Target tracking with sensor navigation using coupled RSS and AOA measurements

Slavisa Tomic, Marko Beko, Rui Dinis, João Pedro Gomes

Research output: Contribution to journalArticle

11 Citations (Scopus)
11 Downloads (Pure)

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.

Original languageEnglish
Article number2690
JournalSensors (Switzerland)
Volume17
Issue number11
DOIs
Publication statusPublished - 21 Nov 2017

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navigation
Target tracking
Navigation
sensors
Sensors
Nonlinear Dynamics
Bayes Theorem
Observation
Wireless sensor networks
arrivals
Defects
exponents
defects
simulation

Keywords

  • Angle of arrival (AoA)
  • Kalman filter (KF)
  • Maximum a posteriori (MAP) estimator
  • Received signal strength (RSS)
  • Sensor navigation
  • Target tracking

Cite this

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title = "Target tracking with sensor navigation using coupled RSS and AOA measurements",
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.",
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Target tracking with sensor navigation using coupled RSS and AOA measurements. / Tomic, Slavisa; Beko, Marko; Dinis, Rui; Gomes, João Pedro.

In: Sensors (Switzerland), Vol. 17, No. 11, 2690, 21.11.2017.

Research output: Contribution to journalArticle

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AU - Tomic, Slavisa

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AU - Dinis, Rui

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