摘要
针对密集分布目标的波达方向(DOA)估计,是当前高精度定位技术的难点和热点.已有的基于压缩感知原理的DOA估计方法往往存在离散网格与连续域参数匹配难度高、离散网格之间相关性高、计算效率低等问题.针对DOA密集分布、低信噪比的非理想情况,分别采用稀疏参数法(SPA)和连续压缩传感(CCS)算法,设计了无网格的压缩感知密集DOA估计方法,分析了这两种算法的性能特点.通过对比仿真实验证明:该方法可以有效提高密集DOA的估计精度.
Efficient direction of arrival(DOA)estimation for densely distributed targets was a difficult and hot spot in current high-precision positioning technology.There were many problems and issuesfor existing DOA estimation methods which were designed based on the compressed sensing theory,such as the difficulty of matching the discrete grids with parameters in continuous domain,the high correlation among discrete grids,and the low computational efficiency.In this paper concerning the non-ideal situation of dense DOA distribution and low signal-to-noise ratio,gridless compressed sensing dense DOA estimation methods were designed using sparse and parametric approach(SPA)algorithm and continuous compressed sensing(CCS)algorithm respectively.The performance of these two algorithms for dense DOA estimation were analyzed.The simulation experiments showed that the method designed in this paper could effectively improve the estimation precision of dense DOA.
作者
顾旭
魏爽
李莉
苏颖
GU Xu;WEI Shuang;LI Li;SU Ying(College of Information,Mechanical and Electrical Engineering,Shanghai Normal University,Shanghai 201418,China)
出处
《上海师范大学学报（自然科学版）》
2020年第1期76-82,共7页
Journal of Shanghai Normal University(Natural Sciences)
