Automatic target recognition (ATR) performance improvement using integrated grayscale optical correlator and neural network

Abstract

We have continued to develop the Grayscale Optical Correlator (GOC) system and have explored a variety of automatic target recognition (ATR) applications to take advantage of the inherent performance advantages of the GOC vast parallelism and high-speed [1-4]. Recently, we have added a neural network (NN) post-processor to greatly decrease the false positive detection rate while retaining the high positive detection rate obtained by the by the GOC. In this paper, we will discuss recent advancements in both the ATR processing algorithm development as well as an innovative breakthrough in designing new GOC hardware system architecture. First, we will briefly overview recent advances in our GOC and NN processor algorithm development. We will then present a new architecture that can lead to the mass production of a new generation of high-performance, low-cost Grayscale Optical Correlator. This new GOC architecture relies on the utilization of the maturing Digital Light Processor (DLP) as both the input and the filter Spatial Light Modulator (SLM). Detailed system description and performance analysis will also be reported.