A Survey on TOA Based Wireless Localization and NLOS Mitigation Techniques

Güvenç, İsmail; Chong, Chia‐Chin

doi:10.1109/surv.2009.090308

Cited by 946 publications

(535 citation statements)

References 47 publications

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“…In range-based measurement localization, the major task is to find the accurate position in non-line-of-sight (NLOS) paths. These range-based measurements may include time-of-arrival (TOA) [6], time-difference-ofarrival (TDOA) [7], angle-of-arrival (AOA) [8], and received signal strength (RSS). After evaluating the distance between the nodes, the position of the blind node can be obtained based on three edge-measuring or maximum likelihood methods [9].…”

Section: Related Workmentioning

confidence: 99%

A Monte Carlo localization method based on differential evolution optimization applied into economic forecasting in mobile wireless sensor networks

Miao

Zhu

2018

J Wireless Com Network

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The localization of sensor node is an essential problem for many economic forecasting applications in wireless sensor networks. Considering that the mobile sensors change their locations frequently over time, Monte Carlo localization algorithm utilizes the moving characteristics of nodes and employs the probability distribution function (PDF) in the previous time slot to estimate the current location by using a weighted particle filter. However, it also has the problem of insufficient number of valid samples, which further affects the node's localization accuracy. In this paper, differential evolution method is introduced into the Monte Carlo localization algorithm. The sample weight is taken as the objective function, and differential evolution algorithm is implemented in sample stage. Finally, the node position is estimated by making the sample close to the actual location of the node instead of being filtered out. The simulation results demonstrate that the proposed algorithmprovidesabetterpositionestimationwithlesslocalizationerror.

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Section: Related Workmentioning

confidence: 99%

A Monte Carlo localization method based on differential evolution optimization applied into economic forecasting in mobile wireless sensor networks

Miao

Zhu

2018

J Wireless Com Network

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“…1-(b), due to ranging errors [2]- [6]. To evaluate the TOA-based location estimation scenario [7] in respectively. …”

Section: System Model In Toa Measurementsmentioning

confidence: 99%

“…Random noise errors are caused by various environmental conditions such as channel fading and multipath [6]. A variety of localization methods have been designed and widely studied to improve the performance of TOA-based localization in wireless sensor networks, as reported in [7]. In this paper, contrary to conventional localization methods, we present an effective localization method with adaptive bias computation.…”

Section: Introductionmentioning

confidence: 99%

An Effective TOA-based Localization Method with Adaptive Bias Computation

Go¹

2016

Journal of IKEEE

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In this paper, we propose an effective time-of-arrival (TOA)-based localization method with adaptive bias computation in indoor environments. The goal of the localization is to estimate an accurate target's location in wireless localization system. However, in indoor environments, non-line-of-sight (NLOS) errors block the signal propagation between target device and base station. The NLOS errors have significant effects on ranging between two devices for wireless localization. In TOA-based localization, finding the target's location inside the overlapped area in the TOA-circles is difficult. We present an effective localization method using compensated distance with adaptive bias computation. The proposed method is possible for the target's location to estimate an accurate location in the overlapped area using the measured distances with subtracted adaptive bias. Through localization experiments in indoor environments, estimation error is reduced comparing to the conventional localization methods.

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“…al. [23], Stoica and Li [31], and Guvenc and Chong [16] (and references therein) for detailed documentation on those problems. We are only interested in the situations where there are usually two types of nodes: the anchors whose positions are known and the sensors (or sources) whose positions are to be estimated based on range-based measurements by, such as received signal strength, time-of-arrival or time-difference-of-arrival.…”

mentioning

confidence: 99%

“…After identifying NLOS connections, different optimization techniques can be employed to improve the localization performance. For the single-source network, the linear programming method and the weighted linear least squares method are proposed in [39] and [15], respectively (see the nice survey [16] for the overview of various NLOS identification techniques and optimization methods). For the collaborative localization, the convex SDP relaxation model and the distributed iterative parallel projection method are proposed in [36] and [20], respectively.…”

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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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A Survey on TOA Based Wireless Localization and NLOS Mitigation Techniques

Cited by 946 publications

References 47 publications

A Monte Carlo localization method based on differential evolution optimization applied into economic forecasting in mobile wireless sensor networks

A Monte Carlo localization method based on differential evolution optimization applied into economic forecasting in mobile wireless sensor networks

An Effective TOA-based Localization Method with Adaptive Bias Computation

Convex Euclidean distance embedding for collaborative position localization with NLOS mitigation

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