Robust positioning in NLOS environments using nonparametric adaptive kernel density estimation

The problem of locating a mobile station in a wireless network has been extensively investigated due to the growing need for reliable location-based services. In non-line-of-sight environments, the positioning accuracy of classical least-squares based solutions is inaccurate. In order to mitigate the effect induced by non-line-of-sight errors, we address a novel robust nonparametric approach. Herein, we first estimate the range error distribution using nonparametric adaptive kernel density estimation and then optimize the approximate log-likelihood function via a quasi-Newton method. In simulations, the proposed approach shows improved positioning accuracy when the non-line-of-sight contamination is high as compared to several other competitors. Furthermore, it requires only a small amount of computational time.