NLOS Identification and Mitigation for Mobile Tracking

Yu, Kegen; Dutkiewicz, Eryk

doi:10.1109/taes.2013.6557997

Cited by 54 publications

(49 citation statements)

References 29 publications

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“…We adopt the highfrequency measurements [29], the reliable data. Therefore, at time k, we can obtain M range measurementsẑ n k = z 1 n k , … ,ẑ M n k between the target and the nth beacon node.…”

Section: Proposed Nlos Localization Methodsmentioning

confidence: 99%

Mean Shift-Based Mobile Localization Method in Mixed LOS/NLOS Environments for Wireless Sensor Network

Wang

et al. 2017

Journal of Sensors

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Mobile localization estimation is a significant research topic in the fields of wireless sensor network (WSN), which is of concern greatly in the past decades. Non-line-of-sight (NLOS) propagation seriously decreases the positioning accuracy if it is not considered when the mobile localization algorithm is designed. NLOS propagation has been a serious challenge. This paper presents a novel mobile localization method in order to overcome the effects of NLOS errors by utilizing the mean shift-based Kalman filter. The binary hypothesis is firstly carried out to detect the measurements which contain the NLOS errors. For NLOS propagation condition, mean shift algorithm is utilized to evaluate the means of the NLOS measurements and the data association method is proposed to mitigate the NLOS errors. Simulation results show that the proposed method can provide higher location accuracy in comparison with some traditional methods.

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Section: Proposed Nlos Localization Methodsmentioning

confidence: 99%

Mean Shift-Based Mobile Localization Method in Mixed LOS/NLOS Environments for Wireless Sensor Network

Wang

et al. 2017

Journal of Sensors

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“…The precondition of using the proposed algorithm is that it is necessary to assume the sampling frequency of distance measurement is larger than 1, which is 10 Hz, and the localization frequency is set as 1 Hz in this paper. The idea is referenced from the high-frequency distance measurement data processing in base station localization [22].…”

Section: Measurement Data Preprocessing Methods Based On Voting Selectmentioning

confidence: 99%

“…Abumansoor et al [21] propose a cooperative localization algorithm to mitigate the NLOS error. Yu and Dutkiewicz [22] proposes the high-frequency distance measurement data processing in base station localization. The proposed algorithm could significantly improve position accuracy.…”

Section: Introductionmentioning

confidence: 99%

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Vote selection mechanisms and probabilistic data association-based mobile node localization algorithm in mixed LOS/NLOS environments

Liu

et al. 2015

Telecommun Syst

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As one of the key technologies of wireless sensor networks (WSNs), the localization of mobile nodes (MN) is one of the most significant research topics in WSNs. When a line-of-sight (LOS) channel is available, accuracy localization result can be obtained. Motivated by the fact that the non-line-of-sight (NLOS) propagation of signal is ubiquitous and decreases the accuracy of localization, we propose a MN localization algorithm in mixed LOS/NLOS environments. Considering the characteristics of NLOS error, we propose a localization algorithm based on vote selection mechanisms to filter the distance measurements and reserve the reliable measurements. Then a modified probabilistic data association algorithm is proposed to fuse the multiple measurements reserved from the vote selection. The position of the MN is finally determined by a linear least squares algorithm based on reference nodes selection. This algorithm effectively mitigates various kinds of NLOS errors and largely improves the localization accuracy of the MN in mixed LOS/NLOS environments. The simulation and experiments results show that the proposed algorithm has better robustness and higher localization accuracy than other methods. Electronic supplementary materialThe online version of this article

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“…The non-line of sight identification techniques have been discussed extensively in the literature, but mainly within the cellular network framework [7][8][9][10][11][12][13]. For example, in [10], the authors address the NLOS identification problems based on the multiple received signal strength measurements from Wi-Fi signals.…”

Section: Introductionmentioning

confidence: 99%

NLOS Identification Based on Energy Receiver Using IEEE 802.15.3c

Zhang¹,

Zhang²,

Liang³

2016

Proceedings of the 2015 4th National Conference on Electrical, Electronics and Computer Engineering

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Abstract. Non-line-of-sight (NLOS) is the major problem for the indoor localization. So as to deal with NLOS propagation, a novel NLOS identification method is proposed based on the skewness and slope of the (energy detection) ED-based received energy block. IEEE 802.15.3c 60 GHz channel models are used as examples to be explained in detail. The proposed approach relies on the ED-based parameters which make it simple. Numerical simulations results show that the accuracy of NLOS identification up to 80% which is higher than other ED-based NLOS identification.

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NLOS Identification and Mitigation for Mobile Tracking

Cited by 54 publications

References 29 publications

Mean Shift-Based Mobile Localization Method in Mixed LOS/NLOS Environments for Wireless Sensor Network

Mean Shift-Based Mobile Localization Method in Mixed LOS/NLOS Environments for Wireless Sensor Network

Vote selection mechanisms and probabilistic data association-based mobile node localization algorithm in mixed LOS/NLOS environments

NLOS Identification Based on Energy Receiver Using IEEE 802.15.3c

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