A nonlinear connectionist approach to performance enhancement of correlation filters

Correlation-based filters (e.g MACE, MACH) have been widely employed for automatic target acquisition. In general, a bank of filters is developed wherein each filter is trained to respond to a particular range of conditions (such as aspect angle). Individual filter outputs are utilized to determine a best match between objects in a scene and the training information. However, it is not uncommon for discrete clutter objects to correlate well with an individual filter, resulting in an unacceptable false alarm rate (FAR). It is the authors' hypothesis that although a clutter event may correlate well with an individual filter, there are discernable differences in the way clutter and targets correlate across the bank of filters. In this paper, the authors investigate a connectionist based approach that combines the individual filter outputs in a non-linear manner for improved performance. Particular attention is given to designing the correlation filter constraints in conjunction with the combination approach to optimize performance.