Kullback-leibler Distances for Quantifying Clutter and Models

This paper examines metrics for measuring clutter eeectiveness on model-based automatic target recognition systems with FLIR sensors. The measure for clutter eeectiveness proposed is the diierence 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 system 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 \eeective clutterer." To quantify the eeect 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.