Robust Bias Mitigation for Localization in Wireless Networks

Localization of a wireless device or a sensor node has been a problem of greatimportance in recent years. Location services are becoming an essential part of thetraditional wireless networks as well as the wireless sensor networks. Indoor localizationin particular has been an issue of interest in recent studies since GPS is not suitable forindoor environments. The main issue in target node location estimation is the distanceestimates affected by the non-line-of-sight (NLOS) signal measurements. In any practicalsetting, the NLOS errors are inevitable and need to be mitigated in order to achieveacceptable accuracy. In this work, first a theoretical analysis of effect of bias on thelocalization accuracy is performed. Then, a novel technique is presented which reducesthe adverse effects of bias by utilizing the topological diversity provided by the uniquebeacon combinations from the power set of N(>3) beacons. The proposed approachconsists of two complementary weighted average methods combined to provide a highlevel of robustness. The simulation and empirical results show that the presentedtechnique is effective independent of the bias distribution. Thus it can be implemented ontop of variety of existing localization methods in wireless networks to mitigate theadverse effects of NLOS propagation and achieve high accuracy in a practical scenario.