Target Tracking with Sensor Navigation Using Coupled RSS and AoA Measurements
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Data
2017
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Editora
MDPI
Resumo
This work addresses the problem of tracking a signal-emitting mobile target in wireless
sensor networks (WSNs) with navigated mobile sensors. The sensors are properly equipped to
acquire received signal strength (RSS) and angle of arrival (AoA) measurements from the received
signal, while the target transmit power is assumed not known. We start by showing how to linearize
the highly non-linear measurement model. Then, by employing a Bayesian approach, we combine
the linearized observation model with prior knowledge extracted from the state transition model.
Based on the maximum a posteriori (MAP) principle and the Kalman filtering (KF) framework,
we propose new MAP and KF algorithms, respectively. We also propose a simple and efficient mobile
sensor navigation procedure, which allows us to further enhance the estimation accuracy of our
algorithms with a reduced number of sensors. Model flaws, which result in imperfect knowledge
about the path loss exponent (PLE) and the true mobile sensors’ locations, are taken into consideration.
We have carried out an extensive simulation study, and our results confirm the superiority of the
proposed algorithms, as well as the effectiveness of the proposed navigation routine.
Descrição
Sensors
Palavras-chave
TARGET TRACKING, RECEIVED SIGNAL STRENGTH, ANGLE OF ARRIVAL, KALMAN FILTER, ENGENHARIA ELETROTÉCNICA, ELECTROTECHNICAL ENGINEERING, TARGET TRACKING, FILTRO DE KALMAN, RECEIVED SIGNAL STRENGTH, ANGLE OF ARRIVAL