Kullback-Leibler distances for quantifying clutter and models

We examine metrics for measuring clutter effectiveness on model-based automatic target recognition (ATR) systems with forward-looking infrared (FLIR) sensors. The measure for clutter effectiveness proposed is the difference of two Kullback-Leibler distances between the idealized approximate probabilistic models without clutter and the real model containing clutter. We establish that occluding objects and clutter, when manipulated, do not present a fundamental challenge to model-based ATR systems if the model manipulated is an accurate representation of the obscuring clutter. However, if the obscurer is not manipulated, performance degrades in cases where the obscurer is an `effective clutterer.' To quantify the effect of clutter in ATR, estimation and detection problems are considered for rigid ground-based targets. For estimating the orientation of a vehicle, the Hilbert-Schmidt distance is employed.