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Wireless localization has a great importance in a variety of areas including commercial, service, and military positioning and tracking systems. In harsh indoor environments, it is hard to localize an agent with high accuracy due to non-line-of-sight (NLOS) radio blockage or insufficient information from anchors. Therefore, NLOS identification and mitigation are highlighted as an effective way to improve the localization accuracy. In this paper, we develop a robust and efficient algorithm to enhance the accuracy for (ultrawide bandwidth) time-of-arrival localization through identifying and mitigating NLOS signals with relevance vector machine (RVM) techniques. We also propose a new localization algorithm, called the two-step iterative (TSI) algorithm, which converges fast with a finite number of iterations. To enhance the localization accuracy as well as expand the coverage of a localizable area, we continue to exploit the benefits of RVM in both classification and regression for cooperative localization by extending the TSI algorithm to a centralized cooperation case. For self-localization setting, we then develop a distributed cooperative algorithm based on variational Bayesian inference to simplify message representations on factor graphs and reduce communication overheads between agents. In particular, we build a refined version of Gaussian variational message passing to reduce the computational complexity while maintaining the localization accuracy. Finally, we introduce the notion of a stochastic localization network to verify proposed cooperative localization algorithms.
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Thang Van Nguyen
Youngmin Jeong
Hyundong Shin
IEEE Journal on Selected Areas in Communications
Massachusetts Institute of Technology
Kyung Hee University
Decision Systems (United States)
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Nguyen et al. (Wed,) studied this question.
www.synapsesocial.com/papers/69dcc934f3d3790cb7133d1f — DOI: https://doi.org/10.1109/jsac.2015.2430191