Finding by counting: a probabilistic packet count model for indoor localization in ble environments
Published in The 11th Workshop on Wireless Network Testbeds, Experimental evaluation & Characterization, Association for Computing Machinery, 2017
De, Subham, Shreyans Chowdhary, Aniket Shirke, Yat Long Lo, Robin Kravets, and Hari Sundaram. In Proceedings of the 11th Workshop on Wireless Network Testbeds, Experimental evaluation & Characterization, pp. 67-74. ACM , 2017.
Abstract
We propose a probabilistic packet reception model for Bluetooth Low Energy (BLE) packets in indoor spaces and we validate the model by using it for indoor localization. We expect indoor localization to play an important role in indoor public spaces in the future. We model the probability of reception of a packet as a generalized quadratic function of distance, beacon power and advertising frequency. Then, we use a Bayesian formulation to determine the coefficients of the packet loss model using empirical observations from our testbed. We develop a new sequential Monte-Carlo algorithm that uses our packet count model. The algorithm is general enough to accommodate different spatial configurations. We have good indoor localization experiments: our approach has an average error of ∼ 1.2m, 53% lower than the baseline range-free Monte Carlo localization algorithm.