Direct Localisation using Ray-tracing and Least-Squares Support Vector Machines

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

doi:10.1109/icl-gnss.2018.8440915

Cited by 7 publications

(6 citation statements)

References 5 publications

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“…AOA error for an MPC in a dense multipath environment can lead to a totally different path between the BS and the MS. An increase in such errors will significantly impact the performance of the scheme. More details on this experiment are contained in [ 3 ].…”

Section: Resultsmentioning

confidence: 99%

“…Use of ground reflected paths or any other mitigated NLOS paths results in growing position errors. Identification of LOS paths is covered in [ 3 ], together with the subsequent mitigation of NLOS propagation. When the correct AOA is obtained in a LOS link, the source of error then becomes, mainly, the time delay error, which produces the range between the BS and MS.…”

Section: Resultsmentioning

confidence: 99%

“…When the correct AOA is obtained in a LOS link, the source of error then becomes, mainly, the time delay error, which produces the range between the BS and MS. Measurement errors and the positive bias of this delay can be reduced by using the mitigation techniques discussed in [ 3 ] and thus the localisation performance can be improved. In a LOS environment TOA/AOA was determined to perform better than the Direct method at any level of acceptable outage.…”

Section: Resultsmentioning

confidence: 99%

“…The three environments considered in [ 3 ] are the city center, city peripheral area, and open area (park), but for comparison with the TOA and TDOA localisation schemes, the city center and the park are chosen to represent a dense multipath environment (NLOS), and a LOS scenario, respectively. These are shown in Figure 6 below.…”

Section: Experimental Setup and Methodologymentioning

confidence: 99%

“…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 ]. We also compare the performance of both approaches in different environments.…”

Section: Introductionmentioning

confidence: 99%

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

Chitambira

Armour²,

Wales³

et al. 2018

Sensors

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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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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Experimental Setup and Methodologymentioning

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

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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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Self-Localization Algorithms for Wireless Sensor Networks in the Smart City

Peled-Eitan

Greenberg

2021

2021 IEEE International Conference on Microwaves, Antennas, Communications and Electronic Systems (COMCAS)

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Direct Localisation using Ray-tracing and Least-Squares Support Vector Machines

Cited by 7 publications

References 5 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

Self-Localization Algorithms for Wireless Sensor Networks in the Smart City

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