NLOS Identification and Mitigation for Geolocation Using Least-Squares Support Vector Machines

Chitambira, Benny; Armour, Simon; Wales, S.W.; Beach, M.A.

doi:10.1109/wcnc.2017.7925566

Cited by 14 publications

(13 citation statements)

References 11 publications

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“…TDOA uses 3 Base stations, and one should be the same BS that is used by the other schemes which only require one BS. Detailed information on these algorithms, is provided in [ 2 ].…”

Section: Experimental Setup and Methodologymentioning

confidence: 99%

“…They were first proposed by Suykens and Vendewalle [ 15 ]. They are typically used for classification and regression as is the case in [ 2 , 16 ]. The regression capability is exploited in many applications that require function estimation.…”

Section: Experimental Setup and Methodologymentioning

confidence: 99%

“…From these, those that are determined to be LOS are grouped separately to those determined to be NLOS. We determined in [ 2 ] that a training data size of at least 3000 data points was sufficient to produce high performing tuning parameters, but in this study, we use data points N = 10,000. These datapoints consist of half LOS and half NLOS MPCs.…”

Section: Experimental Setup and Methodologymentioning

confidence: 99%

“…The use of Least-Square Support Vector Machines (LSSVMs) to address the challenges of localisation in NLOS environments is becoming popular. Already LSSVMs have been shown to be effective for NLOS identification and mitigation in urban environments [ 2 ]. In this article, we show a direct scheme, that provides for localisation of the mobile station, without needing to go through NLOS identification and mitigation processes [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

“…Ray tracing data for the greater city of Bristol is used in the study. We use NLOS identification and mitigation, and the localisation algorithms discussed in [ 2 ], to compare with the Direct method. Detailed discussion on other NLOS identification and mitigation techniques can be found in [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Employing Ray-Tracing and Least-Squares Support Vector Machines for Localisation

Chitambira

Armour²,

Wales³

et al. 2018

Sensors

Self Cite

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This article evaluates the use of least-squares support vector machines, with ray-traced data, to solve the problem of localisation in multipath environments. The schemes discussed concern 2-D localisation, but could easily be extended to 3-D. It does not require NLOS identification and mitigation, hence, it can be applied in any environment. Some background details and a detailed experimental setup is provided. Comparisons with schemes that require NLOS identification and mitigation, from earlier work, are also presented. The results demonstrate that the direct localisation scheme using least-squares support vector machine (the Direct method) achieves superior outage to TDOA and TOA/AOA for NLOS environments. TDOA has better outage in LOS environments. TOA/AOA performs better for an accepted outage probability of 20 percent or greater but as the outage probability lowers, the Direct method becomes better.

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“…TDOA uses 3 Base stations, and one should be the same BS that is used by the other schemes which only require one BS. Detailed information on these algorithms, is provided in [ 2 ].…”

Section: Experimental Setup and Methodologymentioning

confidence: 99%

Section: Experimental Setup and Methodologymentioning

confidence: 99%

Section: Experimental Setup and Methodologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Employing Ray-Tracing and Least-Squares Support Vector Machines for Localisation

Chitambira

Armour²,

Wales³

et al. 2018

Sensors

Self Cite

View full text Add to dashboard Cite

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A comparative study on NLOS error elimination methods based on channel measurement experiment

Chang

Wang²,

GAO³

et al. 2022

西北工业大学学报

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In order to study the performance of different elimination methods on the distance estimation forward error caused by the non-line-of-sight (NLOS) propagation of radio signals, this paper is based on the mean value, root mean square delay spread, skewness, kurtosis and peak-to-average ratio extracted from the channel state information (CSI), and combine it with the logarithmic estimated distance based on the time of arrival (TOA) as the feature input vector, through the establishment of Gaussian process regression (GPR), least square support vector machine regression (LS-SVMR) and BP neural network training model for experimental performance comparison. Through the actual measurement of the 2.4 to 5.4 GHz wireless propagation channel in the typical indoor environment, the error elimination experiment is carried out to compare the NLOS error elimination performance under different input characteristics, different bandwidths and different frequency bands. The experimental results show that the GPR model has the best NLOS error elimination performance, and the extracted CSI multi-features as the input of the GPR model can reduce the average absolute error and root mean square error by 71.12% and 81.36%, respectively. As the bandwidth continues to increase, the error elimination performance is gradually optimized. By increasing the bandwidth, the NLOS positioning error when the input features are less can be effectively improved. The positioning error of the low frequency band is smaller than that of the high frequency band under the multi-features, so the combination of all available frequency bands can eliminate the NLOS positioning error better than a single frequency band.

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An efficient positioning solution in urban canyons using enhanced extended Kalman particle filter

Jiang

2019

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Purpose This paper aims to propose a 3D-map aided tightly coupled positioning solution for land vehicles to reduce the errors caused by non-line-of-sight (NLOS) and multipath interference in urban canyons. Design/methodology/approach First, a simple but efficient 3D-map is created by adding the building height information to the existing 2D-map. Then, through a designed effective satellite selection method, the distinct NLOS pseudo-range measurements can be excluded. Further, an enhanced extended Kalman particle filter algorithm is proposed to fuse the information from dual-constellation Global Navigation Satellite Systems and reduced inertial sensor system. The dependable degree of each selected satellite is adjusted through fuzzy logic to further mitigate the effect of misjudged LOS and multipath. Findings The proposed solution can improve positioning accuracy in urban canyons. The experimental results evaluate the effectiveness of the proposed solution and indicate that the proposed solution outperforms all the compared counterparts. Originality/value The effect of NLOS and multipath is addressed from both the observation level and fusion level. To the authors’ knowledge, mitigating the effect of misjudged LOS and multipath in the fusion algorithm of tightly coupled integration is seldom considered in existing literature.

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NLOS Identification and Mitigation for Geolocation Using Least-Squares Support Vector Machines

Cited by 14 publications

References 11 publications

Employing Ray-Tracing and Least-Squares Support Vector Machines for Localisation

Employing Ray-Tracing and Least-Squares Support Vector Machines for Localisation

A comparative study on NLOS error elimination methods based on channel measurement experiment

An efficient positioning solution in urban canyons using enhanced extended Kalman particle filter

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