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.
Highlights
The location of a mobile terminal (MT) can be estimated using different parameters of a received signal, such as the time-of-arrival (TOA), angle-of-arrival (AOA), and/or the received signal strength (RSS)
We proposed a novel NLOS identification technique that does not require a time history of range measurements as opposed to prior art techniques
It is relatively simple as compared to previous reported works. This technique does not assume that the MT has to be in motion, which was commonly assumed in the literature
Summary
The location of a mobile terminal (MT) can be estimated using different parameters of a received signal, such as the time-of-arrival (TOA), angle-of-arrival (AOA), and/or the received signal strength (RSS). If the direct path between a fixed terminal (FT) (An FT is usually a base station in a cellular network or an anchor node in a sensor network.) and the MT is being obstructed, the TOA of the signal to the FT will be delayed, which introduces a positive bias. Using such NLOS TOA estimates during the localization of the MT position may significantly degrade the positioning accuracy. FTs that are under the NLOS condition have to be identified and their effects have to be mitigated
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