Abstract
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.
Highlights
Recent advances in key technological innovations such as highperformance chips, 5G communication networks, and“Internet Protocol Version 6 (IPV6), has promoted the rapid development of pervasive computing technology (Bolad and Akcakoca 2018)
Spectral clustering and weighted back propagation neural network (SWBN) algorithm attempts to improve the accuracy of indoor positioning by using natural geomagnetic characteristics only
SWBN uses the spatial correlation of geomagnetism, divides the sub-fingerprint database through the reference point clustering algorithm of spectral clustering, and uses weighted backpropagation neural network positioning algorithm to predict the weighted coordinates of objects
Summary
Recent advances in key technological innovations such as highperformance chips, 5G communication networks, and“Internet Protocol Version 6 (IPV6), has promoted the rapid development of pervasive computing technology (Bolad and Akcakoca 2018). Common indoor positioning technologies include infrared positioning(Mohebbi, Stroulia, and Nikolaidis 2017), Bluetooth(Cao et al, n.d.), Ultra Wide Band (UWB)(Duanyang et al 2018), Radio Frequency Identification (RFID) (Seco and Jiménez 2018), Wi-Fi(Mendoza-Silva, Torres-Sospedra, and Huerta 2017), ultrasonic (Medina, Segura, and De la Torre 2013), microphone array(Funke et al 2014), among others These positioning methods are based on the propagation of wireless signals, which are easy to obtain and can be located using existing indoor wireless networks. In 2012, Stainford University EinenelM (Le Grand and Thrun 2012) developed an indoor magnetic fingerprint matching and positioning technology based on commercial intelligent terminals They tested it in a classroom and they were able to achieve a positioning accuracy of 0.7 m in a straight path and 1.2 m in a circular path. The paper is organized as follows: Section 1 introduces related research studies, Section 2 introduces the geomagnetic indoor positioning, Section 3 provides a detailed description of the algorithm, Section 4 presents experimental results and their analysis, and Section 5 draws some concluding remarks
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