应用人工蜂群算法的动态波达方向跟踪

单泽彪; 石要武; 刘小松; 张志成

doi:10.3788/OPE.20152303.0838

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应用人工蜂群算法的动态波达方向跟踪

信息科学 | 更新时间：2020-08-13

- 应用人工蜂群算法的动态波达方向跟踪
- DOA tracking of moving targets by artificial bee colony algorithm
- 光学精密工程 2015年23卷第3期页码：838-845
- 作者机构：
  
  1. 吉林大学通信工程学院,吉林长春,中国,130012
  2. 吉林大学工程仿生教育部重点实验室,吉林长春,130022
- 作者简介：
- 基金信息：
  
  国家自然科学基金资助项目(No.51075175)();吉林省科技发展计划资助项目(No.20140101078JC)()
- DOI：10.3788/OPE.20152303.0838
  中图分类号： TN911;TP391
- 收稿日期：2014-11-05，
  
  修回日期：2014-12-05，
  
  纸质出版日期：2015-03-25
- 稿件说明：
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单泽彪, 石要武, 刘小松等. 应用人工蜂群算法的动态波达方向跟踪[J]. 光学精密工程, 2015,23(3): 838-845

SHAN Ze-biao, SHI Yao-wu, LIU Xiao-song etc. DOA tracking of moving targets by artificial bee colony algorithm[J]. Editorial Office of Optics and Precision Engineering, 2015,23(3): 838-845
单泽彪, 石要武, 刘小松等. 应用人工蜂群算法的动态波达方向跟踪[J]. 光学精密工程, 2015,23(3): 838-845 DOI： 10.3788/OPE.20152303.0838.

SHAN Ze-biao, SHI Yao-wu, LIU Xiao-song etc. DOA tracking of moving targets by artificial bee colony algorithm[J]. Editorial Office of Optics and Precision Engineering, 2015,23(3): 838-845 DOI： 10.3788/OPE.20152303.0838.

摘要

针对目标信号源波达方向(DOA)的实时变化

将人工蜂群算法应用于最大似然函数的优化

实现了动态目标DOA的实时跟踪。首先

提出了一种可变遗忘因子的自适应样本协方差矩阵更新方法

该方法可根据目标信号源DOA变化的快慢自适应调整历史数据和当前采样数据在协方差矩阵中所占的权重

从而保证在获得较小稳态误差的同时又可获得较快的跟踪速度。然后

直接应用了性能优越的最大似然估计方法

避免了子空间跟踪类算法需要不断重复特征值或奇异值分解等问题。最后

采用人工蜂群仿生智能算法对似然函数的求解进行优化

从而极大地减少了算法的计算量

保证了算法的快速性和实时性。实验结果表明:在单快拍采样的情况下

信噪比为0 dB时

跟踪两个目标信号源的均方根误差为0.995 2°

基本达到了阵列信号处理中目标跟踪方法的设计要求。

Abstract

To track the real changes of dynamic target DOA (Direction of Arrival) quickly and accurately

the artificial bee colony theory and a corresponding algorithm are used to optimize the likelihood function and to implement the real time tracking of dynamic target DOA. First

an adaptive subspace updating algorithm with a variable forgetting factor is proposed

which could adjust adaptively the weights of current and historical data in a covariance matrix according to the DOA change speed and could obtain a smaller stable error while a better tacking speed. Then

by making use of the maximum likelihood algorithm with superior performance

this method avoids the repetitious feature values and singular value decomposition in the subspace tracking algorithms. Finally

the artificial bee colony algorithm is used to optimize the likelihood function and to reduce the computation of the algorithm. Experimental results on sampling in singe snapshot indicate that the Root Mean Square Error ( RMSE) of DOA estimation is 0.995 2° under tracking estimation two signal sources with a SNR of 0 dB. It satisfies the requirements of design for target tracking method in an array signal processing.

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references

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