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Title: Target Tracking With Sensor Navigation Using Coupled RSS and AoA Measurements
Authors: Dinis, Rui
Tomic, Slavisa
Beko, Marko
Gomes, João Pedro
Keywords: Target tracking
Sensor navigation
Received signal strength (RSS)
Angle of arrival (AoA)
Maximum a posteriori (MAP) estimator
Kalman filter (KF)
Issue Date: 2017
Publisher: MDPI
Citation: Tomic, S.; Beko, M.; Dinis, R.; Gomes, J.P. Target Tracking with Sensor Navigation Using Coupled RSS and AoA Measurements. Sensors 2017, 17, 2690.
Abstract: 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.
Peer Reviewed: yes
metadata.dc.identifier.doi: 10.3390/s17112690
ISSN: 1424-8220
Appears in Collections:DCT - Artigos/Papers

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