3-D Target Localization in Wireless Sensor Networks Using RSS and AoA Measurements

Slavisa Tomic, Marko Beko, Rui Dinis

Research output: Contribution to journalArticle

66 Citations (Scopus)

Abstract

This paper addresses target localization problems in both noncooperative and cooperative 3-D wireless sensor networks (WSNs), for both cases of known and unknown sensor transmit power, i.e., PT. We employ a hybrid system that fuses distance and angle measurements, extracted from the received signal strength and angle-of-arrival information, respectively. Based on range and angle measurement models, we derive a novel nonconvex estimator based on the least squares criterion. The derived nonconvex estimator tightly approximates the maximum-likelihood estimator for small noise. We then show that the developed estimator can be transformed into a generalized trust region subproblem framework, by following the squared range approach, for noncooperative WSNs. For cooperative WSNs, we show that the estimator can be transformed into a convex problem by applying appropriate semidefinite programming relaxation techniques. Moreover, we show that the generalization of the proposed estimators for known PT is straightforward to the case where PT is not known. Our simulation results show that the new estimators have excellent performance and are robust to not knowing PT. The new estimators for noncooperative localization significantly outperform the existing estimators, and our estimators for cooperative localization show exceptional performance in all considered settings.

Original languageEnglish
Article number7509001
Pages (from-to)3197-3210
Number of pages14
JournalIEEE Transactions on Vehicular Technology
Volume66
Issue number4
DOIs
Publication statusPublished - Apr 2017

Fingerprint

RSS
3D
Wireless Sensor Networks
Wireless sensor networks
Angle measurement
Estimator
Target
Distance measurement
Electric fuses
Hybrid systems
Maximum likelihood
Sensors
Trust Region Subproblem
Angle of Arrival
Semidefinite Programming Relaxation
Angle
Received Signal Strength
Hybrid Systems
Maximum Likelihood Estimator
Range of data

Keywords

  • Angle-of-arrival (AoA)
  • generalized trust region subproblem (GTRS)
  • received signal strength (RSS)
  • semidefinite programming (SDP)
  • wireless localization
  • wireless sensor network (WSN)

Cite this

@article{71e37ed2cff84226b13e985e2c6cc74a,
title = "3-D Target Localization in Wireless Sensor Networks Using RSS and AoA Measurements",
abstract = "This paper addresses target localization problems in both noncooperative and cooperative 3-D wireless sensor networks (WSNs), for both cases of known and unknown sensor transmit power, i.e., PT. We employ a hybrid system that fuses distance and angle measurements, extracted from the received signal strength and angle-of-arrival information, respectively. Based on range and angle measurement models, we derive a novel nonconvex estimator based on the least squares criterion. The derived nonconvex estimator tightly approximates the maximum-likelihood estimator for small noise. We then show that the developed estimator can be transformed into a generalized trust region subproblem framework, by following the squared range approach, for noncooperative WSNs. For cooperative WSNs, we show that the estimator can be transformed into a convex problem by applying appropriate semidefinite programming relaxation techniques. Moreover, we show that the generalization of the proposed estimators for known PT is straightforward to the case where PT is not known. Our simulation results show that the new estimators have excellent performance and are robust to not knowing PT. The new estimators for noncooperative localization significantly outperform the existing estimators, and our estimators for cooperative localization show exceptional performance in all considered settings.",
keywords = "Angle-of-arrival (AoA), generalized trust region subproblem (GTRS), received signal strength (RSS), semidefinite programming (SDP), wireless localization, wireless sensor network (WSN)",
author = "Slavisa Tomic and Marko Beko and Rui Dinis",
note = "Sem PDF. Fundacao para a Ciencia e a Tecnologia (PEst-OE/EEI/UI0066/2014; UID/EEA/50008/2013; PTDC/EEI-TEL/6308/2014-HAMLeT; IF/00325/2015; SFRH/BPD/108232/2015; SFRH/BD/91126/2012)",
year = "2017",
month = "4",
doi = "10.1109/TVT.2016.2589923",
language = "English",
volume = "66",
pages = "3197--3210",
journal = "IEEE Transactions on Vehicular Technology",
issn = "0018-9545",
publisher = "IEEE Institute of Electrical and Electronics",
number = "4",

}

3-D Target Localization in Wireless Sensor Networks Using RSS and AoA Measurements. / Tomic, Slavisa; Beko, Marko; Dinis, Rui.

In: IEEE Transactions on Vehicular Technology, Vol. 66, No. 4, 7509001, 04.2017, p. 3197-3210.

Research output: Contribution to journalArticle

TY - JOUR

T1 - 3-D Target Localization in Wireless Sensor Networks Using RSS and AoA Measurements

AU - Tomic, Slavisa

AU - Beko, Marko

AU - Dinis, Rui

N1 - Sem PDF. Fundacao para a Ciencia e a Tecnologia (PEst-OE/EEI/UI0066/2014; UID/EEA/50008/2013; PTDC/EEI-TEL/6308/2014-HAMLeT; IF/00325/2015; SFRH/BPD/108232/2015; SFRH/BD/91126/2012)

PY - 2017/4

Y1 - 2017/4

N2 - This paper addresses target localization problems in both noncooperative and cooperative 3-D wireless sensor networks (WSNs), for both cases of known and unknown sensor transmit power, i.e., PT. We employ a hybrid system that fuses distance and angle measurements, extracted from the received signal strength and angle-of-arrival information, respectively. Based on range and angle measurement models, we derive a novel nonconvex estimator based on the least squares criterion. The derived nonconvex estimator tightly approximates the maximum-likelihood estimator for small noise. We then show that the developed estimator can be transformed into a generalized trust region subproblem framework, by following the squared range approach, for noncooperative WSNs. For cooperative WSNs, we show that the estimator can be transformed into a convex problem by applying appropriate semidefinite programming relaxation techniques. Moreover, we show that the generalization of the proposed estimators for known PT is straightforward to the case where PT is not known. Our simulation results show that the new estimators have excellent performance and are robust to not knowing PT. The new estimators for noncooperative localization significantly outperform the existing estimators, and our estimators for cooperative localization show exceptional performance in all considered settings.

AB - This paper addresses target localization problems in both noncooperative and cooperative 3-D wireless sensor networks (WSNs), for both cases of known and unknown sensor transmit power, i.e., PT. We employ a hybrid system that fuses distance and angle measurements, extracted from the received signal strength and angle-of-arrival information, respectively. Based on range and angle measurement models, we derive a novel nonconvex estimator based on the least squares criterion. The derived nonconvex estimator tightly approximates the maximum-likelihood estimator for small noise. We then show that the developed estimator can be transformed into a generalized trust region subproblem framework, by following the squared range approach, for noncooperative WSNs. For cooperative WSNs, we show that the estimator can be transformed into a convex problem by applying appropriate semidefinite programming relaxation techniques. Moreover, we show that the generalization of the proposed estimators for known PT is straightforward to the case where PT is not known. Our simulation results show that the new estimators have excellent performance and are robust to not knowing PT. The new estimators for noncooperative localization significantly outperform the existing estimators, and our estimators for cooperative localization show exceptional performance in all considered settings.

KW - Angle-of-arrival (AoA)

KW - generalized trust region subproblem (GTRS)

KW - received signal strength (RSS)

KW - semidefinite programming (SDP)

KW - wireless localization

KW - wireless sensor network (WSN)

UR - http://www.scopus.com/inward/record.url?scp=85018305452&partnerID=8YFLogxK

U2 - 10.1109/TVT.2016.2589923

DO - 10.1109/TVT.2016.2589923

M3 - Article

VL - 66

SP - 3197

EP - 3210

JO - IEEE Transactions on Vehicular Technology

JF - IEEE Transactions on Vehicular Technology

SN - 0018-9545

IS - 4

M1 - 7509001

ER -