Abstract
Non-line-of-sight (NLOS) identification and mitigation carry significant importance in wireless localization systems. In this paper, we propose a novel NLOS identification technique based on the multipath channel statistics such as the kurtosis, the mean excess delay spread, and the root-mean-square delay spread. In particular, the IEEE 802.15.4a ultrawideband channel models are used as examples and the above statistics are found to be well modeled by log-normal random variables. Subsequently, a joint likelihood ratio test is developed for line-of-sight (LOS) or NLOS identification. Three different weighted least-squares (WLSs) localization techniques that exploit the statistics of multipath components (MPCs) are analyzed. The basic idea behind the proposed WLS approaches is that smaller weights are given to the measurements which are likely to be biased (based on the MPC information), as opposed to variance-based WLS techniques in the literature. Accuracy gains with respect to the conventional least-squares algorithm are demonstrated via Monte-Carlo simulations and verified by theoretical derivations.