MagPP: Combining Particle Filters and Pedestrian Dead Reckoning Algorithm with Geomagnetism for Indoor Positioning Using Smartphone

Qiu, Kaiyue; Huang, Hung-Lung; Li, Wei; Luo, Dean

doi:10.18494/sam.2019.2460

Cited by 2 publications

(2 citation statements)

References 16 publications

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“…Three geomagnetic features were extracted during the experiment. Previous research experiments (Qiu et al 2019)showed that compared with the total magnetic strength alone, if the three-component geomagnetic values of x, y and z match, the accuracy is higher, so this paper stores x, Threecomponent geomagnetic value of y and z. The interpolation method is used to create three magnetic fingerprints, respectively in the direction of gravity, the horizontal direction and the composite direction of the magnetic field.…”

Section: To Construct a Fingerprint Map Of Geomagnetismmentioning

confidence: 99%

Geomagnetic Field-Based Indoor Positioning Using Back-Propagation Neural Networks

Qiu

Huang

El-Rabbany

2020

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. High-precision indoor positioning in complex environments has always been a hot research topic within the positioning and robotic communities. As one of the indoor positioning technologies, geomagnetic positioning is receiving widespread attention due to its global coverage. Additionally, geomagnetic positioning does not require special infrastructure configuration, its hardware cost is low, and its positioning errors do not accumulate over time. However, geomagnetic positioning is prone to mismatching, which causes serious problems at the positioning points. To tackle this challenge, this paper proposes an indoor localization method based on spectral clustering and weighted back-propagation neural network. The main research contribution is that in the offline phase, the spatial specificity of geomagnetism is used to define the similarity between fingerprints. In addition, a clustering-based reference point algorithm is proposed to divide the sub-fingerprint database, and a positioning prediction model based on back-propagation neural network is trained. Subsequently, in the online stage, the weights of different positioning prediction models are calculated according to the defined fingerprint similarity, weighted average prediction coordinates are obtained, and thereby the positioning accuracy is improved. Experimental results show that, in comparison with other neural network-based positioning methods, the positioning error of our proposed algorithm is reduced by approximately 26.6% and the positioning time is reduced by 24.7%. Experimental results show that the average positioning error of the algorithm is 1.81m.

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Section: To Construct a Fingerprint Map Of Geomagnetismmentioning

confidence: 99%

Geomagnetic Field-Based Indoor Positioning Using Back-Propagation Neural Networks

Qiu

Huang

El-Rabbany

2020

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…Signal strength-based Bluetooth positioning also fails to deliver high accuracy [ 9 , 10 ]. Magnetic positioning and pedestrian dead reckoning are often used in combination with other positioning methods due to the difficulty of achieving precise location estimates independently, resulting in limited positioning accuracy [ 11 , 12 ]. Distance-based UWB indoor positioning leverages non-sinusoidal narrow-pulse transmission data, offering advantages such as fast transmission speed, low power consumption, high positioning accuracy, and robust anti-interference capabilities [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

Snake optimizer LSTM-based UWB positioning method for unmanned crane

Wang,

Fan,

Wang

et al. 2023

PLoS ONE

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Position determination is a critical technical challenge to be addressed in the unmanned and intelligent advancement of crane systems. Traditional positioning techniques, such as those based on magnetic grating or encoders, are limited to measuring the positions of the main carriage and trolley. However, during crane operations, accurately determining the position of the load becomes problematic when it undergoes swinging motions. To overcome this limitation, this paper proposes a novel Ultra-Wide-Band (UWB) positioning method for unmanned crane systems, leveraging the Snake Optimizer Long Short-Term Memory (SO-LSTM) framework. The objective is to achieve real-time and precise localization of the crane hook. The proposed method establishes a multi-base station and multi-tag UWB positioning system using a Time Division Multiple Access (TDMA) combined with Two-Way Ranging (TWR) scheme. This system enables the acquisition of distance measurements between the mobile tag and UWB base stations. Furthermore, the hyperparameters of the LSTM network are optimized using the Snake Optimizer algorithm to enhance the accuracy and effectiveness of UWB positioning estimation. Experimental results demonstrate that the SO-LSTM-based positioning method yields a maximum positioning error of 0.1125 meters and a root mean square error of 0.0589 meters. In comparison to conventional approaches such as the least squares method (LS) and the Kalman filter method (KF), the proposed SO-LSTM-based positioning method significantly reduces the root mean square error (RMSE) by 63.39% and 58.01%, respectively, while also decreasing the maximum positioning error (MPE) by 60.77% and 52.65%.

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MagPP: Combining Particle Filters and Pedestrian Dead Reckoning Algorithm with Geomagnetism for Indoor Positioning Using Smartphone

Cited by 2 publications

References 16 publications

Geomagnetic Field-Based Indoor Positioning Using Back-Propagation Neural Networks

Geomagnetic Field-Based Indoor Positioning Using Back-Propagation Neural Networks

Snake optimizer LSTM-based UWB positioning method for unmanned crane

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