Abstract

We demonstrate an adaptive multi-layer optical classifier performing single pulse radar target recognition to identify isolated aircraft targets with varying orientation and/or range from the radar. The system uses optically-calculated time-frequency representations as its internal representation, and in particular the triple autocorrelation [1] due to the natual range invariance of this feature. This approach increases the separability of the input data by nonlinearly mapping it into a higher dimensional feature space. Serial processing of the optically computed feature vector using CCD detectors and electronic postprocessing restricts system throughput since the massive quantities of data overburdens electronic digital postprocessing, whereas adaptive optical classification avoids this electronic bottleneck. We have previously demonstrated a broadband communications signal classifier using a non-adaptive classifer [2]. In this paper we report an adaptive multi-layer optical classifier and present experimental classification results in which the neural layer learns to identify optically computed triple autocorrelation representations of a training set of aircraft radar range profiles. The generalization performance to untrained low resolution profiles was excellent.

© 1997 Optical Society of America