Real-Time Recursive Fingerprint Radio Map Creation Algorithm Combining Wi-Fi and Geomagnetism

Seong, Ju-Hyeon; Seo, Dong-Hoan

doi:10.3390/s18103390

Cited by 3 publications

(2 citation statements)

References 27 publications

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“…The fingerprint method is a common method for Wi-Fi indoor positioning [26][27][28][29][30][44][45][46][47][48][49], and various factors affect positioning results [50,51]. In this paper, we discuss the effects of three typical user poses and body shadowing on Wi-Fi signals.…”

Section: Discussionmentioning

confidence: 99%

Improving Wi-Fi Fingerprint Positioning with a Pose Recognition-Assisted SVM Algorithm

et al. 2019

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The fingerprint method has been widely adopted for Wi-Fi indoor positioning. In the fingerprint matching process, user poses and user body shadowing have serious impact on the received signal strength (RSS) data and degrade matching accuracy; however, this impact has not attracted large attention. In this study, we systematically investigate the impact of user poses and user body shadowing on the collected RSS data and propose a new method called the pose recognition-assisted support vector machine algorithm (PRASVM). It fully exploits the characteristics of different user poses and improves the support vector machine (SVM) positioning performance by introducing a pose recognition procedure. This proposed method firstly establishes a fingerprint database with RSS and sensor data corresponding to different poses in the offline phase, and fingerprints of different poses in the database are extracted to train reference point (RP) classifiers of different poses and a pose classifier using an SVM algorithm. Secondly, in the online phase, the poses of RSS data measured online are recognised by a pose classifier, and RSS data measured online are grouped with different poses. Then online RSS data from each group at an unknown user location are reclassified as corresponding RPs by the RP classifiers of the corresponding poses. Finally, user location is determined by grouped RSS data corresponding to coordinates of the RPs. By considering the user pose and user body shadowing, the observed RSS data matches the fingerprint database better, and the classification accuracy of grouped online RSS data is remarkably improved. To verify performances of the proposed method, experiments are carried out: one in an office setting, and the other in a lecture hall. The experimental results show that the positioning accuracies of the proposed PRASVM algorithm outperform the conventional weighted k-nearest neighbour (WKNN) algorithm by 52.29% and 40.89%, outperform the SVM algorithm by 73.74% and 60.45%, and outperform the pose recognition-assisted WKNN algorithm by 34.76% and 21.86%, respectively. As a result, the PRASVM algorithm noticeably improves positioning accuracy.

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Section: Discussionmentioning

confidence: 99%

Improving Wi-Fi Fingerprint Positioning with a Pose Recognition-Assisted SVM Algorithm

et al. 2019

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“…Common fingerprint methods firstly collect the received signal strength (RSS) data and corresponding coordinate information as the fingerprint database at each reference point (RP) in the offline phase. In the online phase, the user’s location can be determined with the best-fitted fingerprint by comparing the online-measured RSS with the fingerprint database [25,26,27,28,29]. Several conventional matching algorithms, such as K nearest neighbour (KNN) [30], weighted KNN (WKNN) [31], and Bayesian probability algorithm [32], can be used as fingerprint methods.…”

Section: Introductionmentioning

confidence: 99%

A Calibration-Free Method Based on Grey Relational Analysis for Heterogeneous Smartphones in Fingerprint-Based Indoor Positioning

Zhang

Guo

Luo

et al. 2019

Sensors

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The fingerprint method has been widely adopted in Wi-Fi indoor positioning because of its advantage in non-line-of-sight channels between access points (APs) and mobile users. However, the received signal strength (RSS) during the fingerprint positioning process generally varies due to the dissimilar hardware configurations of heterogeneous smartphones. This difference may degrade the accuracy of fingerprint matching between fingerprint and test data. Thus, this paper puts forward a fingerprint method based on grey relational analysis (GRA) to approach the challenge of heterogeneous smartphones and to improve positioning accuracy. Initially, the grey relational coefficient (GRC) between the RSS comparability sequence of each reference point (RP) and the RSS reference sequence of the test point (TP) is calculated. Subsequently, the grey relational degree (GRD) between each RP and TP is determined on the basis of GRC, and the K most relational RPs are selected in accordance with the value of GRD. Finally, the user location is determined by weighting the K most relational RPs that correspond to the coordinates. The main advantage of this GRA method is that it does not require device calibration when handling heterogeneous smartphone problems. We further carry out extensive experiments using heterogeneous Android smartphones in an office environment to verify the positioning performance of the proposed method. Experimental results indicate that the proposed method outperforms the existing ones no matter whether heterogeneous smartphones are used.

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Region-based massive positioning method using Bluetooth low energy

Jeong¹,

Shin²,

Lee³

et al. 2021

jamet

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Real-Time Recursive Fingerprint Radio Map Creation Algorithm Combining Wi-Fi and Geomagnetism

Cited by 3 publications

References 27 publications

Improving Wi-Fi Fingerprint Positioning with a Pose Recognition-Assisted SVM Algorithm

Improving Wi-Fi Fingerprint Positioning with a Pose Recognition-Assisted SVM Algorithm

A Calibration-Free Method Based on Grey Relational Analysis for Heterogeneous Smartphones in Fingerprint-Based Indoor Positioning

Region-based massive positioning method using Bluetooth low energy

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