Non-line-of-sight mitigation in TOA-based RTLS using partially linear model

Location-awareness is becoming an essential aspect of wireless networks. However, the non-line-of-sight (NLOS) environment with the presence of walls and other obstacles that result in positively biased estimations, which present a significant challenge for traditional multilateral techniques. In this paper, a robust estimator based on a partially linear model is proposed to improve the localization accuracy in the case of the NLOS environment. By employing linearization of multilateral ranging equations, the position estimation can be obtained without prior knowledge about the density of measurement errors. In order to evaluate the performance of the provided methodology, Monte Carlo simulations and practical experiments have been carried out. Comparing with the existing method, the proposed approach can achieve high localization accuracy especially when NLOS contamination is high.