Estimating Directional Data From Network Topology for Improving Tracking Performance

Tomic, Slavisa; Beko, Marko; Dinis, Rui; Montezuma, Paulo

doi:10.3390/jsan8020030

Open AccessArticle

Estimating Directional Data From Network Topology for Improving Tracking Performance

by

Slavisa Tomic

^1,*

,

Marko Beko

^1,2

,

Rui Dinis

^3,4 and

Paulo Montezuma

^3,4

¹

COPELABS, Universidade Lusófona de Humanidades e Tecnologias, 1749-024 Lisboa, Portugal

²

UNINOVA, Monte de Caparica, 2829-516 Caparica, Portugal

³

Instituto de Telecomunicações, 1049-001 Lisboa, Portugal

⁴

Departamento de Engenharia Electrotécnica, Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, 2829-516 Caparica, Portugal

^*

Author to whom correspondence should be addressed.

J. Sens. Actuator Netw. 2019, 8(2), 30; https://doi.org/10.3390/jsan8020030

Submission received: 31 March 2019 / Revised: 24 April 2019 / Accepted: 13 May 2019 / Published: 20 May 2019

(This article belongs to the Special Issue Localization in Wireless Sensor Networks)

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Abstract

This work proposes a novel approach for tracking a moving target in non-line-of-sight (NLOS) environments based on range estimates extracted from received signal strength (RSS) and time of arrival (TOA) measurements. By exploiting the known architecture of reference points to act as an improper antenna array and the range estimates, angle of arrival (AOA) of the signal emitted by the target is first estimated at each reference point. We then show how to take advantage of these angle estimates to convert the problem into a more convenient, polar space, where a linearization of the measurement models is easily achieved. The derived linear model serves as the main building block on top of which prior knowledge acquired during the movement of the target is incorporated by adapting a Kalman filter (KF). The performance of the proposed approach was assessed through computer simulations, which confirmed its effectiveness in combating the negative effect of NLOS bias and superiority in comparison with its naive counterpart, which does not take prior knowledge into consideration.

Keywords: target tracking; non-line-of-sight (NLOS); received signal strength (RSS); time of arrival (TOA); angle of arrival (AOA); Kalman filter (KF)

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MDPI and ACS Style

Tomic, S.; Beko, M.; Dinis, R.; Montezuma, P. Estimating Directional Data From Network Topology for Improving Tracking Performance. J. Sens. Actuator Netw. 2019, 8, 30. https://doi.org/10.3390/jsan8020030

AMA Style

Tomic S, Beko M, Dinis R, Montezuma P. Estimating Directional Data From Network Topology for Improving Tracking Performance. Journal of Sensor and Actuator Networks. 2019; 8(2):30. https://doi.org/10.3390/jsan8020030

Chicago/Turabian Style

Tomic, Slavisa, Marko Beko, Rui Dinis, and Paulo Montezuma. 2019. "Estimating Directional Data From Network Topology for Improving Tracking Performance" Journal of Sensor and Actuator Networks 8, no. 2: 30. https://doi.org/10.3390/jsan8020030

APA Style

Tomic, S., Beko, M., Dinis, R., & Montezuma, P. (2019). Estimating Directional Data From Network Topology for Improving Tracking Performance. Journal of Sensor and Actuator Networks, 8(2), 30. https://doi.org/10.3390/jsan8020030

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

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Estimating Directional Data From Network Topology for Improving Tracking Performance

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