Position, Scale, and Rotation Invariant Target Recognition Using Range Imagery.

This thesis explores a new approach to the recognition of tactical targets using a multifunction laser radar sensor. Targets of interest were tanks, jeeps, and trucks. Doppler images were segmented and overlaided onto a relative range image. The resultant shapes were then transformed into a position, scale, and rotation invariant PSRI feature space. The classification processes used the correlation peak of the template PSRI space and the target PSRI space as features. Two classification methods were implemented a classical distance measurement approach and a new biologically-based neural network multilayer perception architecture. Both methods demonstrated classification rates near 100 with a true rotation invariance demonstrated up to 20 degrees. Neural networks were shown to have a distinct advantage in a robust environment and when a figure of merit criteria was applied. A space domain correlation was developed using local normalization and multistage processing to locate and classify targets in high clutter and with partially occluded targets.