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EM- and JMAP-ML Based Joint Estimation Algorithms for Robust Wireless Geolocation in Mixed LOS/NLOS Environments
Technical University of Darmstadt, Germany .
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
Technical University of Darmstadt, Germany .
2014 (English)In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 62, no 1, 168-182 p.Article in journal (Refereed) Published
Abstract [en]

We consider robust geolocation in mixed line-of-sight (LOS)/non-LOS (NLOS) environments in cellular radio networks. Instead of assuming known propagation channel states (LOS or NLOS), we model the measurement error with a general two-mode mixture distribution although it deviates from the underlying error statistics. To avoid offline calibration, we propose to jointly estimate the geographical coordinates and the mixture model parameters. Two iterative algorithms are developed based on the well-known expectation-maximization (EM) criterion and joint maximum a posteriori-maximum likelihood (JMAP-ML) criterion to approximate the ideal maximum-likelihood estimator (MLE) of the unknown parameters with low computational complexity. Along with concrete examples, we elaborate the convergence analysis and the complexity analysis of the proposed algorithms. Moreover, we numerically compute the Cramer-Rao lower bound (CRLB) for our joint estimation problem and present the best achievable localization accuracy in terms of the CRLB. Various simulations have been conducted based on a real-world experimental setup, and the results have shown that the ideal MLE can be well approximated by the JMAP-ML algorithm. The EM estimator is inferior to the JMAP-ML estimator but outperforms other competitors by far.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE) , 2014. Vol. 62, no 1, 168-182 p.
Keyword [en]
Cramer-Rao lower bound (CRLB); EM criterion; geolocation; JMAP-ML criterion; mixture distribution; non-line-of-sight (NLOS) mitigation
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:liu:diva-104838DOI: 10.1109/TSP.2013.2286779ISI: 000330291000014OAI: oai:DiVA.org:liu-104838DiVA: diva2:699610
Available from: 2014-02-28 Created: 2014-02-28 Last updated: 2017-12-05

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