中文摘要：

针对现有的基于压缩感知的目标定位算法,观测矩阵不满足约束等距性条件的问题,提出了一种基于StOMP的稀疏目标定位算法.该算法将基于网格的目标定位问题转化为压缩感知问题,首先利用QR分解得到满足约束等距性质的观测矩阵;然后利用StOMP重构算法实现稀疏定位.为了验证该算法的有效性,利用模拟无线传感器网络进行对比实验.结果表明,相比于基于orth预处理的稀疏目标定位算法,此算法在定位精度小幅提高的同时,很大程度上提升了目标定位的速度,具有较好的定位性能.

英文摘要：

In the existing target localization algorithms based on compressive sensing, the measurement matrixes don’t satisfy Restricted Isometry Property （RIP）.To solve this problem,a novel localization algorithm-sparse target localization based on StOMP is proposed.The proposed algorithm formulates the target localization problem with compressive sensing model via grid process. More specific,QR decomposition is first used to get a new measurement matrix which satisfies RIP property.Then StOMP is exploited to conduct sparse representation. The proposed method is comparison tested on a simulated network.The experimental results show that the proposed method has promising performance in terms of both target localization accuracy and processing speed, compared with sparse target localization based on orth preprocessing.