基于稀疏表示的信号DOA估计  

Sparse representation perspective for source localization based on JSL_0-SVD

作　　者：冯莹莹[1] 程向阳[1] 邓明[2] 

机构地区：[1]阜阳师范学院信息工程学院,安徽阜阳236041 [2]阜阳师范学院计算机与信息学院,安徽阜阳236041

出　　处：《计算机应用研究》2013年第2期537-540,共4页Application Research of Computers

基　　金：安徽高校省级科学研究项目(KJ2012B138);安徽省质量工程项目(20101985)

摘　　要：将信号DOA的估计问题转换为一个联合稀疏表示的求解问题。通过对接收数据矩阵的奇异值分解实现各时间和频率快拍数据的联合;然后通过求解一个平滑l0范数稀疏约束的联合优化问题实现信号源DOA的估计。基于稀疏表示的信号DOA估计方法不仅能够有效地减少数据量,而且具有以下优点:更好的抗噪声性能、更高的计算效率、适用于相关和非相关信号。通过与其他DOA估计方法的比较,表明了该方法的有效性和优越性。The source localization problem was cast as the problem of recovering a joint sparse representation. It used the sin- gular value decomposition of the data matrix to summarize multiple time and frequency samples, then imposed the smoothed l0 norm to enforce sparsity and used a fixed-point iteration approach to solve the joint optimization problem. The proposed algo- rithm has the following advantages： improved robustness to noise, improved computation efficiency, robustness to limited num- ber of samples, robustness to correlated sources, no requirement of accurate initialization. The performance of the proposed method was compared to standard spectrum based approaches and other sparse based methods.

关 键 词：到达角 奇异值分解 联合稀疏 平滑l0范数 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]