Composite correlation filters have been demonstrated in many automatic target recognition (ATR) applications because of their ability for class recognition and distortion-tolerance with shift invariance. Both the optimal tradeoff synthetic discriminant function (OTSDF) filters and optimal tradeoff distance classifier correlation filter (OTDCCF) approaches use parameters to combine multiple characteristics. Usually a set of filters is grouped into a bank for recognizing multiple targets across multiple geometric distortions. We extend these approaches to use independent tradeoff parameters in the filter synthesis for each class and grouping bin to improve classification. A method for determining the extended parameters is presented. Test results using the public SAR imagery MSTAR database are shown.