Please use this identifier to cite or link to this item: http://hdl.handle.net/11144/3653
Title: Bayesian methodology for target tracking using combined RSS and AoA measurements
Authors: Dinis, Rui
Tomic, Slavisa
Beko, Marko
Tuba, Milan
Bacanin, Nebojsa
Keywords: Target tracking
Received signal strength (RSS)
Angle of arrival (AoA)
Maximum a posteriori (MAP) estimator
Kalman filter (KF)
Extended KF (EKF)
Unscented KF (UKF)
Particle filter (PF)
Issue Date: 2017
Publisher: ELSEVIER
Abstract: This work addresses the target tracking problem based on received signal strength (RSS) and angle of arrival (AoA) measurements. The Bayesian methodology, which integrates the information given by observations with prior knowledge extracted from target motion model in order to enhance the estimation accuracy was employed. First, by converting the considered highly non-linear measurement model into a linear one, i.e., a novel linearization technique of the measurement model is proposed. The derived model is then merged with the prior knowledge, and a novel maximum a posteriori (MAP) estimator whose solution is given in closed-form is proposed. It is also shown that the Kalman filter (KF) can be directly applied on top of the linearized observation model, which results in a proposal of a novel KF algorithm. Furthermore, to the best of authors’ knowledge, this paper premierly presents the application of the extended KF (EKF) and the unscented KF (UKF) to the considered tracking problem, by applying first-order linearization technique to the original non-linear model, and by applying the unscented transformation to carefully selected sample points, respectively. Finally, importance weights are computed for a large number of randomly selected sample points to render a well-known particle filter (PF) solution. Simulation results show that the proposed algorithms perform better than a naive one which uses only information from observations. They also confirm the effectiveness of the proposed linearization technique in comparison with the exist
Peer reviewed: yes
URI: http://hdl.handle.net/11144/3653
metadata.dc.identifier.doi: 10.1016/j.phycom.2017.10.005
ISSN: 1874-4907
Publisher version: https://www.sciencedirect.com/science/article/pii/S1874490717300551?via%3Dihub
Appears in Collections:DCT - Artigos/Papers

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