王彤,,
吴建新
西安电子科技大学雷达信号处理国家重点实验室 西安 710071
基金项目:国家自然科学基金(61471285)
详细信息
作者简介:王娟：女，1987年生，博士生，研究方向为阵列信号处理、空时自适应处理、广域GMTI
王彤：男，1974年生，教授，研究方向为合成孔径雷达成像、机载雷达运动目标检测
吴建新：男，1982年生，副教授，研究方向为阵列信号处理、自适应信号处理、空时自适应处理
通讯作者:王彤 twang@mail.xidian.edu.cn
中图分类号:TN959.73计量
文章访问数:1990
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被引次数:0
出版历程
收稿日期:2019-03-18
修回日期:2019-08-30
网络出版日期:2019-09-04
刊出日期:2020-02-19
Iterative Multiple Signal Classification Algorithm with Small Sample Size
Juan WANG,Tong WANG,,
Jianxin WU
National Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, China
Funds:The National Natural Science Foundation of China (61471285)
摘要
摘要:当样本数不足时，由采样协方差矩阵特征分解得到的噪声子空间偏离其真实值，使得多重信号分类(MUSIC)算法目标角度(DOA)估计性能下降。为了解决这个问题，该文提出了一种迭代算法通过校正信号子空间来提高MUSIC算法性能。该方法首先利用采样协方差矩阵特征分解得到的噪声子空间粗略估计目标角度；其次基于信源的稀疏性和导向矢量的低秩特性，由上一步得到的目标角度以及其邻域角度对应的导向矢量构造一个新的信号子空间；最后通过解一个优化问题来校正信号子空间。仿真结果表明，该算法有效地提高了子空间估计精度。基于新的信号子空间实现MUSIC DOA估计可以使得性能得到改善，且在低样本数下改善尤为明显。
关键词:目标角度估计/
多重信号分类算法/
迭代/
优化问题
Abstract:For cases with small samples, the estimated noise subspace obtained from sample covariance matrix deviates from the true one, which results in MUltiple SIgnal Classification (MUSIC) Direction-Of-Arrival (DOA) estimation performance breakdown. To deal with this problem, an iterative algorithm is proposed to improve the MUSIC performance by modifying the signal subspace in this paper. Firstly, the DOAs are roughly estimated based on the noise subspace obtained from sample covariance matrix. Then, considering the sparsity of signals and the low-rank property of steering matrix, a new signal subspace is got from the steering matrix consisting of estimated DOAs and their adjacent angles. Finally, the signal subspace is modified by solving an optimization problem. Simulation results demonstrate the proposed algorithm can improve the subspace estimation accuracy and furtherly improve the MUSIC DOA estimation performance, especially in small sample cases.
Key words:Direction-Of-Arrival (DOA) estimation/
MUltiple SIgnal Classification (MUSIC)/
Iterative/
Optimization problem
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