An Efficient Normalized Rank Based SVM for Room Level Indoor WiFi Localization with Diverse Devices

作者：Rezgui, Yasmine; Pei, Ling^*; Chen, Xin; Wen, Fei; Han, Chen

来源：Mobile Information Systems, 2017, 2017: 6268797.

DOI：10.1155/2017/6268797

• Indoor localization
• Localization method
• Location finger printings
• Neural network algorithm
• Received signal strength indicators
• Support vector machine classifiers
• Transformation based
• Wi-Fi fingerprinting

This paper proposes an efficient and effective WiFi fingerprinting-based indoor localization algorithm, which uses the Received Signal Strength Indicator (RSSI) of WiFi signals. In practical harsh indoor environments, RSSI variation and hardware variance can significantly degrade the performance of fingerprinting-based localization methods. To address the problem of hardware variance and signal fluctuation in WiFi fingerprinting-based localization, we propose a novel normalized rank based Support Vector Machine classifier (NR-SVM). Moving from RSSI value based analysis to the normalized rank transformation based analysis, the principal features are prioritized and the dimensionalities of signature vectors are taken into account. The proposed method has been tested using sixteen different devices in a shopping mall with 88 shops. The experimental results demonstrate its robustness with no less than 98.75% correct estimation in 93.75% of the tested cases and 100% correct rate in 56.25% of cases. In the experiments, the new method shows better performance over the KNN, Naive Bayes, Random Forest, and Neural Network algorithms. Furthermore, we have compared the proposed approach with three popular calibration-free transformation based methods, including difference method (DIFF), Signal Strength Difference (SSD), and the Hyperbolic Location Fingerprinting (HLF) based SVM. The results show that the NR-SVM outperforms these popular methods.

• 出版日期2017
• 单位上海交通大学