College of Communications Engineering, Army Engineering University of PLA, Nanjing 210007, China.
Sensors (Basel). 2018 Sep 14;18(9):3110. doi: 10.3390/s18093110.
Device-free localization (DFL) that aims to localize targets without carrying any electronic devices is addressed as an emerging and promising research topic. DFL techniques estimate the locations of transceiver-free targets by analyzing their shadowing effects on the radio signals that travel through the area of interest. Recently, compressive sensing (CS) theory has been applied in DFL to reduce the number of measurements by exploiting the inherent spatial sparsity of target locations. In this paper, we propose a novel CS-based multi-target DFL method to leverage the frequency diversity of fine-grained subcarrier information. Specifically, we build the dictionaries of multiple channels based on the saddle surface model and formulate the multi-target DFL as a joint sparse recovery problem. To estimate the location vector, an iterative location vector estimation algorithm is developed under the multitask Bayesian compressive sensing (MBCS) framework. Compared with the state-of-the-art CS-based multi-target DFL approaches, simulation results validate the superiority of the proposed algorithm.
无设备定位（DFL）旨在定位无需携带任何电子设备的目标，是一个新兴的、很有前途的研究课题。DFL 技术通过分析无线电信号在感兴趣区域内的传输过程中目标的阴影效应，来估计无收发器目标的位置。最近，压缩感知（CS）理论已被应用于 DFL 中，通过利用目标位置的固有空间稀疏性来减少测量次数。在本文中，我们提出了一种基于 CS 的多目标 DFL 方法，以利用细粒度子载波信息的频率多样性。具体来说，我们基于鞍面模型构建了多个通道的字典，并将多目标 DFL 表述为一个联合稀疏恢复问题。为了估计位置向量，我们在多任务贝叶斯压缩感知（MBCS）框架下开发了一种迭代位置向量估计算法。与最先进的基于 CS 的多目标 DFL 方法相比，仿真结果验证了所提出算法的优越性。
