IEEE Transactions on Industrial Electronics · 2011 · 118 citations · 44 references
ComputingInformation TechnologyMobile SystemsSensor FusionWireless CommunicationsBayesian FilteringKalman FilteringLocalization UncertaintySensor Fusion For LocalizationState EstimationState-space ModelsData FusionDiscrete-time Markov ChainsDiscrete-time Markov ProcessesLocation EstimationMarkov Jump ProcessesMarkov Jump SystemsNonlinear EstimationPosition Estimation
In this paper, we consider the problem of mobile location estimation in a rough wireless environment. To mitigate the non-line-of-sight (NLOS) effects, the transition between the line of sight and the NLOS is described by a discrete-time Markov chain. The multiple-model approach is applied first time for locating a mobile station (MS), and a location estimation algorithm is developed by employing the basic interacting-multiple-model approach to a jump Markov system with two independent switching parameters. The data fusion of the time of arrival and the received signal strength measurements is utilized, and the cubature Kalman filter is applied for nonlinear estimation. Simulations are presented to illustrate the effectiveness of the proposed algorithm involving locating an MS with maneuvers.
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Ienkaran Arasaratnam, S. Haykin · IEEE Transactions on Automatic Control · 2009 · 3.3K citations
Computing, Computational Engineering, Bayesian Data Analysis +11