Hybrid RSS/AOA localization using approximated weighted least square in wireless sensor networks

Seyoung Kang; Taehyun Kim; Wonzoo Chung

doi:10.3390/s20041159

Hybrid RSS/AOA localization using approximated weighted least square in wireless sensor networks

Seyoung Kang, Taehyun Kim, Wonzoo Chung

Department of Computer Science and Engineering

Research output: Contribution to journal › Article › peer-review

17 Citations (Scopus)

Abstract

We present a target localization method using an approximated error covariance matrix based weighted least squares (WLS) solution, which integrates received signal strength (RSS) and angle of arrival (AOA) data for wireless sensor networks. We approximated linear WLS errors via second-order Taylor approximation, and further approximated the error covariance matrix using a least-squares solution and the variance in measurement noise over the sensor nodes. The algorithm does not require any prior knowledge of the true target position or noise variance. Simulations validated the superior performance of our new method.

Original language	English
Article number	1159
Journal	Sensors (Switzerland)
Volume	20
Issue number	4
DOIs	https://doi.org/10.3390/s20041159
Publication status	Published - 2020 Feb 2

Keywords

Angle of arrival (AOA)
Received signal strength (RSS)
Target localization
Weighted least square (WLS)
Wireless sensor network (WSN)

ASJC Scopus subject areas

Analytical Chemistry
Information Systems
Atomic and Molecular Physics, and Optics
Biochemistry
Instrumentation
Electrical and Electronic Engineering

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10.3390/s20041159

Cite this

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abstract = "We present a target localization method using an approximated error covariance matrix based weighted least squares (WLS) solution, which integrates received signal strength (RSS) and angle of arrival (AOA) data for wireless sensor networks. We approximated linear WLS errors via second-order Taylor approximation, and further approximated the error covariance matrix using a least-squares solution and the variance in measurement noise over the sensor nodes. The algorithm does not require any prior knowledge of the true target position or noise variance. Simulations validated the superior performance of our new method.",

keywords = "Angle of arrival (AOA), Received signal strength (RSS), Target localization, Weighted least square (WLS), Wireless sensor network (WSN)",

author = "Seyoung Kang and Taehyun Kim and Wonzoo Chung",

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PY - 2020/2/2

Y1 - 2020/2/2

N2 - We present a target localization method using an approximated error covariance matrix based weighted least squares (WLS) solution, which integrates received signal strength (RSS) and angle of arrival (AOA) data for wireless sensor networks. We approximated linear WLS errors via second-order Taylor approximation, and further approximated the error covariance matrix using a least-squares solution and the variance in measurement noise over the sensor nodes. The algorithm does not require any prior knowledge of the true target position or noise variance. Simulations validated the superior performance of our new method.

AB - We present a target localization method using an approximated error covariance matrix based weighted least squares (WLS) solution, which integrates received signal strength (RSS) and angle of arrival (AOA) data for wireless sensor networks. We approximated linear WLS errors via second-order Taylor approximation, and further approximated the error covariance matrix using a least-squares solution and the variance in measurement noise over the sensor nodes. The algorithm does not require any prior knowledge of the true target position or noise variance. Simulations validated the superior performance of our new method.

KW - Angle of arrival (AOA)

KW - Received signal strength (RSS)

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KW - Weighted least square (WLS)

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ER -

Hybrid RSS/AOA localization using approximated weighted least square in wireless sensor networks

Abstract

Keywords

ASJC Scopus subject areas

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