NLOS Identification and Weighted Least-Squares Localization for UWB Systems Using Multipath Channel Statistics

By Ismail Güvenç, Chia-Chin Chong, Fujio Watanabe, H. Inamura. EURASIP Journal on Advances in Signal Processing, 2008

345 citations recorded by the source corpus.

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.

Subfields: Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/3ad87fc5e8a1f5c6f6d5d61034e8c3e18a4ea613

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Citations in the corpus

A Machine Learning Approach to Ranging Error Mitigation for UWB Localization (2012, background): “This latter issue is critical for high-resolution localization systems [11], [12], [15], [20], [21], since NLOS propagation result s in positively biased range estimates [29], which in turn degra de localization performance.”
NLOS identification and mitigation for localization based on UWB experimental data (2010, methodology): “We observe that the LS-SVM, using the three features from [42], reduces the false alarm probability compared to the parametric technique.”
A Survey on TOA Based Wireless Localization and NLOS Mitigation Techniques (2009, methodology): “Hence, in [43], [44] an alternative weighting technique is proposed, which uses certain statistics of the multipath components of the received signals.”
The Integrated Sensing and Communication Revolution for 6G: Vision, Techniques, and Applications (2024, neutral): “If the LoS path is blocked, dedicated non-line-of-sight (NLoS) detection routines can detect this phenomenon and discard the corresponding measurements [95] or the NLoS paths can be used to solve the positioning problem [96].”
A Novel NLOS Mitigation Algorithm for UWB Localization in Harsh Indoor Environments (2019, background): “usually requires a joint probability distribution or thresholds [16]–[19].”
Network localization and navigation via cooperation (2011, methodology): “For example, the NLOS identification technique in [ 10 ] is based on signal features such as root mean square (RMS) delay-spread, Kurtosis, and mean excess delay.”
Modern WLAN Fingerprinting Indoor Positioning Methods and Deployment Challenges (2016, background): “Although some enhancements have been proposed for non-LOS (NLOS) conditions [49], [50], the localization errors are high [46].”
Cooperative Localization in Wireless Networks (2009, background): “3) When LOS/NLOS detection is performed, it can be based on some underlying statistics, say κj→i, extracted from the received signal [88], such as the mean excess delay.”