Analysis of Wireless Localization in Nonline-of-Sight Conditions

Liu, Dawei; Lee, Moon-Chuen; Pun, Chi‐Man; Liu, Hongli

doi:10.1109/tvt.2013.2244928

Cited by 45 publications

(19 citation statements)

References 35 publications

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“…In [11], the mean square error (MSE) and variances of estimated distances are applied to NLOS identification namely measurements with larger variances are considered NLOS. Measurements are classified into LOS and NLOS according to several features of them by using the least squares SVM in [12].…”

Section: Related Workmentioning

confidence: 99%

A novel strategy of localization based on EKF for mobile robot

Zhang

Huang

Yuan

et al. 2014

Proceedings of the 33rd Chinese Control Conference

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This paper focuses on the accurate localization based on WSN (wireless sensor network) for indoor mobile robot using NLOS (non-line of sight) identification. For the traditional localization three measurement circles from observation are usually obtained to estimate the robot position, which brings up the larger errors in positioning. In order to resolve this problem, the position distributions calculated from multiple measurements are used to estimate the mobile robot location. Intersections of each two measurement circles are computed and distributions are fitted by using Gaussian. Expectations are taken as the observation values for extended Kalman Filter (EKF), which is applied to optimizing of localization. Also NLOS identifications are proposed to find the potential NLOS measurements which will severely deteriorate observation results. Efficiency of the presented localization algorithm with NLOS identification is illustrated via simulation experiments.

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Section: Related Workmentioning

confidence: 99%

A novel strategy of localization based on EKF for mobile robot

Zhang

Huang

Yuan

et al. 2014

Proceedings of the 33rd Chinese Control Conference

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show abstract

“…These methods capitalise on this physical topology to provide trigonometric solutions to the location of the MS, but are principally limited by channel noise. Chief among the culprits of channel noise is that which comes from the multipath, or non-line of sight (NLOS), environment (Liu, Lee, Pun, & Liu, 2013).…”

Section: Introductionmentioning

confidence: 99%

A computationally efficient approach for hidden-Markov model-augmented fingerprint-based positioning

Roth

Tummala

McEachen

2015

International Journal of Systems Science

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This paper presents a computationally efficient approach for mobile subscriber position estimation in wireless networks. A method of data scaling assisted by timing adjust is introduced in fingerprint-based location estimation under a framework which allows for minimising computational cost. The proposed method maintains a comparable level of accuracy to the traditional case where no data scaling is used and is evaluated in a simulated environment under varying channel conditions. The proposed scheme is studied when it is augmented by a hidden-Markov model to match the internal parameters to the channel conditions that present, thus minimising computational cost while maximising accuracy. Furthermore, the timing adjust quantity, available in modern wireless signalling messages, is shown to be able to further reduce computational cost and increase accuracy when available. The results may be seen as a significant step towards integrating advanced position-based modelling with power-sensitive mobile devices.

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“…The environmental noise is a result of the nonline of sight (NLoS) conditions and the dynamic environmental variations characterizing the indoor environments. The NLoS conditions are considered the main source of errors in localization systems [4]. To elaborate on this, the NLoS conditions occur when the RF signals travel through obstructed paths causing them to get reflected, refracted, and diffracted by obstacles.…”

Section: Introductionmentioning

confidence: 99%

Error reduction in distance estimation of RSS propagation models using Kalman filters

Abusara

Hassan

2015

2015 6th International Conference on Modeling, Simulation, and Applied Optimization (ICMSAO)

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In this paper, we propose an indoor localization system that integrates the received signal strength (RSS) correction methods with probabilistic propagation models. The proposed system aims to achieve accurate modeling of signals' propagation inside buildings without the need for expensive site surveys. This is achieved by eliminating the environmental noise causing temporal variations in the RSS measurements, using the Kalman filtering technique. In addition, we use Gaussian Process Regression (GPR) to model the signal propagation inside buildings as a function of distance. Our experimental results support our argument that GPR models outperform the conventional path-loss model. In addition, our results also show that integrating Kalman filters with GPR models improves the accuracy of distance estimation by almost 2 meters.

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Analysis of Wireless Localization in Nonline-of-Sight Conditions

Cited by 45 publications

References 35 publications

A novel strategy of localization based on EKF for mobile robot

A novel strategy of localization based on EKF for mobile robot

A computationally efficient approach for hidden-Markov model-augmented fingerprint-based positioning

Error reduction in distance estimation of RSS propagation models using Kalman filters

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