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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.

As an emerging technique, device-free localization (DFL) is promising to localize the target without attaching any transceivers. Recently, the benefits of channel state information (CSI) on DFL have been revealed in this paper. Motivated by this, in this paper, we propose to exploit the channel diversity of CSI measurements for multi-target DFL under the compressive sensing (CS) framework. The CSI-based multi-target DFL problem is formulated as a joint sparse recovery problem which reconstructs the unknown sparse vectors of multiple channels. Moreover, in practice, some faulty prior information (e.g., coarse positions) is usually available. To take advantage of this information for joint sparse recovery, novel support knowledge-aided multiple sparse Bayesian learning (SA-M-SBL) algorithm is introduced, which incorporates the prior information into a three-layer hierarchical prior model. With this model, the joint sparsity of the sparse vectors can be induced, and their values can be estimated via the variational Bayesian inference (VBI). The numerical simulation results demonstrate the outstanding performance of the proposed method compared with the state-of-the-art CS-based multi-target DFL methods.