基于组稀疏度的分布式传感器阵列网络目标定位

Qing Shen, Wei Liu, Li Wang, Yin Liu
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引用次数: 3

摘要

研究了分布式传感器阵列网络的目标定位问题,在压缩感知(CS)框架下,提出了一种基于群稀疏度的二维定位方法。它不是融合单独估计的到达角(AOAs),而是同时处理所有接收器收集的信息以形成最终目标位置。仿真结果表明,与常用的极大似然估计(MLE)相比,所提出的定位方法具有显著的性能提升。
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Group Sparsity Based Target Localization for Distributed Sensor Array Networks
The target localization problem for distributed sensor array networks where a sub-array is placed at each receiver is studied, and under the compressive sensing (CS) framework, a group sparsity based two-dimensional localization method is proposed. Instead of fusing the separately estimated angles of arrival (AOAs), it processes the information collected by all the receivers simultaneously to form the final target locations. Simulation results show that the proposed localization method provides a significant performance improvement compared with the commonly used maximum likelihood estimator (MLE).
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