共形极化敏感阵列单元失效下的稳健参数估计

doi:10.19665/j.issn1001-2400.2023.03.018

摘要/Abstract

摘要：

传统极化-DOA参数估计方法在部分阵列单元失效的情况下,其估计性能会严重恶化甚至失效;同时,面临着日益复杂的电磁环境,部分阵列失效单元不可避免地引入错误数据,这对算法的稳健性是一个巨大挑战。针对以上问题,为了充分探究部分阵列单元失效和错误数据对算法参数估计性能的影响,在共形极化敏感阵列中考虑了部分单元完全失效和部分单元出错概率两种情况,提出了一种基于变分稀疏贝叶斯学习的稳健空域二维联合稀疏极化-DOA参数估计方法。首先利用信源的空域稀疏特性,建立基于共形极化敏感阵列包含错误数据的二维稀疏接收信号模型;其次,采用奇异值分解方法来降低阵列输出矩阵的维度,从而减小算法运算量;然后,利用变分稀疏贝叶斯学习算法来获得信源稳健的DOA估计;最后,通过模值约束算法获得信源的极化参数估计。仿真结果表明,在阵列单元失效的情况下,所提算法具有相对稳健的参数估计性能,具有较高的估计精度和角度分辨率。

关键词: 信号处理, 波达方向, 稀疏重构, 变分稀疏贝叶斯学习

Abstract:

The estimation performance of traditional polarization-direction of arrival (DOA) parameter estimation methods will deteriorate seriously or even fail when some array elements fail.Meanwhile,error data from some failed array elements are introduced in the face of an increasingly complex electromagnetic environment.It is also a significant challenge to the robustness of the methods.For the above issues,to fully explore the impact of some array element failure and error data on the parameter estimation performance of the methods,two cases of partial elements complete failure and partial elements error probability are considered in the conformal polarization sensitive array (CPSA),and a spatially two-dimensional joint sparse polarization-DOA parameter estimation method based on variational sparse Bayesian learning (VSBL) is proposed.First,a two-dimensional sparse received array signal model based on the CPSA is established by using spatial sparse characteristics of the source.Second,the singular value decomposition method is used to reduce the dimension of the array output matrix,so as to reduce the computation load of the method.Subsequently,a robust DOA estimation is obtained by using the VSBL.Finally,the polarization parameter estimation of the source is obtained by the modulus constraint method.Simulation results validate that the proposed method has a relatively more robust parameter estimation performance and a higher estimation accuracy and a higher angle resolution in the case of array element failure.

Key words: signal processing, direction of arrival, sparse reconstruction, variational sparse Bayesian learning

中图分类号:

TN957.51

蓝晓宇,姜来,耿莽河,王宇鹏. 共形极化敏感阵列单元失效下的稳健参数估计[J]. 西安电子科技大学学报, 2023, 50(3): 192-201.

LAN Xiaoyu,JIANG Lai,GENG Manghe,WANG Yupeng. Estimation of robust parameter in the presence of conformal polarization sensitive array element failure[J]. Journal of Xidian University, 2023, 50(3): 192-201.

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算法	计算复杂度
文中算法	O(3J³-2J²+48J²MK²)
SBL	O(J³+18J²MK²)
OGSBL	O(J³+J²T+18J²MK²)

算法	文中算法	OGSBL	SBL
阵元完全失效产生错误数据下单次平均运行时间	9.275 4	7.936 7	1.531 4
阵元以概率形式生成错误数据下单次平均运行时间	9.736 5	8.850 6	1.682 6