High-accuracy localization in harsh environments is a challenging research problem, mainly due to non-lineof-sight (NLOS) propagation, multipath effect, and multiuser interference. Many techniques have been proposed to address this problem; most of them focus on improving the accuracy of ranging estimation, e.g., NLOS identification and mitigation. In this paper, we take ranging one step further by introducing the concept of ranging likelihood (RL), showing that RL is the essential element for localization. Moreover, we present effective techniques for real-time RL estimation. We focus on ultra-wide bandwidth (UWB) localization systems and assess the performance of the proposed approach by using the data from an extensive indoor measurement campaign. The results show that the proposed approach can significantly improve the performance of wireless localization in harsh environments.

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Received signal strength (RSS)-based localization has been widely used in location-aware applications due to its low cost and low complexity. The accuracy of RSS-based localization depends on the values of parameters used in the path-loss model, which is specific to the operating environment. Given the dependence, however, most work assumes the pathloss parameters are fixed and known, an assumption that costs positioning accuracy. In this paper, we estimate the target position and the path-loss parameters jointly for an arbitrary distribution of noise. We formulate the estimation problem as an optimization that has a prominent geometric structure. To achieve any level of positioning accuracy, as measured by the outage on the distance error, we prove that the anchors must be placed in certain ways and that the sample size of RSSs must be large enough. The method to place anchors and a guaranteed sample size are given in the paper. The proposed framework has practical utility to localization in unknown environments when the target node must be simple and yet able to supply accurate positioning.