SA-M-SBL: An Algorithm for CSI-Based Device-Free Localization With Faulty Prior Information

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.