CN115034634A - Phased array radar resource scheduling management method based on greedy algorithm - Google Patents

Phased array radar resource scheduling management method based on greedy algorithm Download PDF

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CN115034634A
CN115034634A CN202210701176.4A CN202210701176A CN115034634A CN 115034634 A CN115034634 A CN 115034634A CN 202210701176 A CN202210701176 A CN 202210701176A CN 115034634 A CN115034634 A CN 115034634A
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phased array
array radar
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赵宪涛
徐磊
栾铸徵
丁佐诚
赵新燕
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723 Research Institute of CSIC
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    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
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    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
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Abstract

The invention discloses a greedy algorithm-based phased array radar resource scheduling management method, which comprises the following steps: firstly, constructing a phased array radar work task model, and weighting each parameter forming the phased array radar work task model to obtain a weighting coefficient and a priority function for a certain specific task; then, performing priority ordering by using a greedy algorithm, outputting a task priority sequence, executing target searching and tracking work tasks of the phased array radar to obtain a target parameter matrix, feeding the target parameter matrix back to the task parameters, optimizing and updating the task priority sequence by using the greedy algorithm, and performing next target searching and tracking work tasks of the phased array radar; and after the phased array radar works for a specific time, analyzing the resource utilization rate of the phased array radar, if the expected effect is not met, adjusting the task parameters and the working parameters, and sequencing the task priorities by utilizing a greedy algorithm again. The invention improves the time utilization rate and the energy utilization rate of the phased array radar.

Description

Phased array radar resource scheduling management method based on greedy algorithm
Technical Field
The invention relates to the technical field of phased array radar resource management, in particular to a greedy algorithm-based phased array radar resource scheduling management method.
Background
In recent years, the phased array radar technology is applied more and more widely, and application scenes are more and more. The phased array radar resource scheduling management is to schedule radar resources in real time within a task range specified by a radar according to task requirements, and meet the requirement of exerting the optimal performance of the radar under the resource constraint condition through reasonable time and energy distribution so as to fulfill the aim of completing various actual tasks.
Aiming at the problem of phased array radar resource scheduling optimization, scholars at home and abroad carry out a great deal of research and obtain a great deal of achievements. In the early stage, due to the constraint of software and hardware conditions of radar design, template scheduling is widely applied. The template scheduling is divided into a fixed template, a partial template, a multi-template and the like, has the advantages of simple method, simple and convenient engineering realization, less resource occupation and the like, and also has the defects of low scheduling efficiency, poor adaptive capacity, low resource utilization rate and the like. With the improvement of computer processing capability and the continuous improvement of radar task requirements, the adaptive scheduling method gradually replaces the template scheduling method, and becomes the mainstream trend of phased array radar resource scheduling design.
Phased array radar resource management is a guarantee for giving full play to the performance premise of a radar system, and especially under the condition that the battlefield environment of a radar is very complex and targets are dense, the self-adaptive radar resource management strategy has important significance. The self-adaptive scheduling method is a scheduling method for selecting an optimal working sequence for the radar after comprehensive balance optimization by evaluating resources such as radar energy, time and the like in real time under the radar constraint condition. There are many existing adaptive resource scheduling methods, such as a risk cost-based method, an information theory-based method, a task comprehensive planning-based method, and a time window-based method. In different working scenes, different resource scheduling methods can exert respective advantages, but the single resource scheduling method is difficult to meet the challenges of task diversity and environment complexity faced by the phased array radar.
Disclosure of Invention
The invention aims to provide a greedy algorithm-based phased array radar resource scheduling management method which is high in time utilization rate and energy utilization rate, strong in task execution capacity and capable of meeting requirements of task diversity and environment complexity.
The technical solution for realizing the purpose of the invention is as follows: a phased array radar resource scheduling management method based on a greedy algorithm comprises the following steps:
step 1, performing centralized analysis on factors such as working dynamic background, target quantity and target characteristics of the phased array radar, and constructing a working task model of the phased array radar;
step 2, weighting each task parameter forming the phased array radar working task model according to the target parameters in the airspace and the phased array radar observation model, determining a weighting coefficient, and obtaining a priority function of a set task;
step 3, performing priority sorting by using a greedy algorithm, outputting a task priority sequence, and executing work tasks including target searching and tracking of the phased array radar;
step 4, completing the working task of the phased array radar, obtaining a target parameter matrix, feeding the target parameter matrix back to task parameters of a working task model of the phased array radar, optimizing and updating a task priority sequence by using a greedy algorithm again, performing the next working task of the phased array radar, executing the step 4 in a circulating mode until the working time of the phased array radar reaches a set time, and entering the step 5;
step 5, analyzing the resource utilization rate of the phased array radar, judging according to a resource utilization rate threshold value, adjusting task parameters and working parameters if the expected effect is not met, sorting the task priorities by using a greedy algorithm again, and returning to the step 4; and if the expected effect is met, the task parameters and the working parameters are not adjusted, and the step 4 is directly executed.
A mobile terminal comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the phased array radar resource scheduling management method when executing the program.
A computer readable storage medium having stored thereon a computer program, wherein said program, when executed by a processor, implements the steps in the phased array radar resource scheduling management method.
Compared with the prior art, the invention has the remarkable advantages that: (1) in the working process of the phased array radar, a working task model close to the actual working environment can be constructed according to factors such as the working background, the number of targets and the characteristics of the targets of the phased array radar, so that the environment adaptability of the phased array radar is improved; (2) in the phased array radar resource scheduling process, a priority function can be created through weighting processing according to a work task model, a task sequence ordering problem is converted into a function solving problem, an optimal solution is solved through a greedy algorithm, and the time utilization rate and the energy utilization rate of the phased array radar are improved; (3) in the phased array resource scheduling process, target parameters and radar resource utilization rate can be detected, radar working parameters are fed back and adjusted, the optimized sequence is updated in real time, and the radar task execution capacity is improved.
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Fig. 1 is a schematic flow diagram of a greedy algorithm-based phased array radar resource scheduling management method according to the present invention.
FIG. 2 is a schematic flow chart of the greedy algorithm of the present invention.
Fig. 3 is a resource utilization comparison graph of a phased array radar using 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
The invention provides a phased array radar resource scheduling management method, which is characterized in that dynamic working background, target quantity, target characteristics and other factors are analyzed in a centralized manner, working parameters such as beam direction, waveform, data rate and the like of a phased array radar are adjusted by utilizing a greedy algorithm rule, task priorities are sequenced in real time, reasonable energy scheduling and resource allocation selection are realized, and working tasks such as target searching, tracking and the like under different situations are completed; meanwhile, radar working parameters are evaluated and adjusted according to dynamic updating of target data, and radar resource scheduling is managed in real time, so that the purpose of improving the time utilization rate and the energy utilization rate of the radar is achieved. The invention provides a new solution for dynamic constraint balance of resources such as time-energy-computer and the like for phased array radar resource scheduling.
The invention discloses a phased array radar resource scheduling management method based on a greedy algorithm, which comprises the following steps of:
step 1, performing centralized analysis on factors including a working dynamic background, the number of targets and target characteristics of the phased array radar, and constructing a working task model of the phased array radar;
step 2, weighting each task parameter forming the phased array radar working task model according to the target parameters in the airspace and the phased array radar observation model, determining a weighting coefficient, and obtaining a priority function of a set task;
3, performing priority sequencing by using a greedy algorithm, outputting a task priority sequence, and executing work tasks of the phased array radar including target searching and tracking;
step 4, completing the working task of the phased array radar, obtaining a target parameter matrix, feeding the target parameter matrix back to task parameters of a working task model of the phased array radar, optimizing and updating a task priority sequence by using a greedy algorithm again, performing the next working task of the phased array radar, executing the step 4 in a circulating mode until the working time of the phased array radar reaches a set time, and entering the step 5;
step 5, analyzing the resource utilization rate of the phased array radar, judging according to a resource utilization rate threshold value, adjusting task parameters and working parameters if the expected effect is not met, sorting the task priorities by using a greedy algorithm again, and returning to the step 4; and if the expected effect is met, the task parameters and the working parameters are not adjusted, and the step 4 is directly executed.
As a specific implementation manner, the constructing of the phased array radar work task model in step 1 specifically includes:
and constructing a phased array radar work task model according to important parameters such as monitoring airspace division, beam arrangement positions, beam residence time, data updating rate and work priority.
As a specific implementation manner, the centralized analysis is performed on the factors of the dynamic background, the number of targets, and the target characteristics of the phased array radar in step 1, and a working task model of the phased array radar is constructed according to the monitored airspace division, the beam arrangement position, the beam dwell time, the data update rate, and the working priority, specifically as follows:
dividing a phased array radar monitoring airspace into N different sub-airspaces, wherein each sub-airspace has L wave positions, and the current irradiation wave position of the sub-airspace is B i The radar irradiation frame period, i.e. the beam dwell time, is T si Then the average beam dwell time per wave position is Δ T si Each sub-spatial domain task has priority PR sn Constructing a phased array radar task model
Figure BDA0003704284550000031
Figure BDA0003704284550000041
Wherein N is 1,2, …, N, i is 1,2, …, L,
Figure BDA0003704284550000042
respectively representing the beam arrival time, the time window, the deadline and the actual scheduling execution time of the ith beam dwell request of the nth sub-airspace.
As a specific implementation manner, the task parameters forming the phased array radar work task model in step 2 include beam arrival time, time window, deadline, actual scheduling execution time, and task priority of each sub-airspace.
As a specific implementation manner, in step 2, according to the target parameters in the airspace and the phased array radar observation model, weighting each task parameter that forms the phased array radar working task model, and determining a weighting coefficient ω, specifically as follows:
ω＝[ω 1 ,ω 2 ,ω 3 ,ω 4 ,ω 5 ]
wherein, ω is 1 ,ω 2 ,ω 3 ,ω 4 ,ω 5 In turn a beamThe arrival time, the time window, the deadline, the actual scheduling execution time and the weighting coefficient of each sub-airspace task priority;
obtaining a priority function Pf (ω) for the set task:
Figure BDA0003704284550000043
as a specific implementation manner, the priority ranking is performed by using a greedy algorithm in step 3, a task priority sequence is output, and a working task including target searching and tracking of the phased array radar is executed, which specifically includes the following steps:
and converting the phased array radar resource scheduling problem into a mathematical problem solved by the optimal solution of the priority function, and gradually selecting the beam arrival time, the time window, the cut-off time, the actual scheduling execution time and the priority of each sub-airspace task in the priority function according to a greedy algorithm criterion to obtain an optimal solution set, namely the initial optimal selection of the working sequence.
As a specific implementation manner, in step 4, completing the working task of the phased array radar to obtain a target parameter matrix, feeding the target parameter matrix back to the task parameters of the working task model of the phased array radar, optimizing and updating the task priority sequence by using the greedy algorithm again, and performing the next working task of the phased array radar, specifically as follows:
after the phased array radar task sequence is executed, searching and tracking the targets in one or more working cycles are completed, real-time parameters of K targets in a search space are obtained, the targets are classified according to target characteristics, and the PRD (priority of target processing) is obtained by sequencing j Where j is 1,2, …, K, feeding the priority back to the phased array radar task to generate a new target task priority PR sj ：
PR sj ＝α 1 PR sn +α 2 PRD j
α＝[α 1 ,α 2 ]A weighting factor that is a task priority;
and (4) performing priority sequencing on the updated working task model of the phased array radar by using a greedy algorithm again, and updating the task priority sequence of the next execution period of the phased array radar.
As a specific implementation manner, the analyzing the resource utilization rate of the phased array radar in step 5, and determining according to a resource utilization rate threshold value specifically includes:
the phased array radar executes M tasks simultaneously, and after the greedy algorithm executes task priority selection and updates a plurality of periods, the utilization rate of phased array radar resources in the period T is calculated, namely the time utilization rate eta of the phased array radar T And energy utilization efficiency eta E ：
Figure BDA0003704284550000051
Figure BDA0003704284550000052
Wherein T is the total working time of the phased array radar, T m For the time spent in executing the mth task, M is 1,2, …, M; t is ms Scheduling an execution Interval, P, for the mth task av For phased array radar average transmitted power, E m The energy consumed for the execution of the mth task;
comparing the actual resource utilization rate of the phased array radar with the expected resource utilization rate of the phased array radar, and meeting the expected value to indicate that the execution sequence meets the expected requirement; if the task priority does not meet the expected value, updating each parameter and the weighting coefficient of the task priority, and selecting again by using a greedy algorithm rule, thereby searching the optimal work sequence.
The invention also provides a mobile terminal which comprises a memory, a processor and a computer program which is stored on the memory and can run on the processor, wherein the processor executes the program to realize the phased array radar resource scheduling management method.
The present invention also provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps in the phased array radar resource scheduling management method.
The invention is described in further detail below with reference to the figures and the specific embodiments.
Examples
The embodiment provides a phased array radar resource scheduling management method, which is used for intensively analyzing factors such as working dynamic background, target quantity and target characteristics of a phased array radar, constructing a radar working task model according to important parameters, performing priority sequencing by using a greedy algorithm, executing tasks, and completing working tasks such as target searching and tracking under different situations; according to the dynamic update of target data, correcting a working task model of the phased array radar, adjusting radar working parameters before the start of the next working cycle of the phased array radar, finishing the real-time sequencing of task priorities through a greedy algorithm, and executing a task sequence again; after working for a period of time, working parameters and working sequence rationality are judged according to the radar resource scheduling utilization rate, and real-time adjustment is carried out.
With reference to fig. 1, the phased array radar resource scheduling management method based on the greedy algorithm of the embodiment includes the following steps:
step 1, performing centralized analysis on the working dynamic background, the target quantity and the target characteristics of the phased array radar, and constructing a working task model of the phased array radar according to the monitoring airspace division, the beam arrangement position, the beam residence time, the data update rate and the working priority, wherein the working task model specifically comprises the following steps:
dividing a phased array radar monitoring airspace into N different sub-airspaces, wherein each sub-airspace has L wave positions, and the current irradiation wave position of the sub-airspace is B i Radar exposure frame period, i.e. beam dwell time, of T si Then the average beam dwell time per wave position is Δ T si The priority of each sub-airspace monitoring task is PR Sn The construction of the working task model of the phased array radar can be expressed as follows:
Figure BDA0003704284550000061
wherein N is 1,2, …, N, i＝1,2,…,L，
Figure BDA0003704284550000062
Respectively representing the arrival time, the time window, the deadline and the actual scheduling execution time of the ith beam dwell request of the nth sub-airspace.
Step 2, weighting all parameters forming the phased array radar working task model according to target parameters in an airspace and the phased array radar observation model to obtain a weighting coefficient and a priority function for a certain specific task, wherein the weighting coefficient comprises the following specific steps:
each parameter forming the working task model of the phased array radar comprises beam arrival time, a time window, cut-off time, scheduling execution time and task priority; :
weighting each parameter forming the phased array radar work task model according to the target parameters in the airspace and the phased array radar observation model to obtain a weighting coefficient:
ω＝[ω 1 ,ω 2 ,ω 3 ,ω 4 ,ω 5 ]
a priority function for a particular task is obtained:
Figure BDA0003704284550000063
and 3, performing priority sequencing by using a greedy algorithm, outputting a task priority sequence, and executing target searching and tracking work tasks of the phased array radar, wherein the priority sequencing comprises the following steps:
converting a phased array radar resource scheduling problem into a mathematical problem solved by a priority function optimal solution, and gradually selecting arrival time, a time window, cutoff time, execution time and task priority in the priority function according to a greedy algorithm criterion to obtain an optimal solution set, namely initial optimal selection of a working sequence;
in the greedy algorithm selection process, according to the greedy algorithm rule, the selected greedy strategy has no aftereffect, namely, the previous process of a certain state cannot influence the later state and is only related to the current state. And generating an overall optimal solution by obtaining the optimal solution of the local state of each task parameter. In the algorithm implementation process, an initialized task parameter matrix M, a task sequence index i and a task parameter change flag are given firstly. Judging the change quantity delta f of the optimization function through the perturbation of the parameters, and if judging that the optimization function is improved, receiving the improvement; and if the task priority sequence is not improved, continuing perturbation until the program algorithm is ended, and solving the optimal solution to obtain the task priority sequence. FIG. 2 is a flow chart of a greedy algorithm implementation.
Step 4, completing radar target searching and tracking tasks, obtaining a target parameter matrix, feeding the target parameter matrix back to task parameters, optimizing and updating the task priority sequence by using the greedy algorithm again, and performing next target searching and tracking work tasks of the phased array radar, wherein the method specifically comprises the following steps:
after the phased array radar task sequence is executed, searching/tracking of targets is completed in one or more working cycles, real-time parameters of K targets in a search space are obtained, the targets are classified according to target characteristics, and target processing priority PRD is obtained in a sequencing mode j Where j is 1,2, …, K, feeding the priority back to the phased array radar task to generate a new target task priority PR sj ：
PR sj ＝α 1 PR sn +α 2 PRD j
α＝[α 1 ,α 2 ]A weighting factor that is a task priority;
and selecting the updated target task model again by using a greedy algorithm, and updating the task priority sequence of the next execution cycle. The process ensures that the phased array radar can achieve the effect of continuously optimizing the task sequence according to the actual situation.
Step 5, circularly executing the step 4, analyzing the radar resource utilization rate after the phased array radar works for a specific time, judging according to a resource utilization rate threshold value, adjusting the task parameters and the working parameters if the expected effect is not met, sorting the task priorities by using a greedy algorithm again, and returning to the step 4; if the expected effect is met, the task parameter and the working parameter are not adjusted, and the step 4 is directly executed, specifically as follows:
the phased array radar executes M tasks simultaneously, and after the greedy algorithm executes the task priority selection and updates a plurality of periods, the resource utilization rate of the phased array radar in the period T is calculated, namely the time utilization rate eta of the phased array radar T And energy utilization efficiency eta E ：
Figure BDA0003704284550000071
Figure BDA0003704284550000072
Wherein T is the total working time of the phased array radar, T m Time taken to execute the M-th (M is 1,2, …, M) task, T ms Scheduling an execution interval, P, for the M-th (M-1, 2, …, M) task av Average transmission power for phased array radar, E m The energy consumed for the M (M-1, 2, …, M) th task to execute;
after the radar works for a specific time, analyzing the utilization rate of radar resources, comparing the actual resource utilization rate of the phased array radar with the expected resource utilization rate of the phased array radar, and meeting the requirement that an expected value execution sequence meets the expected requirement; and if the expected value is not met, updating each parameter of the task priority and the weighting coefficient thereof, and selecting again by using a greedy algorithm rule, thereby searching for the optimal working sequence, wherein the process is a process of the phased array radar self-adaptive environment.
In this embodiment, a fixed mode phased array radar resource scheduling management method and a simulation experiment using the phased array radar resource scheduling management method of the present invention are used, and fig. 3 is a simulation diagram of the resource utilization rate of a fixed mode phased array radar and the resource utilization rate of a phased array radar using the present invention changing with a task amount. According to simulation results, the time utilization rate and the energy utilization rate of the phased array radar resource scheduling algorithm optimized by the method are improved to a certain extent compared with the unoptimized algorithm.
In conclusion, the greedy algorithm-based phased array radar resource scheduling management method can search for the optimized priority sequence in the iterative loop, improves the time utilization rate and the energy utilization rate of the phased array radar, namely improves the task execution capacity of the phased array radar, and meets the requirements of task diversity and environment complexity of the phased array radar.

Claims (10)

1. A phased array radar resource scheduling management method based on a greedy algorithm is characterized by comprising the following steps:
step 1, performing centralized analysis on factors including a working dynamic background, the number of targets and target characteristics of the phased array radar, and constructing a working task model of the phased array radar;
step 2, weighting each task parameter forming the phased array radar working task model according to the target parameters in the airspace and the phased array radar observation model, determining a weighting coefficient, and obtaining a priority function of a set task;
3, performing priority sequencing by using a greedy algorithm, outputting a task priority sequence, and executing work tasks of the phased array radar including target searching and tracking;
step 4, completing the working task of the phased array radar, obtaining a target parameter matrix, feeding the target parameter matrix back to task parameters of a working task model of the phased array radar, optimizing and updating a task priority sequence by using a greedy algorithm again, performing the next working task of the phased array radar, executing the step 4 in a circulating mode until the working time of the phased array radar reaches a set time, and entering the step 5;
step 5, analyzing the resource utilization rate of the phased array radar, judging according to a resource utilization rate threshold value, adjusting task parameters and working parameters if the expected effect is not met, sorting the task priorities by using a greedy algorithm again, and returning to the step 4; and if the expected effect is met, the task parameters and the working parameters are not adjusted, and the step 4 is directly executed.
2. The greedy algorithm-based phased array radar resource scheduling management method according to claim 1, wherein a phased array radar work task model is constructed in step 1, and specifically:
and constructing a working task model of the phased array radar according to important parameters such as monitoring airspace division, beam arrangement positions, beam residence time, data update rate and working priority.
3. The greedy algorithm-based phased array radar resource scheduling management method according to claim 2, wherein the factors of the working dynamic background, the number of targets and the characteristics of the targets of the phased array radar are analyzed in a centralized manner in the step 1, and a phased array radar working task model is constructed according to the monitoring airspace division, the beam arrangement position, the beam residence time, the data update rate and the working priority, and specifically the following steps are performed:
dividing a phased array radar monitoring airspace into N different sub-airspaces, wherein each sub-airspace has L wave positions, and the current irradiation wave position of the sub-airspace is B i The radar irradiation frame period, i.e. the beam dwell time, is T si Then the average beam dwell time per wave position is Δ T si Each sub-spatial domain task has priority PR sn Constructing a phased array radar task model
Figure FDA0003704284540000011
Figure FDA0003704284540000012
Wherein N is 1,2, …, N, i is 1,2, …, L,
Figure FDA0003704284540000013
respectively representing the beam arrival time, the time window, the deadline and the actual scheduling execution time of the ith beam dwell request of the nth sub-airspace.
4. The greedy algorithm based phased array radar resource scheduling management method according to claim 3, wherein the task parameters forming the phased array radar work task model in step 2 include beam arrival time, time window, deadline time, actual scheduling execution time, and task priority of each sub-airspace.
5. The greedy algorithm-based phased array radar resource scheduling management method according to claim 4, wherein in the step 2, each task parameter forming the working task model of the phased array radar is weighted according to the target parameter in the airspace and the phased array radar observation model, and a weighting coefficient ω is determined as follows:
ω＝[ω 1 ，ω 2 ，ω 3 ，ω 4 ，ω 5 ]
wherein, ω is 1 ，ω 2 ，ω 3 ，ω 4 ，ω 5 Sequentially weighting coefficients of the arrival time of the wave beam, a time window, a cut-off time, the actual scheduling execution time and the priority of each sub-airspace task;
obtaining a priority function Pf (ω) for the set task:
Figure FDA0003704284540000021
6. the greedy algorithm based phased array radar resource scheduling management method according to claim 5, wherein the greedy algorithm is used for priority ordering, a task priority sequence is output, and work tasks including target searching and tracking of the phased array radar are executed, specifically as follows:
and converting the phased array radar resource scheduling problem into a mathematical problem solved by the optimal solution of the priority function, and gradually selecting the beam arrival time, the time window, the cut-off time, the actual scheduling execution time and the priority of each sub-airspace task in the priority function according to a greedy algorithm criterion to obtain an optimal solution set, namely the initial optimal selection of the working sequence.
7. The greedy algorithm-based phased array radar resource scheduling management method according to claim 6, wherein the working tasks of the phased array radar are completed in the step 4 to obtain a target parameter matrix, the target parameter matrix is fed back to task parameters of a working task model of the phased array radar, the greedy algorithm is reused to optimize and update a task priority sequence, and the next working task of the phased array radar is performed, specifically as follows:
after the phased array radar task sequence is executed, searching and tracking the targets in one or more working periods are completed to obtain real-time parameters of K targets in a search airspace, the targets are classified according to target characteristics, and the targets are sequenced to obtain the PRD (priority rank) of target processing j Where j is 1,2, …, K, feeding the priority back to the phased array radar task to generate a new target task priority PR sj ：
PR sj ＝α 1 PR sn +α 2 PRD j
α＝[α 1 ，α 2 ]A weighting factor that is a task priority;
and (4) performing priority sequencing on the updated working task model of the phased array radar by using a greedy algorithm again, and updating the task priority sequence of the next execution period of the phased array radar.
8. The greedy algorithm based phased array radar resource scheduling management method according to claim 6, wherein the phased array radar resource utilization rate is analyzed in step 5, and the determination is performed according to a resource utilization rate threshold value, which is specifically as follows:
the phased array radar executes M tasks simultaneously, and after the greedy algorithm executes the task priority selection and updates a plurality of periods, the resource utilization rate of the phased array radar in the period T is calculated, namely the time utilization rate eta of the phased array radar T And energy utilization efficiency eta E ：
Figure FDA0003704284540000031
Figure FDA0003704284540000032
Wherein T is the total working time of the phased array radar, T m To perform the mth task, M is 1,2, …, M; t is ms Scheduling an execution Interval, P, for the mth task av For phased array radar average transmitted power, E m The energy consumed for the execution of the mth task;
comparing the actual resource utilization rate of the phased array radar with the expected resource utilization rate of the phased array radar, and meeting the expected value to indicate that the execution sequence meets the expected requirement; if the task priority does not meet the expected value, updating each parameter and the weighting coefficient of the task priority, and selecting again by using a greedy algorithm rule, thereby searching the optimal work sequence.
9. A mobile terminal comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the phased array radar resource scheduling management method according to any one of claims 1 to 8 when executing the program.
10. A computer readable storage medium having stored thereon a computer program, which when executed by a processor performs the steps in the phased array radar resource scheduling management method according to any one of claims 1 to 8.
CN202210701176.4A 2022-06-21 2022-06-21 Phased array radar resource scheduling management method based on greedy algorithm Pending CN115034634A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116562039A (en) * 2023-05-17 2023-08-08 扬州宇安电子科技有限公司 Phased array radar scheduling model simulation method and simulation system

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116562039A (en) * 2023-05-17 2023-08-08 扬州宇安电子科技有限公司 Phased array radar scheduling model simulation method and simulation system
CN116562039B (en) * 2023-05-17 2023-10-20 扬州宇安电子科技有限公司 Phased array radar scheduling model simulation method and simulation system

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