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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.