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

Abstract

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.

A Review of Technologies and Techniques for Indoor Navigation Systems for the Visually Impaired

“…There are two types of fingerprint methods: those that adopt deterministic strategies and those that adopt probabilistic strategies [ 19 , 75 , 76 ].…”

Comparison of CNN Applications for RSSI-Based Fingerprint Indoor Localization

“…There are two main types of Wi-Fi-based indoor positioning technologies: the received signal strength indicator (RSSI)-based ranging positioning algorithm [12][13][14] and the fingerprint-based positioning algorithm [15][16][17]. The RSSI-based ranging positioning algorithm usually adopts the received Wi-Fi signal to estimate the distance between the target (its location is unknown) and the access point (its location is known) using the wireless radio signal propagation model and then estimates the target position using trilateration or multilateration methods.…”

“…Another BLE-based scheme is proposed in [14], where the higher precision needed an extra training phase for localization. Fingerprint-based positioning methods were explored in a machine learning-based method that was developed in [15], where the support vector machine (SVM) was used to determine the different postures of the user. Jang et al [16] provided an explicit survey for the limitation of an offline fingerprint map and overcame it with simultaneous localization and mapping (SLAM) methods.…”

Fusion of Channel State Information and Received Signal Strength for Indoor Localization Using a Single Access Point

“…Many of these work using a time-based geometric location such as time of arrival (ToA) [9] , round trip time (RTT) [10] or time difference of arrival (TDoA) [11] , even angle of arrival (AoA) [12]. Nevertheless, fingerprinting-based indoor localization systems are in the spotlight, being their benefits the simplicity, low hardware requirements and non requiring additional sensors to infer user location [13,14].…”