NLOS mitigation in TOA-based localization using semidefinite programming

Vaghefi, Reza Monir; Schloemann, Javier; Buehrer, R. Michael

doi:10.1109/wpnc.2013.6533288

Cited by 59 publications

(31 citation statements)

References 25 publications

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“…The algorithms proposed in [4][5][6][7]14,2] use a beacon that is fixed and aware of its location. All these methods assume that the node is unaware of its location.…”

Section: Related Workmentioning

confidence: 99%

“…Several localization algorithms based on received signal strength (RSS) [2], direction of arrival (DOA) [3], time of arrival (TOA) [4,5] and time difference of arrival (TDOA) [6,7] are employed to perform localization in wireless sensor networks. The RSS based method is cost effective and ubiquitous when compared to other measurement techniques.…”

Section: Introductionmentioning

confidence: 99%

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Sensor node tracking using semi-supervised Hidden Markov Models

2015

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“…The algorithms proposed in [4][5][6][7]14,2] use a beacon that is fixed and aware of its location. All these methods assume that the node is unaware of its location.…”

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Sensor node tracking using semi-supervised Hidden Markov Models

2015

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“…NLOS scenarios occur when there is an obstruction between transmitters and receivers located in indoor environments and outdoor situations such as urban areas. In general, the research *Correspondence: jchang@hanyang.ac.kr 2 Department of Electronic Engineering, Hanyang University, Seoul 133-791, Republic of Korea Full list of author information is available at the end of the article fields of localization for the LOS/NLOS mixture problem can be categorized into two parts: (1) the constrained least squares (LS) method using optimization such as the semidefinite relaxation and second-order cone relaxation [9][10][11][12] and (2) localization using robust statistics. While localization using the optimization method has comparatively high accuracy, the computational load is higher than that of the analytical solution.…”

Section: Introductionmentioning

confidence: 99%

Robust time-of-arrival source localization employing error covariance of sample mean and sample median in line-of-sight/non-line-of-sight mixture environments

Park

2016

EURASIP J. Adv. Signal Process.

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We propose a line-of-sight (LOS)/non-line-of-sight (NLOS) mixture source localization algorithm that utilizes the weighted least squares (WLS) method in LOS/NLOS mixture environments, where the weight matrix is determined in the algebraic form. Unless the contamination ratio exceeds 50 %, the asymptotic variance of the sample median can be approximately related to that of the sample mean. Based on this observation, we use the error covariance matrix for the sample mean and median to minimize the weighted squared error (WSE) loss function. The WSE loss function based on the sample median is utilized when statistical testing supports the LOS/NLOS state, while the WSE function using the sample mean is employed when statistical testing indicates that the sensor is in the LOS state. To testify the superiority of the proposed methods, the mean square error (MSE) performances are compared via simulation.

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“…Here |E| denotes the number of observed distances. Even for the case that there is just one unknown source, the SDP model in [37], which aims to simultaneously locate one single source and the NLOS biases, has (m + 1) of 3 × 3 positive semidefinite constraints and m linear constraints, where m is the number of anchors and it also equals the number of observed distances. When |E| grows beyond a few hundreds, the complexity of those models would significantly slow down the state-of-art SDP solvers.…”

mentioning

confidence: 99%

“…Consequently, the NLOS mitigation which does not require a priori the NLOS connection status or NLOS error information is necessary in practical applications. The SDP models discussed above in [7], [37] do not require any prior information about NLOS. Some studies such as the recent one by Yousefi et.…”

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confidence: 99%

Convex Euclidean distance embedding for collaborative position localization with NLOS mitigation

Ding

2016

Comput Optim Appl

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One of the challenging problems in collaborative position localization arises when the distance measurements contain Non-Line-Of-Sight (NLOS) biases. Convex optimization has played a major role in modelling such problems and numerical algorithm developments. One of the successful examples is the Semi-Definite Programming (SDP), which translates Euclidean distances into the constraints of positive semidefinite matrices, leading to a large number of constraints in the case of NLOS biases. In this paper, we propose a new convex optimization model that is built upon the concept of Euclidean Distance Matrix (EDM). The resulting EDM optimization has an advantage that its Lagrangian dual problem is well structured and hence is conducive to algorithm developments. We apply a recently proposed 3-block alternating direction method of multipliers to the dual problem and tested the algorithm on some real as well as simulated data of large scale. In particular, the EDM model significantly outperforms the SDP model and several others. Index TermsEuclidean distance matrix, Collaborative localization, Non-Line of Sight (NLOS), Augmented Lagrangian, Alternating direction method of multipliers (ADMM).

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NLOS mitigation in TOA-based localization using semidefinite programming

Cited by 59 publications

References 25 publications

Sensor node tracking using semi-supervised Hidden Markov Models

Sensor node tracking using semi-supervised Hidden Markov Models

Robust time-of-arrival source localization employing error covariance of sample mean and sample median in line-of-sight/non-line-of-sight mixture environments

Convex Euclidean distance embedding for collaborative position localization with NLOS mitigation

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