Improved PSO and Its Application to CS DOA Estimation

ZHAO Hong-wei; LIU Bo; LIU Heng

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Improved PSO and Its Application to CS DOA Estimation

更新时间：2026-09-04

- Improved PSO and Its Application to CS DOA Estimation
- Vol. 33, Issue 5, Pages: 33-36(2016)
- 作者机构：
  
  西安空间无线电技术研究所,陕西,西安,710100
- 作者简介：
- 基金信息：
- DOI：
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- Published：2016
- 稿件说明：
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赵宏伟, 刘波, 刘恒. 改进粒子群优化在压缩感知DOA估计中的应用[J]. 微电子学与计算机, 2016, 33(5): 33-36, 41.

ZHAO Hong-wei, LIU Bo, LIU Heng. Improved PSO and Its Application to CS DOA Estimation[J]. 2016, 33(5): 33-36.
赵宏伟, 刘波, 刘恒. 改进粒子群优化在压缩感知DOA估计中的应用[J]. 微电子学与计算机, 2016, 33(5): 33-36, 41. DOI：

ZHAO Hong-wei, LIU Bo, LIU Heng. Improved PSO and Its Application to CS DOA Estimation[J]. 2016, 33(5): 33-36. DOI：

摘要

以压缩感知(CS)的波达方向(DOA)估计为研究背景

针对正交匹配追踪算法用于高维信号稀疏恢复时效率低下问题

提出一种结合粒子群优化(PSO)和正交匹配追踪(OMP)的新DOA估计算法.该算法充分利用了PSO算法解决寻优问题的优势

并结合OMP算法的处理流程

具有运算时间较少、估计误差较低和适用范围广等优势.为了提高算法性能

对标准PSO算法的粒子更新机制和惯性权值等进行了改进.仿真结果证明了方法的有效性.

Abstract

The present direction of arrival (DOA) estimation methods based on the compressive sensing (CS) theory is background at present. Shortcoming of greedy pursuit methods is efficiency to solve sparse recovery. A new algorithm based on Particle Swarm Optimization (PSO) algorithm and orthogonal matching pursuit (OMP) algorithm is proposed. It has less computation time

low estimation error and wide application range

which makes full use of the advantages of PSO and OMP algorithm. To improve algorithm performance

the particle updating system of standard PSO algorithm are improved. The results of simulation experiments show that the proposed algorithm is effective.

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references

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Zhang Tong. Sparse recovery with orthogonal matching pursuit under RIP[J]. IEEE Transaction on Information Theory, 2011, 57(9): 6215-6221. DOI: 6215

Jacob R, Yahmat-samii. Particle swarm optimization in electromagnetics[J]. IEEE Transactionon Antennas and Propagation, 2004, 52(2): 397-407. DOI: 397

蒋柏峰, 吕晓德, 向茂生. 一种基于阵列接收信号重拍的单快拍DOA估计方法[J]. 电子与信息学报, 2014, 36(6): 2014-2019. DOI: 2014

陈基漓. 粒子群算法种群多样性控制方法研究[J]. 微电子学与计算机, 2013, 30(6): 6-9. DOI: 6

陶海红, 廖桂生, 王伶. 用于杂波抑制滤波器优化设计的混合遗传算法及其收敛性分析[J]. 电子学报, 2012, 40(12): 2086-2089. DOI: 2086

方红, 杨海蓉. 贪婪算法与压缩感知理论[J]. 自动化学报, 2011, 37(12): 1413-1422. DOI: 1413

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