RSSI-fingerprinting-based mobile phone localization with route constraints

Abstract

Accurate positioning of a moving vehicle along a route enables various applications, such as travel-time estimation, in transportation. Global Positioning System (GPS)-based localization algorithms suffer from low availability and high energy consumption. A received signal strength indicator (RSSI) measured in the course of the normal operation of Global System for Mobile Communications (GSM)-based mobile phones, on the other hand, consumes minimal energy in addition to the standard cell-phone operation with high availability but very low accuracy. In this paper, we incorporate the fact that the motion of vehicles satisfies route constraints to improve the accuracy of the RSSI-based localization by using a hidden Markov model (HMM), where the states are segments on the road, and the observation at each state is the RSSI vector containing the detected power levels of the pilot signals sent by the associated and neighboring cellular base stations. In contrast to prior HMM-based models, we train the HMM based on the statistics of the average driver's behavior on the road and the probabilistic distribution of the RSSI vectors observed in each road segment. We demonstrate that this training considerably improves the accuracy of the localization and provides localization performance robust over different road segment lengths by using extensive cellular data collected in Istanbul, Turkey; Berkeley, CA, USA; and New Delhi, India.