Distributed multi-object localisation by consensus on compressive sampling received signal strength fingerprints

Abstract

Recent growing interest for location-based services has created a demand on object localisation approaches with low cost and high accuracy. In this study, the problem of distributed multi-object localisation using fingerprints of received signal strength (RSS) is addressed by combining average consensus and compressed sensing. First, Bayesian compressed sensing is employed at each agent to recover the sparse index vector from RSS measurements, which are corrupted by noises. It relaxes the requirement on accurate prior position knowledge of beacon nodes and is applicable in non-line-of-sight conditions. Then, average consensus is adopted to compel all agents to reach an agreement on the index vector, and in turn, on the location of objects. By using only one-hop neighbours' information, the proposed distributed localisation method is applicable to large-scale networks. Moreover, the final location of each object is obtainable from each individual agent, which makes the proposed method flexible to the network administration. Experimental results are included to demonstrate the effectiveness of the proposed method.