GMM-based localization algorithm under NLOS conditions

Aiming at indoor node localizations of WSN,a node localization algorithm,where priori-knowledge is not necessary,was proposed.on basis of analyzing the error model,combined with Gaussian mixture model (GMM).By training the distance measurements containing NLOS errors,the more accurate range estimati...

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Bibliographic Details
Published inTongxin Xuebao Vol. 35; pp. 99 - 106
Main Authors Wei CUI, Cheng-dong WU, Yun-zhou ZHANG, Zi-xi JIA, Long CHENG
Format Journal Article
LanguageChinese
Published Editorial Department of Journal on Communications 01.01.2014
Subjects
NLOS;RSSI;residual weighting algorithm;particle swarm optimization algorithm;Gaussian mixture model
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Summary:Aiming at indoor node localizations of WSN,a node localization algorithm,where priori-knowledge is not necessary,was proposed.on basis of analyzing the error model,combined with Gaussian mixture model (GMM).By training the distance measurements containing NLOS errors,the more accurate range estimations can be obtained.For higher localization accuracy,the particle swarm optimization (PSO) was introduced to optimize the expectation-maximization (EM)algorithm.Finally,by using the residual weighting algorithm to estimate the distance,the estimation coordinates of target nodes can be determined.The proposed algorithm was proved to be effective through simulation experiments.
ISSN:1000-436X
DOI:10.3969/j.issn.1000-436x.2014.01.012