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Information theoretics in the IMM decision process

Tracking within dense clutter environments has severely stressed modern tracking capabilities. When this is compounded by large sensor uncertainties, different platform geometries, and poor sensor quality against targets that operate under variable speeds (often below thresholds detectable by GMTI sensors) and under high maneuvers, most tracking approaches fail. Two competing approaches have gained in popularity in recent years, Multiple Hypothesis Tracking and Interacting Multiple Model. Both of these approaches rely on the principles of hybrid state estimation using Gaussian mixtures. Traditionally, the chi-squared approach has been used to assess tracking performance, whether we use a single track model or multiple models within the Gaussian mixture framework. This paper will examine the use of Kullback-Leibler metrics as a viable means of measuring the impact of data selection on model parameter estimation and compare performance with respect to the Mahalanobis distance metric. Specifically, we shall show that the Mahalanobis distance is actually a special case of the Kullback-Leibler metric when evaluating Gaussian mixture model systems.