Bayesian methodology for target tracking using combined RSS and AoA measurements
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Data
2017
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Editora
Elsevier
Resumo
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 existing one, reducing the estimation error for about 25%.
Descrição
Physical Communication 25 (2017) pág.158–166
Palavras-chave
TARGET TRACKING, RECEIVED SIGNAL STRENGTH, ANGLE OF ARRIVAL, KALMAN FILTER, ENGENHARIA ELETROTÉCNICA, ELECTROTECHNICAL ENGINEERING, FILTRO DE KALMAN, TARGET TRACKING, RECEIVED SIGNAL STRENGTH, ANGLE OF ARRIVAL