Distributed cooperative location estimation (D-COOLEST) in wireless environments

Abstract

In probabilistic location estimation, Kalman filters, particle filters, and hidden Markov model (HMM) schemes are commonly used. Among those, HMM-based algorithms have the best performance. However, there is still room for improvement. In this paper, we propose two distributed cooperative location estimation algorithms, D-COOLEST1 and D-COOLEST2, for HMM-based location estimation. The users are designed to exchange their observations and estimation results with each other after random encounters which allows them to further improve the accuracy of their location estimation. To the best of our knowledge, this is the first work in the literature to propose the theoretical framework for user cooperation in probabilistic location estimation. Simulation results show that the proposed algorithms can significantly improve the estimation accuracy and reduce the normalized mean squared error (MSE).