Efficient Mobile Location Tracking and Data Reduction for Proximity Detection Applications

The paper considers mobile location tracking and trajectory data reduction techniques for applications pertaining to mobile contact tracing and proximity detection in wireless cellular networks. Unscented Kalman filtering with non-line-of-sight bias mitigation is first applied for robust mobile trajectory estimation. An approach for modeling and analysis of pair-wise proximity and multi-mobile clustering scenarios is then introduced within a hypothesis testing framework, and a thorough performance evaluation is presented to assess the achievable detection and false alarm probabilities based on factors pertaining to proximity distance and timespan, ranging accuracy and bias statistics. For scenarios of practical interest, results show that correct proximity detection rates in excess of 70-to-80% range can be achieved while maintaining very low false alarm rates. Data reduction using the discrete Haar transform is subsequently applied for efficient storage of trajectory data. An analysis of the tradeoffs between reduction level and proximity detection reliability is presented to demonstrate the viability of the proposed approach with its low complexity and good performance at moderate reduction levels. Additional comparative analysis is presented to assess the impact of specific distance measures and wavelet types, and it is found that the Chebyshev distance offers improvements in detection accuracy compared to Euclidean and Manhattan measures, while wavelet change, when retaining short support, didn't have a significant impact.