DNN-based Indoor Fingerprinting Localization with WiFi FTM

Abstract

In this work, we present a deep neural network (DNN)-based indoor fingerprinting localization method with WiFi fine time measurements (FTM). The proposed method leverages the WiFi FTM and its variance as environment features to provide accurate location estimation. An i-th layer DNN structure used in this paper is implemented by back propagation using an Adam optimizer. The weights and the bias of the l-text{th} layer that minimize the loss function is computed in order to minimize the positioning mean squared error (MSE). Experimental results using real-world data obtained in a typical office setting proves the efficiency of the proposed solution. The performance of the system is remarkably improved, using the 600times 600 hidden layer size of the DNN, we achieved an average positioning accuracy of 0.7 m and 0.9 m for the 68-th percentiles (1-sigma) and 95-th percentiles (2-sigma) respectively.