The classification of nonstationary signals of unknown duration is of great importance in areas like oil exploration, moving target detection, and pattern recognition. In an earlier work, we provided a solution to this problem, based on the wavelet transform, by defining representations called pseudo power signatures for signal classes which were independent of signal length, location and magnitude, and proposed a simple approach using the Singular Value Decomposition to generate these signatures. This paper offers a new approach resulting in more discriminating signatures. The enhanced signatures are obtained by solving a nonlinear minimization problem involving an inverse projection. The problem formulation, solution procedure, and computational algorithm are presented in this work. An analysis of the projection signatures, and their efficacy in separating highly correlated signal classes are demonstrated through simulation examples.