Nonparametric nonline-of-sight identification

Recently, there has been much interest in accurate determination of mobile user locations in cellular environments. A general approach to this geolocation problem is to gather time-of-arrival measurements from a number of base stations (BSs) and to estimate user locations using the traditional least square approach. However, in nonline-of-sight (NLOS) situations, measurements are significantly biased. Hence, very large errors in location estimation may be introduced when traditional techniques are adopted. For this reason, before employing an algorithm for location estimation, it is useful to know which BS's are in line-of-sight (LOS) and which are in NLOS of the mobile station. In this paper, a nonparametric approach to this NLOS identification problem is proposed. Since the statistics of NLOS errors are usually unknown, a nonparametric probability density estimation technique is employed to approximate the distribution of the measurements. Then, an appropriate metric is used to determine the distance between the distribution of the measurements and the distribution of the measurement noise. Depending on the closeness of the distributions, the propagation environment is classified as LOS or NLOS. In a situation where reliability of measurements from a BS is to be quantified, the distance can be used to represent the reliability of the measurements as well as to classify the station.