摘要
以多重信号分类(Multrple Signal Classification,MUSIC)算法为代表的现代空间谱估计方法,估计的信源数受限于阵列形式,并且需要的采样数据量巨大.文章从压缩感知的基础理论出发,利用目标信号空间分布的稀疏性,建立了基于压缩感知的阵列信号空间谱估计模型.利用压缩感知方法,可以使用较少的阵元数对空间信号进行采样测量,并准确重构信号.相比传统的MUSIC空间谱估计算法,该方法所需阵元数少,采样数据量小,并且能同时进行信号强度和角度的估计.所提方法对推动压缩感知理论在阵列信号空间谱估计中的应用具有一定意义.
Abstract The traditional major spatial spectrum estimation method is based on the mul- tiple signal classification （MUSIC） algorithm, which estimated signal source number is limited to the array element number and need large sampling data size. According to the compressive sensing basis theory and using the sparse distribution of the spatial signal source bearing, this paper constructs the spatial spectrum estimation model of the array signal processing based on compressive sensing theory. Utilizing compressive sensing method, the spatial signal can be sampled by little array elements, so with small sam- pling data, the signal amplitude and bearing can be estimated simultaneously. The method presented in this paper promots the application of compressive sensing in the array signal spatial spectrum estimation.
出处
《电波科学学报》
EI
CSCD
北大核心
2014年第1期150-157,共8页
Chinese Journal of Radio Science
关键词
空间谱估计
压缩感知
稀疏信号
正交匹配追踪算法
spatial spectrum estimation
compressive sensing
sparse signal
orthogonal matching pursuit algorithm
