A Neural Network Based Recursive Least Square Multilateration Technique for Indoor Positioning

Adhikari, Bhagawat; Fernando, Xavier

doi:10.1109/camad52502.2021.9617769

Cited by 3 publications

(2 citation statements)

References 15 publications

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“…The simulation result of this work shows that with the help of ANN, the ranging error is rapidly reduced solely with RSSI information. Adhikari et al also exploited the ANN with a recursive least-squares technique for reducing the positioning error using RSSI [30]. Their simulation results show an extraordinary improvement in terms of accuracy with median error down to 0.035 m. However, the method uses 10 APs, which is complex and costly.…”

Section: Ann-based Ipssmentioning

confidence: 99%

A Machine Learning Approach to Improve Ranging Accuracy with AoA and RSSI

Zhang

Kalathas

et al. 2022

Sensors

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Ranging accuracy is a critical parameter in time-based indoor positioning systems. Indoor environments often have complex structures, which make centimeter-level-accurate ranging a challenging task. This study proposes a new distance measurement method to decrease the ranging error in multipath environment. Our method uses an artificial neural network that utilizes the received signal strength indicator along with a signal’s angle of arrival to calculate the line-of-sight distance. This combination results in a significant reduction of the error caused by multipath effects that common RSSI-based methods suffer from. It outperforms traditional ranging methods while the implementation complexity is kept low.

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Section: Ann-based Ipssmentioning

confidence: 99%

A Machine Learning Approach to Improve Ranging Accuracy with AoA and RSSI

Zhang

Kalathas

et al. 2022

Sensors

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“…Bhagawat Adhikari et al proposed a neural network positioning model combined with recursive least squares. The model is simple, but the accuracy is limited [28]. Satish R. Jondhale and others proposed a generalised regression neural network model combined with a Kalman filter, which makes up for the lack of accuracy of the Kalman filter, but the amount of calculation is very large [29].…”

Section: Related Workmentioning

confidence: 99%

Height information aided 3D real‐time large‐scale underground user positioning

Tang

Zhang

et al. 2022

IET Radar Sonar & Navi

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Due to the cost of inertial navigation and visual navigation equipment and lake of satellite navigation signals, they cannot be used in large-scale underground mining environment. To solve this problem, this study proposes a large-scale underground 3D real-time positioning method with seam height assistance. This method uses the ultra wide band positioning base station as the core and is combined with seam height information to build a factor graph confidence transfer model to realise 3D positioning. The simulation results show that the proposed real-time method is superior to the existing algorithms in positioning accuracy and can meet the needs of large-scale underground users.

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