[1]周仕霖.基于改进粒子群的重频组多目标优化算法[J].探测与控制学报,2021,43(03):92.[doi:.]
 ZHOU Shilin.Radar PRF Set Selection Based on Improved Particle Swarm Optimization[J].,2021,43(03):92.[doi:.]
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基于改进粒子群的重频组多目标优化算法()
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《探测与控制学报》[ISSN:1008-1194/CN:61-1316/TJ]

卷:
43
期数:
2021年03
页码:
92
栏目:
出版日期:
2021-08-01

文章信息/Info

Title:
Radar PRF Set Selection Based on Improved Particle Swarm Optimization
文章编号:
1008-1194(2021)03-0092-06
作者:
周仕霖
西安科技大学,陕西 西安 710600
Author(s):
ZHOU Shilin
Xi’an University of Science and Technology, Xi’an 710600, China
关键词:
脉冲重频组粒子群算法多目标优化
Keywords:
PRF setparticle swarm optimizationmulti-objective optimization
分类号:
TP301
DOI:
.
文献标志码:
A
摘要:
针对传统目标优化算法存在优化目标较为单一的问题,提出基于改进粒子群的重频组多目标优化算法。该算法以传统粒子群算法为基础,使用混沌理论改进粒子群算法结构,结合多目标协同优化,实现了重频组在多个方面综合性能的提升。仿真实验结果表明,该算法能够提升雷达系统抗盲区性能,有效减小了因回波错误互相关产生的虚影。
Abstract:
In order to solve the problem that the traditional objective optimization algorithm could only optimize single objective, a multi-objective optimization algorithm based on improved particle swarm optimization (PSO) was proposed. Based on the traditional PSO, this algorithm used chaos theory to improve the structure of PSO, and combineed multi-objective collaborative optimization to improve the comprehensive performance of the PSO in many aspects. Simulation results showed that this algorithm could improve the blind resistance performance of radar system and effectively reduced the virtual shadow caused by echo error correlation.

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备注/Memo

备注/Memo:
收稿日期:2020-12-31
作者简介:周仕霖(2000—),男,陕西西安人。
更新日期/Last Update: 2021-08-04