Robust Bias Mitigation Algorithm for Localization in Wireless Networks

Non-line-of-sight (NLOS) signal propagation is a major issue in location estimation. Several application oriented approaches have been proposed in recent years to mitigate the errors induced by the biased distance estimates from NLOS propagation. In this paper, we present a new algorithm which reduces the adverse effects of bias by utilizing the topological diversity provided by L unique beacon combinations from the power set of N(>3) beacons. The algorithm consists of two complementary weighted average techniques combined to provide high level of robustness. Our simulation and empirical work shows that the proposed algorithm is effective independent of the bias distributions. Thus, it can be implemented over a variety of existing localization methods in wireless networks to mitigate the adverse effects of NLOS propagation and achieve high accuracy in practical scenarios.