Decentralized indoor wireless localization using compressed sensing of signal-strength fingerprints

摘要: This paper combines recent developments in sparse approximation and distributed consensus theory to efficiently perform decentralized localization wireless networks. To this goal, we exploit the Compressed Sensing (CS) framework, which provides a new paradigm for recovering signals being some basis by means of limited amount random incoherent projections. In particular, propose novel technique that considers spatial correlations among received measurements at base stations (BSs) provide global accurate position estimation, while reducing significantly exchanged BSs required positioning. We common structure design gossip-based algorithm order alleviate effects radio channel-induced signal variations on estimation accuracy. Experimental evaluation with real data demonstrates superiority proposed CS-based over traditional fingerprinting methods terms achieved positioning