Hybrid RSS/AOA Localization using Approximated Weighted Least Square in Wireless Sensor Networks

Kang, Seyoung; Kim, TaeHyun; Chung, Wonzoo

doi:10.3390/s20041159

Cited by 34 publications

(46 citation statements)

References 22 publications

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“…Therefore, great efforts have been devoted to improve their accuracy by applying a weighting matrix to the LS solution, so called weighted LS (WLS). In [9,[16][17][18], a weighting covariance matrix is derived based on noise statistics. However, since noise statistics are not available, they need to be estimated first.…”

Section: Introductionmentioning

confidence: 99%

“…In other applications such as localization and tracking of first responders, RSSI and AOA information is collected by the anchors and processed at a data fusion center. In this case, target positioning using hybrid AOA/RSSI data can be cast as a nonconvex optimization problem [ 9 ]. Solutions of this problem can be categorized into optimization-based and least square (LS) estimators [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

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Unbalanced Hybrid AOA/RSSI Localization for Simplified Wireless Sensor Networks

Tran

Huang

et al. 2020

Sensors

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Source positioning using hybrid angle-of-arrival (AOA) estimation and received signal strength indicator (RSSI) is attractive because no synchronization is required among unknown nodes and anchors. Conventionally, hybrid AOA/RSSI localization combines the same number of these measurements to estimate the agents’ locations. However, since AOA estimation requires anchors to be equipped with large antenna arrays and complicated signal processing, this conventional combination makes the wireless sensor network (WSN) complicated. This paper proposes an unbalanced integration of the two measurements, called 1AOA/nRSSI, to simplify the WSN. Instead of using many anchors with large antenna arrays, the proposed method only requires one master anchor to provide one AOA estimation, while other anchors are simple single-antenna transceivers. By simply transforming the 1AOA/1RSSI information into two corresponding virtual anchors, the problem of integrating one AOA and N RSSI measurements is solved using the least square and subspace methods. The solutions are then evaluated to characterize the impact of angular and distance measurement errors. Simulation results show that the proposed network achieves the same level of precision as in a fully hybrid nAOA/nRSSI network with a slightly higher number of simple anchors.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Unbalanced Hybrid AOA/RSSI Localization for Simplified Wireless Sensor Networks

Tran

Huang

et al. 2020

Sensors

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“…However, as WSNs were originally not used for location-awareness services, the challenge lies in dealing with the measurement errors in RSS and AOA measurements. To improve the accuracy of target position estimation, hybrid localization algorithms combining both RSS and AOA measurements have been recently investigated in [ 18 , 19 , 20 , 21 , 22 , 23 , 24 ].…”

Section: Introductionmentioning

confidence: 99%

Target Localization with Unknown Transmit Power and Path-Loss Exponent Using a Kalman Filter in WSNs

Kang

Kim

Chung

2020

Sensors

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We present a novel hybrid localization algorithm for wireless sensor networks in the absence of knowledge regarding the transmit power and path-loss exponent. Transmit power and the path-loss exponent are critical parameters for target localization algorithms in wireless sensor networks, which help extract target position information from the received signal strength. In the absence of information on transmit power and path-loss exponent, it is critical to estimate them for reliable deployment of conventional target localization algorithms. In this paper, we propose a simultaneous estimation of transmit power and path-loss exponent based on Kalman filter. The unknown transmit power and path-loss exponent are estimated using a Kalman filter with the tentatively estimated target position based solely on angle information. Subsequently, the target position is refined using a hybrid method incorporating received signal strength measurements based on the estimated transmit power and path-loss exponent. Our proposed algorithm accurately estimates transmit power and path-loss exponent and yields almost the same target position accuracy as the simulation results confirm, as the hybrid target localization algorithms with known transmit power and path-loss exponent. Simulation results confirm the proposed algorithm achieves 99.7% accuracy of the target localization performance with known transmit power and path-loss exponent, even in the presence of severe received signal strength measurement noise.

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“…본 논문에서는 보다 정확도를 높이기 위하여 다음과 같이 참고문헌 [5]에서 사용한 error covariance 기반의 weighted least squares 방식을 사용하고자 한다.…”

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“…여기서         은 각 측정치 (RSS, AOA) 에러로 인하여 생긴 파라미터 에러를 나타내고, 표적과 노드의 위치와 식 (1)과 식 (2)에서 정의된 각각의 측정잡음의 식으로 풀 이될 수 있으나, 본 논문에서는 LS 기법을 사용함으로 자 세한 전개는 불필요하여 생략한다[5] . 식 (3)에 들어가는 보조 변수는 다음과 같이 정의된다.…”

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Least-Squares Approach Using Moving-Averaged Signals for Transmit Power and Path Loss Exponent Estimations in Wireless Sensor Networks

Kang¹,

Kim²,

Chung³

2020

J. Korean Inst. Electromagn. Eng. Sci.

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In this paper, we present an estimation scheme for the transmit power(TP) and path loss exponent(PLE) for hybrid target localization in wireless sensor networks. In the absence of knowledge on the TP and PLE, hybrid target localization schemes that utilize both received signal strength (RSS) and angle of arrival(AOA) measurements have significantly reduced performances. To estimate the TP and PLE in the presence of a severe measurement noise, moving-averaged RSS measurements are applied to a least-squares algorithm utilizing a tentatively estimated target position using an AOA-only algorithm.

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Hybrid RSS/AOA Localization using Approximated Weighted Least Square in Wireless Sensor Networks

Cited by 34 publications

References 22 publications

Unbalanced Hybrid AOA/RSSI Localization for Simplified Wireless Sensor Networks

Unbalanced Hybrid AOA/RSSI Localization for Simplified Wireless Sensor Networks

Target Localization with Unknown Transmit Power and Path-Loss Exponent Using a Kalman Filter in WSNs

Least-Squares Approach Using Moving-Averaged Signals for Transmit Power and Path Loss Exponent Estimations in Wireless Sensor Networks

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