TRACK-TO-TRACK ASSOCIATION USING INFORMATION THEORETIC CRITERIA

There are three primary types of data association problems of interest in space surveillance: the observation-to-track association (OTTA) problem, the track-to track association (TTTA) problem, and the observation-to-observation association (OTOA) problem. In this paper, we build on recent work to further investigate the use of information theoretic criteria to solve the TTTA problem, in which we have multiple uncorrelated tracks (UCTs) to be tested for association against a given set of tracks given at a different (usually previous) time instance. Both the tracks and the UCTs are uncertain and are probabilistically described using multivariate normal distributions. This allows for a closed-form solution based on the unscented transform and on information divergence for Gaussian distributions. We will establish relationship to the covariance-based track association (CBTA) technique and compare the performance of the two methods in Monte Carlo simulations.