NLOS identification and mitigation for localization based on UWB experimental data

By Stefano Maranò, Wesley M. Gifford, Henk Wymeersch, Moe Z. Win. IEEE Journal on Selected Areas in Communications, 2010

656 citations recorded by the source corpus.

Sensor networks can benefit greatly from location-awareness, since it allows information gathered by the sensors to be tied to their physical locations. Ultra-wide bandwidth (UWB) transmission is a promising technology for location-aware sensor networks, due to its power efficiency, fine delay resolution, and robust operation in harsh environments. However, the presence of walls and other obstacles presents a significant challenge in terms of localization, as they can result in positively biased distance estimates. We have performed an extensive indoor measurement campaign with FCC-compliant UWB radios to quantify the effect of non-line-of-sight (NLOS) propagation. From these channel pulse responses, we extract features that are representative of the propagation conditions. We then develop classification and regression algorithms based on machine learning techniques, which are capable of: (i) assessing whether a signal was transmitted in LOS or NLOS conditions; and (ii) reducing ranging error caused by NLOS conditions. We evaluate the resulting performance through Monte Carlo simulations and compare with existing techniques. In contrast to common probabilistic approaches that require statistical models of the features, the proposed optimization-based approach is more robust against modeling errors.

Subfields: Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/9f16a150ab7fd51b4d506868a9185f3292165605

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In Electrical and Electronic Engineering

Citations in the corpus

Accurate Passive Location Estimation Using TOA Measurements (2012, neutral): “In the second step, we remove the assumption and update the estimation results.”
A Novel NLOS Mitigation Algorithm for UWB Localization in Harsh Indoor Environments (2019, methodology): “Compared with the SVM based method in [29], when using the respective optimal feature sets, the difference of overall identification performance between two methods is very small.”
A Machine Learning Approach to Ranging Error Mitigation for UWB Localization (2012, methodology): “In our recent contribution [45], we hav e evaluated anon-parametricapproach to NLOS identification, followed by NLOS mitigation, based directly on measured UWB waveforms.”
Kalman-Filter-Based Integration of IMU and UWB for High-Accuracy Indoor Positioning and Navigation (2020, background): “UWB is a communication technology that uses nanosecond nonsinusoidal narrow pulse signal to transmit data, it has become an effective transmission technology in location-aware sensor networks [11].”