A Probabilistic Similarity Measure and a Non-Linear Post-Filter for Mobile Phone Positioning using GSM Signal Power Measurements

In this paper we present the design of a particle filter for post filtering instantaneous positioning estimates of GSM mobile terminals. The instantaneous estimates are obtained by comparing signal power levels, which are reported by the mobile terminal to the base station, with a database of predictions using a novel statistically motivated similarity measure. Unlike a simple Euclidian distance measure, the proposed scheme incorporates inherent information about signal power level measurements requested by the serving base station but not reported by the mobile terminal. Furthermore, we show how the Monte Carlo method of particle filtering helps to obtain better position estimates and, surprisingly, also helps to reduce the computational complexity. Results are presented for real field data.