四川大学学报（自然科学版）2024，Vol.61Issue(3)：87-94,8.DOI:10.19907/j.0490-6756.2024.031006
一种包含组合范数惩罚项的波达方向稀疏估计方法
A sparse estimation method for DOA involving combined norm penalties
摘要
Abstract
The estimation of direction of arrival(DOA)is a fundamental problem in array data analysis.In the scenarios where the received array data is complex elliptically symmetrically distributed,the mainstream of sparse DOA estimation methods apply the l1-norm penalty to exploit the sparsity of DOA.In these meth-ods,the sparsity of signal is emphasized while the diversity of signal is unfortunately neglected.As a result,these methods have to overlook the weak signals(signals with low power).In this paper,we propose a novel penalized likelihood(model)method incorporating the combination of two norm penalties for the effective es-timation of DOAs including both sparse and weak signals.The combination of norm penalties is a linear com-bination of l1-norm penalty and l2-norm square penalty with independent combination coefficients(penalty pa-rameters),where the l2-norm square penalty term can help preserve the diversity of signal.Under the Majorization-Minimization(MM)algorithm framework,we design an algorithm to solve the model and prove its convergence.Finally,it is shown by a numerical experiment that the new method has higher estima-tion accuracy comparing with the known methods.关键词
阵列信号/波达方向/复椭球对称分布/惩罚似然估计/MM算法Key words
Array signals/Direction of arrival/Complex elliptically symmetric distribution/Penalized likeli-hood estimation/MM algorithm分类
数理科学引用本文复制引用
李宝山,徐海文,陈晨,李凡..一种包含组合范数惩罚项的波达方向稀疏估计方法[J].四川大学学报（自然科学版）,2024,61(3):87-94,8.基金项目
四川省科技厅项目(2021JDRC0080) （2021JDRC0080）
中央高校基本业务费面上项目(J2021-058) （J2021-058）
国家自然科学基金民航联合基金重点项目(U2033213) （U2033213）
