Kld Minimization-Based Constrained Measurement Filtering For Two-Step TDOA Indoor Tracking

This paper presents an enhanced two-step method for tracking an indoor point target using the time difference of arrival (TDOA) measurements from an ultra wideband (UWB) positioning system. Again, the algorithm preprocesses the raw TDOAs and then feeds the results to a recursively bounded grid-based filter (RBGF) for position tracking. Different from the state-of-the-art, inequality constraints on the true TDOAs from the RBGF are exploited in the preprocessing step through constrained Kullback-Leibler divergence (KLD) minimization. In particular, a semidefinite programming (S-DP) problem is formulated and solved to find a Gaussian TDOA posterior closest in terms of KLD to the unconstrained one while satisfying all inequality constraints. Simulations show that the newly developed algorithm outperforms the one we recently proposed to impose the inequality constraints via probability density function (PDF) truncation.