Wi-Fi indoor 3D localization algorithm based on multi-classifier fusion

Abstract

Indoor localization technology based on Received Signal Strength (RSS) fingerprint is widely used in life and industry. Compared with the traditional localization methods, the localization technology integrating multiple machine learning methods has better localization accuracy. However, in multi-floor localization, the existing fusion localization technology ignores the interaction between different floors in the localization process, resulting in low indoor localization accuracy. This paper proposes a Wi-Fi indoor 3D localization method based on multi-classifier fusion named FLMCF. Firstly, floor classification training is carried out to reduce the location deviation in the vertical direction. Secondly, for each floor, multiple classifiers are used for model training and training the optimal weight set by minimizing the average localization error. In this case, the advantages of each classifier can be fully integrated to improve the localization accuracy. Finally, the Reliability Fusion Weight Selection (RFWS) algorithm determines the weight and calculates the final location estimation. The experimental results indicate that FLMCF is nearly 14.1% better than DIFMIC in the 90th percentile of CDF.