheduling methods have gradually replaced template scheduling methods and become the mainstream trend of phased array radar resource scheduling design.

[0004] Phased array radar resource management is the prerequisite for fully utilizing the performance of the radar system. In particular, when the battlefield environment faced by the radar is very complex and the targets are dense, the adaptive radar resource management strategy is of great significance. The adaptive scheduling method is a scheduling method that selects the optimal working sequence for the radar by evaluating radar energy, time and other resources in real time under radar constraints, and after comprehensive balance optimization. There are many existing adaptive resource scheduling methods, such as risk cost-based methods, information theory-based methods, task comprehensive planning-based methods, and time window-based methods. In different working scenarios, different resource scheduling methods can play their respective advantages, but a single resource scheduling method is also difficult to meet the challenges of task diversity and environmental complexity faced by phased array radars. Summary of the invention

[0005] The purpose of the present invention is to propose a phased array radar resource scheduling management method based on a greedy algorithm, which has high time utilization and energy utilization, strong task execution capability, and can meet the needs of task diversity and environmental complexity.

[0006] The technical solution to achieve the purpose of the present invention is: a phased array radar resource scheduling management method based on a greedy algorithm, comprising the following steps:

[0007] Step 1: Analyze the dynamic background, target quantity, and target characteristics of the phased array radar, and build a phased array radar task model;

[0008] Step 2: According to the target parameters in the airspace and the phased array radar observation model, weight the various task parameters constituting the phased array radar working task model, determine the weighting coefficient, and obtain the priority function for the set task;

[0009] Step 3: Use the greedy algorithm to sort the priorities, output the task priority sequence, and execute the work tasks of the phased array radar including target search and tracking;

[0010] Step 4: Complete the phased array radar task, obtain the target parameter matrix, and feed it back to the task parameters of the phased array radar task model. Use the greedy algorithm again to optimize and update the task priority sequence, and perform the next task of the phased array radar. Repeat step 4 until the phased array radar reaches the set time, and then go to step 5.

[0011] Step 5: Analyze the resource utilization of the phased array radar and make a judgment according to the resource utilization threshold. If the expected effect is not met, adjust the task parameters and working parameters, use the greedy algorithm to sort the task priorities again, and return to step 4. If the expected effect is met, do not adjust the task parameters and working parameters, and directly return to step 4.

[0012] A mobile terminal comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the phased array radar resource scheduling management method when executing the program.

[0013] A computer-readable storage medium having a computer program stored thereon, characterized in that when the program is executed by a processor, the steps in the phased array radar resource scheduling and management method are implemented.

[0014] Compared with the prior art, the present invention has the following significant advantages: (1) During the operation of the phased array radar, a work task model close to the actual working environment can be constructed based on factors such as the radar working background, the number of targets, and the characteristics of the targets, thereby improving the environmental adaptability of the phased array radar; (2) During the resource scheduling of the phased array radar, a priority function can be created based on the work task model through weighted processing, the task sequence sorting problem can be converted into a function solving problem, and the optimal solution can be obtained through a greedy algorithm, thereby improving the time utilization and energy utilization of the phased array radar; (3) During the resource scheduling of the phased array, target parameters and radar resource utilization can be detected, and feedback adjustment can be made to the radar working parameters to complete the real-time update of the optimal sequence, thereby improving the radar task execution capability. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a flow chart of a method for resource scheduling and management of phased array radar based on a greedy algorithm of the present invention.

[0016] Figure 2 It is a flow chart of the greedy algorithm in the present invention.

[0017] Figure 3 It is a comparison curve diagram of resource utilization between a fixed-mode phased array radar and a phased array radar using the method of the present invention in an embodiment of the present invention. DETAILED DESCRIPTION

[0018] The present invention proposes a method for resource scheduling and management of phased array radar, which uses greedy algorithm rules to adjust the working parameters such as phased array radar beam pointing, waveform, data rate, etc. by focusing on the dynamic working background, target number, target characteristics and other factors, and sorts the task priorities in real time to achieve reasonable energy scheduling and resource allocation selection, and complete the work tasks such as target search and tracking under different situations; at the same time, according to the dynamic update of target data, the radar working parameters are evaluated and adjusted, and the radar resource scheduling is managed in real time to achieve the purpose of improving the radar time utilization and energy utilization. The present invention provides a new solution for the dynamic constraint balance of resources such as "time-energy-computer" in the resource scheduling of phased array radar.

[0019] The present invention provides a phased array radar resource scheduling management method based on a greedy algorithm, comprising the following steps:

[0020] Step 1: Analyze the dynamic background, target quantity, and target characteristics of the phased array radar, and build a phased array radar task model;

[0021] Step 2: According to the target parameters in the airspace and the phased array radar observation model, weight the various task parameters constituting the phased array radar working task model, determine the weighting coefficient, and obtain the priority function for the set task;

[0022] Step 3: Use the greedy algorithm to sort the priorities, output the task priority sequence, and execute the work tasks of the phased array radar including target search and tracking;

[0023] Step 4: Complete the phased array radar task, obtain the target parameter matrix, and feed it back to the task parameters of the phased array radar task model. Use the greedy algorithm again to optimize and update the task priority sequence, and perform the next task of the phased array radar. Repeat step 4 until the phased array radar reaches the set time, and then go to step 5.

[0024] Step 5: Analyze the resource utilization of the phased array radar and make a judgment according to the resource utilization threshold. If the expected effect is not met, adjust the task parameters and working parameters, use the greedy algorithm to sort the task priorities again, and return to step 4. If the expected effect is met, do not adjust the task parameters and working parameters, and directly return to step 4.

[0025] As a specific implementation method, the phased array radar working task model is constructed in step 1, specifically:

[0026] The phased array radar work task model is constructed according to important parameters such as surveillance airspace division, beam arrangement position, beam dwell time, data update rate and work priority.

[0027] As a specific implementation method, the factors such as the dynamic background of the phased array radar operation, the number of targets, and the target characteristics described in step 1 are centrally analyzed, and a phased array radar operation task model is constructed according to the monitoring airspace division, beam arrangement position, beam dwell time, data update rate, and work priority, as follows:

[0028] The phased array radar surveillance airspace is divided into N different sub-airspaces, each sub-airspace has L wave positions, and the current illumination wave position of the sub-airspace is B. i , the radar illumination frame period, i.e. the beam dwell time, is T si , then the average residence time of the beam at each wave position is ΔT si , the priority of each sub-airspace task is PR sn , build a phased array radar task model

[0029]

[0030] Where n=1,2,…,N, i=1,2,…,L, They respectively represent the beam arrival time, time window, deadline and actual scheduling execution time of the i-th beam dwell request in the n-th sub-spatial domain.

[0031] As a specific implementation method, the task parameters constituting the phased array radar working task model in step 2 include beam arrival time, time window, deadline, actual scheduling execution time and priority of each sub-airspace task.

[0032] As a specific implementation method, in step 2, the various task parameters constituting the phased array radar working task model are weighted according to the target parameters in the airspace and the phased array radar observation model to determine the weighting coefficient ω, which is specifically as follows:

[0033] ω=[ω1,ω2,ω3,ω4,ω5]

[0034] Among them, ω1, ω2, ω3, ω4, and ω5 are the beam arrival time, time window, deadline, actual scheduling execution time, and weighted coefficients of the priority of each sub-spatial task, respectively;

[0035] Get the priority function Pf(ω) for the set task:

[0036]

[0037] As a specific implementation method, the greedy algorithm described in step 3 is used to perform priority sorting, output a task priority sequence, and execute the work tasks of the phased array radar including target search and tracking, as follows:

[0038] The phased array radar resource scheduling problem is converted into a mathematical problem of solving the optimal solution of the priority function. According to the greedy algorithm principle, the beam arrival time, time window, deadline, actual scheduling execution time and the priority of each sub-airspace task in the priority function are gradually selected to obtain the optimal solution set, that is, the initial optimization selection of the working sequence.

[0039] As a specific implementation method, in step 4, the phased array radar work task is completed, the target parameter matrix is ​​obtained, and the matrix is ​​fed back to the task parameters of the phased array radar work task model. The greedy algorithm is used again to optimize and update the task priority sequence to perform the next work task of the phased array radar, as follows:

[0040] After the phased array radar task sequence is executed, the search and tracking of the target is completed within one or more working cycles, and the real-time parameters of K targets in the search airspace are obtained. The targets are classified according to their characteristics and sorted to obtain the target processing priority PRD. j , where j = 1, 2, ..., K, and this priority is fed back to the phased array radar task to generate a new target task priority PR sj :

[0041] PR sj =α1PR sn +α2PRD j

[0042] α = [α1, α2] is the weighting coefficient of task priority;

[0043] The updated phased array radar working task model is prioritized again using the greedy algorithm to update the task priority sequence of the next execution cycle of the phased array radar.

[0044] As a specific implementation, the phased array radar resource utilization is analyzed in step 5 and determined according to the resource utilization threshold value, as follows:

[0045] The phased array radar executes M tasks at the same time. After the greedy algorithm executes the task priority selection and updates multiple cycles, the resource utilization rate of the phased array radar within this period of time T is calculated, that is, the time utilization rate η of the phased array radar T and energy utilization rate η E :

[0046]

[0047]

[0048] Among them, T is the total working time of the phased array radar, T m is the time spent on executing the mth task, m = 1, 2, ..., M; T ms is the execution interval of the mth task scheduling, P av is the average transmit power of the phased array radar, E m is the energy consumed when executing the mth task;

[0049] The actual resource utilization of the phased array radar is compared with the expected resource utilization of the phased array radar. If the expected value is met, it means that the execution sequence meets the expected requirements. If the expected value is not met, the task priority parameters and weighting coefficients are updated, and the greedy algorithm rules are used to select again to find the optimal working sequence.

[0050] The present invention also provides a mobile terminal, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the phased array radar resource scheduling and management method when executing the program.

[0051] The present invention also provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the steps in the phased array radar resource scheduling and management method are implemented.

[0052] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0053] Example

[0054] The present embodiment provides a method for resource scheduling and management of a phased array radar, which centrally analyzes factors such as the working dynamic background of the phased array radar, the number of targets, and target characteristics, constructs a radar working task model according to important parameters, uses a greedy algorithm to perform priority sorting, and executes tasks to complete work tasks such as target search and tracking under different situations; according to the dynamic update of target data, the phased array radar working task model is corrected, and before the next working cycle of the phased array radar starts, the radar working parameters are adjusted, and the task priority is sorted in real time by the greedy algorithm, and the task sequence is executed again; after working for a period of time, the rationality of the working parameters and the working sequence is judged according to the radar resource scheduling utilization rate, and adjusted in real time.

[0055] Combination Figure 1 The phased array radar resource scheduling management method based on the greedy algorithm in this embodiment includes the following steps:

[0056] Step 1: Conduct a centralized analysis of the phased array radar working dynamic background, target quantity, and target characteristics, and build a phased array radar working task model according to the surveillance airspace division, beam arrangement position, beam dwell time, data update rate, and work priority, as follows:

[0057] The phased array radar surveillance airspace is divided into N different sub-airspaces, each sub-airspace has L wave positions, and the current illumination wave position of the sub-airspace is B. i , radar illumination frame period, that is, beam dwell time is T si , then the average residence time of the beam at each wave position is ΔT si The priority of each sub-airspace monitoring task is PR Sn , the working task model of building phased array radar can be expressed as:

[0058]

[0059] Where n=1,2,…,N, i=1,2,…,L, They represent the arrival time, time window, deadline and actual scheduling execution time of the i-th beam dwell request in the n-th sub-spatial domain respectively.

[0060] Step 2: According to the target parameters in the airspace and the phased array radar observation model, weight the various parameters that constitute the phased array radar work task model to obtain the weighted coefficient and the priority function for a specific task, as follows:

[0061] The parameters constituting the phased array radar working task model include beam arrival time, time window, deadline, scheduling execution time and task priority;

[0062] According to the target parameters in the airspace and the phased array radar observation model, the various parameters that constitute the phased array radar task model are weighted to obtain the weighted coefficients:

[0063] ω=[ω1,ω2,ω3,ω4,ω5]

[0064] Get the priority function for a specific task:

[0065]

[0066] Step 3: Use the greedy algorithm to sort the priorities, output the task priority sequence, and execute the target search and tracking tasks of the phased array radar, as follows:

[0067] The phased array radar resource scheduling problem is converted into a mathematical problem of solving the optimal solution of the priority function. According to the greedy algorithm principle, the arrival time, time window, deadline, execution time and task priority in the priority function are gradually selected to obtain the optimal solution set, that is, the initial optimization selection of the work sequence.

[0068] In the selection process of the greedy algorithm, according to the rules of the greedy algorithm, the selected greedy strategy must have no aftereffect, that is, the process before a certain state will not affect the subsequent state, and it is only related to the current state. The overall optimal solution is generated by obtaining the optimal solution of the local state of each task parameter. In the algorithm implementation process, the initialized task parameter matrix M, the task sequence index i and the task parameter change flag are first given. By perturbing the parameters, the change Δf of the optimization function is judged. If it is judged that there is an improvement on the optimization function, the improvement is accepted; if there is no improvement, the perturbation continues until the program algorithm ends, that is, the optimal solution is found and the task priority sequence is obtained. Figure 2 Implement a flowchart for the greedy algorithm.

[0069] Step 4: Complete the radar target search and tracking tasks, obtain the target parameter matrix, feed it back into the task parameters, and use the greedy algorithm again to optimize and update the task priority sequence to carry out the next target search and tracking task of the phased array radar. The details are as follows:

[0070] After the phased array radar task sequence is executed, the search / tracking of the target is completed within one or more working cycles, the real-time parameters of K targets in the search airspace are obtained, the targets are classified according to their characteristics, and the target processing priority PRD is obtained by sorting. j , where j = 1, 2, ..., K, and this priority is fed back to the phased array radar task to generate a new target task priority PR sj :

[0071] PR sj =α1PR sn +α2PRD j

[0072] α = [α1, α2] is the weighting coefficient of task priority;

[0073] The updated target task model is selected again using the greedy algorithm to update the task priority sequence for the next execution cycle. This process ensures that the phased array radar can continuously optimize the task sequence according to the actual situation.

[0074] Step 5, loop through step 4. After the phased array radar works for a specific period of time, analyze the radar resource utilization rate and make a judgment according to the resource utilization rate threshold. If the expected effect is not met, adjust the task parameters and working parameters, use the greedy algorithm to sort the task priorities again, and return to step 4. If the expected effect is met, do not adjust the task parameters and working parameters, and directly return to step 4. The details are as follows:

[0075] The phased array radar executes M tasks at the same time. After the greedy algorithm executes the task priority selection and updates for several cycles, the resource utilization rate of the phased array radar within this period of time T is calculated, that is, the time utilization rate η of the phased array radar T and energy utilization rate η E :

[0076]

[0077]

[0078] Among them, T is the total working time of the phased array radar, T m is the time spent executing the mth (m=1,2,…,M) task, T ms Schedule the execution interval for the mth (m=1,2,…,M) task, P av is the average transmit power of the phased array radar, E m is the energy consumed when executing the mth (m=1,2,…,M) task;

[0079] After the radar has been working for a specific period of time, the radar resource utilization is analyzed, and the actual resource utilization of the phased array radar is compared with the expected resource utilization of the phased array radar. If the expected value is met, the execution sequence meets the expected requirements; if the expected value is not met, the task priority parameters and their weighted coefficients are updated, and the greedy algorithm rules are used to select again to find the optimal working sequence. This process is the process of phased array radar adapting to the environment.

[0080] This embodiment uses a fixed-mode phased array radar resource scheduling management method and a phased array radar resource scheduling management method of the present invention to conduct simulation experiments. Figure 3 The following is a simulation diagram of the resource utilization of a fixed-mode phased array radar and the resource utilization of a phased array radar using the present invention as a function of the task load. According to the simulation results, it can be seen that the time utilization and energy utilization of the phased array radar resource scheduling algorithm optimized by the present invention are both improved to a certain extent compared with the unoptimized algorithm.

[0081] In summary, the phased array radar resource scheduling management method based on the greedy algorithm of the present invention can find the optimal priority sequence in the iterative loop, improve the time utilization and energy utilization of the phased array radar, that is, improve the task execution capability of the phased array radar, and meet the needs of task diversity and environmental complexity faced by the phased array radar.

Claims

1. A method for resource scheduling and management of phased array radar based on a greedy algorithm, characterized in that: The following steps are involved: Step 1: Analyze the dynamic background, target quantity, and target characteristics of the phased array radar, and build a phased array radar task model; Step 2: According to the target parameters in the airspace and the phased array radar observation model, weight the various task parameters constituting the phased array radar working task model, determine the weighting coefficient, and obtain the priority function for the set task; Step 3: Use the greedy algorithm to sort the priorities, output the task priority sequence, and execute the work tasks of the phased array radar including target search and tracking; Step 4: Complete the phased array radar task, obtain the target parameter matrix, and feed it back to the task parameters of the phased array radar task model. Use the greedy algorithm again to optimize and update the task priority sequence to carry out the next task of the phased array radar. The details are as follows: After the phased array radar task sequence is executed, the search and tracking of the target is completed within one or more working cycles, and the real-time parameters of K targets in the search airspace are obtained. The targets are classified according to their characteristics and sorted to obtain the target processing priority PRD. j , where j = 1, 2, ..., K, and this priority is fed back to the phased array radar task to generate a new target task priority PR sj : PR sj =α1PR sn +α2PRD j α = [α1, α2] is the weighting coefficient of task priority; PR sn Prioritize tasks for each sub-airspace; The updated phased array radar working task model is prioritized again using the greedy algorithm to update the task priority sequence of the next execution cycle of the phased array radar; Execute step 4 repeatedly until the phased array radar reaches the set working time, and then proceed to step 5; Step 5: Analyze the resource utilization of the phased array radar and make a judgment according to the resource utilization threshold. If the expected effect is not met, adjust the task parameters and working parameters, the working parameters include the phased array radar beam pointing, waveform, and data rate. Use the greedy algorithm to sort the task priorities again, and return to step 4. If the expected effect is met, do not adjust the task parameters and working parameters, and directly return to step 4.

2. The method for resource scheduling and management of phased array radar based on greedy algorithm according to claim 1, characterized in that: In step 1, the phased array radar task model is constructed, specifically: The phased array radar work task model is constructed according to important parameters such as surveillance airspace division, beam arrangement position, beam dwell time, data update rate and work priority.

3. The method for resource scheduling and management of phased array radar based on greedy algorithm according to claim 2 is characterized in that: The factors described in step 1, such as the dynamic background of phased array radar operation, the number of targets, and the characteristics of targets, are analyzed intensively, and the phased array radar operation task model is constructed according to the surveillance airspace division, beam arrangement position, beam dwell time, data update rate, and work priority, as follows: The phased array radar surveillance airspace is divided into N different sub-airspaces, each sub-airspace has L wave positions, and the current illumination wave position of the sub-airspace is B. i , the radar illumination frame period, i.e. the beam dwell time, is T si , then the average residence time of the beam at each wave position is ΔT si , the priority of each sub-airspace task is PR sn , build a phased array radar task model Where n=1,2,…,N, i=1,2,…,L, They respectively represent the beam arrival time, time window, deadline and actual scheduling execution time of the i-th beam dwell request in the n-th sub-spatial domain.

4. The method for resource scheduling and management of phased array radar based on greedy algorithm according to claim 3 is characterized in that: The task parameters that constitute the phased array radar working task model in step 2 include beam arrival time, time window, deadline, actual scheduling execution time and the priority of each sub-airspace task.

5. The method for resource scheduling and management of phased array radar based on greedy algorithm according to claim 4 is characterized in that: In step 2, according to the target parameters in the airspace and the phased array radar observation model, the various task parameters constituting the phased array radar working task model are weighted to determine the weighting coefficient ω, which is as follows: ω=[ω1,ω2,ω3,ω4,ω5] Among them, ω1, ω2, ω3, ω4, and ω5 are the beam arrival time, time window, deadline, actual scheduling execution time, and weighted coefficients of the priority of each sub-spatial task, respectively; Get the priority function Pf(ω) for the set task:

6. The method for resource scheduling and management of phased array radar based on greedy algorithm according to claim 5, characterized in that: Step 3 uses the greedy algorithm to perform priority sorting, outputs the task priority sequence, and executes the work tasks of the phased array radar including target search and tracking, as follows: The phased array radar resource scheduling problem is converted into a mathematical problem of solving the optimal solution of the priority function. According to the greedy algorithm principle, the beam arrival time, time window, deadline, actual scheduling execution time and the priority of each sub-airspace task in the priority function are gradually selected to obtain the optimal solution set, that is, the initial optimization selection of the working sequence.

7. The method for resource scheduling and management of phased array radar based on greedy algorithm according to claim 6, characterized in that: The phased array radar resource utilization is analyzed in step 5 and determined according to the resource utilization threshold value, as follows: The phased array radar executes M tasks at the same time. After the greedy algorithm executes the task priority selection and updates multiple cycles, the resource utilization rate of the phased array radar within this period of time T is calculated, that is, the time utilization rate of the phased array radar η T and energy utilization rate η E : Among them, T is the total working time of the phased array radar, T m is the time spent on executing the mth task, m = 1, 2, ..., M; T ms Schedule the execution interval for the mth task, P av is the average transmit power of the phased array radar, E m is the energy consumed when executing the mth task; The actual resource utilization of the phased array radar is compared with the expected resource utilization of the phased array radar. If the expected value is met, it means that the execution sequence meets the expected requirements. If the expected value is not met, the task priority parameters and weighting coefficients are updated, and the greedy algorithm rules are used to select again to find the optimal working sequence.

8. A mobile terminal comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the phased array radar resource scheduling and management method according to any one of claims 1 to 7 is implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps in the phased array radar resource scheduling and management method as described in any one of claims 1 to 7 are implemented.

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