Selective hybrid RSS/AOA weighting algorithm for NLOS intra cell localization

Gazzah, Leïla; Najjar, Leila; Besbes, Hichem

doi:10.1109/wcnc.2014.6952789

Cited by 27 publications

(28 citation statements)

References 9 publications

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“…An LS and a maximum likelihood (ML) estimators for a hybrid scheme that merges RSS difference (RSSD) and AoA measurements were derived in [6] by employing nonlinear constrained optimization. In [7], a selective weighted LS (WLS) estimator for RSS/AoA localization problem was proposed. Another WLS estimator for RSSD/AoA localization problem was presented in [8].…”

Section: Introductionmentioning

confidence: 99%

Distributed RSS-AoA Based Localization With Unknown Transmit Powers

Tomic

Beko

Dinis

2016

IEEE Wireless Commun. Lett.

109

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Abstract-This letter addresses the hybrid range/angle-based target localization problem in a cooperative 3-D wireless sensor network where no central processor is available. Due to battery exhaust over time, sensors' transmit powers are assumed different and unknown. Range and angle measurements are drawn from the received signal strength and angle-of-arrival models, respectively. By exploiting the measurement models, we derive a novel local-estimator by which each target updates its own estimate, based on the least squares criterion. Second-order cone relaxation technique is then applied to approximately solve the attained problem due to its non-convex nature. Our simulation results show that the proposed algorithm efficiently solves the localization problem.Index Terms-Wireless localization, distributed localization, received signal strength (RSS), angle-of-arrival (AoA), second-order cone programming (SOCP), wireless sensor network (WSN).

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Section: Introductionmentioning

confidence: 99%

Distributed RSS-AoA Based Localization With Unknown Transmit Powers

Tomic

Beko

Dinis

2016

IEEE Wireless Commun. Lett.

109

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“…Figure studies the performance of the EHT, EHHT, WHHT, HT, HHT and Hybrid Weighting Least Squares (HWLS) methods against the number of LOS BSs including the situation of less than three LOS BSs. The HWLS method proposed in does not distinguish between NLOS and LOS measurements and applies weighting factors at all BSs, to correct the range measurements to their LOS values. A similar NLOS effect correction is applied on the AOA seen at the serving BS to correct the estimate AOA to their LOS values.…”

Section: Numerical Resultsmentioning

confidence: 99%

“…The MS coordinates Θ can be found using the weighting hybrid least squares approach, as in .

normalΘ = A_{1}^{- 1} b_{1}

where

A_{1} = ([], \begin{matrix} X_{2} - X_{1} & Y_{2} - Y_{1} \\ ⋮ & ⋮ \\ X_{6} - X_{1} & Y_{6} - Y_{1} \\ \sin (θ_{1}) & - \cos (θ_{1}) \\ ⋮ & ⋮ \\ \sin (θ_{6}) & - \cos (θ_{6}) \end{matrix}), and

b_{1} = \frac{1}{2} [\begin{matrix} α_{1}^{2} l_{1}^{2} - α_{2}^{2} l_{2}^{2} + k_{1} - k_{2} \\ ⋮ \\ α_{1}^{2} l_{1}^{2} - α_{6}^{2} l_{6}^{2} + k_{1} - k_{6} \\ X_{1} \sin (θ_{1}) - Y_{1} \cos (θ_{1}) \\ ⋮ \\ X_{6} \sin (θ_{6}) - Y_{6} \cos (θ<...> \end{matrix}]

…”

Section: Hybrid Hypothesis Test Using Weighting For Angle and Range Bmentioning

confidence: 99%

“…The HT identifies and mitigates the NLOS nodes by comparing the variance of the TOA estimation in LOS situation with the mean square error of the TOA estimation in LOS/NLOS situation . The third NLOS mitigation category, which was recently proposed in , does not distinguish between NLOS and LOS measurements and applies weighting factors at the three best nodes (best RSS), to correct the range measurements to their LOS values. A similar NLOS effect correction is applied on the AOA seen at BS.…”

Section: Introductionmentioning

confidence: 99%

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Hybrid RSS/AOA hypothesis test for NLOS/LOS base station discrimination and location error mitigation

Gazzah

Najjar

Besbes

2015

Trans. Emerging Tel. Tech.

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We address the problem of line of sight/no line of sight (LOS/NLOS) identification in a hybrid context where received signal strength and angle of arrival (RSS/AOA) measurements are combined. Two approaches are proposed, which aim to mitigate the range bias introduced by NLOS propagation. First, a hybrid hypothesis test (HHT) is proposed whose aim is to identify the most probable two Base Stations (BSs) to be LOS. The HHT considers an error function incorporating both RSS and AOA measurements. If the HHT succeeds in identifying two LOS BSs, then it directly proceeds to localisation. Otherwise, the HHT identifies, the two least probable BSs to be NLOS, over which a weighting approach will be applied. The weighting indeed serves to attenuate at a some degree, the NLOS propagation effect and then correct the location. In the first scenario, NLOS are completely discarded, whereas in the second one, their effect is attenuated, thus improving the location accuracy in both cases. Simulations results corroborate the expected performance enhancement. Fundamental performance limits for the localisation accuracy based on such hybrid measurements are evaluated through the corresponding Cramer–Rao lower bound. Copyright © 2015 John Wiley & Sons, Ltd.

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“…The range-based localization assumes that each anchor node is able to have the positions of the node and RSS as well as the AOA of the received signals and, hence, theoretically, the exact location of the target is attainable. However, the errors in RSS/AOA measurements are severe, and sophisticated integration of the RSS and AOA measurements has been investigated [17][18][19][20][21][22][23]. The challenge of the range-based localization algorithms is to overcome the limitation of the noisy RSS/AOA measurements with a practical computational complexity.…”

Section: Introductionmentioning

confidence: 99%

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

Kang

Kim

Chung

2020

Sensors

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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.

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Selective hybrid RSS/AOA weighting algorithm for NLOS intra cell localization

Cited by 27 publications

References 9 publications

Distributed RSS-AoA Based Localization With Unknown Transmit Powers

Distributed RSS-AoA Based Localization With Unknown Transmit Powers

Hybrid RSS/AOA hypothesis test for NLOS/LOS base station discrimination and location error mitigation

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

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