Bi-object Device-free Localization Based on Compressive Sensing

The time-varying characteristics of radio frequency signal make it difficult to practice multi-object Device-Free Localization (DFL). A novel algorithm based on compressive sensing and fingerprint method is proposed to locate bi-object in this paper. It utilizes link-centric probabilistic coverage model to construct the mapping relationship between single object radio map and bi-object radio map, which reduces the offline train labour brought for the increased number of objects. Furthermore, K-means clustering method is taken to classify the established bi-object radio map. By comparing online measurement with the centre elements of every cluster, the possible locations of the bi-object are limited to a smaller area, which shortens the computing time. Then, compressive sensing is adopted to transform the localization problem to a sparse signal reconstruction problem. Experiments confirm that the proposed algorithm outperforms than the Radio Tomographic Imaging (RTI) based algorithm.