MULTIPLE-OBJECT DETECTION WITH A CHIRP-ENCODED JOINT TRANSFORM CORRELATOR

We investigate the performance of a chirp-encoded joint transform corr elator in the presence of multiple input objects. We show that, for an input scene containing multiple targets, the chirp-encoding technique focuses the desired cross correlations between the reference signal a nd the input targets and the undesired self-correlations between the t argets in the input scene in separate output planes. The output of the chirp-encoded joint transform correlator is mathematically analyzed f or an input scene containing multiple targets. Both the linear joint t ransform correlator and the nonlinear joint transform correlator in th e presence of multiple input targets are considered. For the nonlinear joint transform correlator, the chirp-encoding focuses the higher-ord er correlation terms, including the higher-order terms of the self-cor relations between the targets in the input scene onto separate output planes. The separation requirements of the conventional and the chirp- encoded joint-transform correlator in the presence of multiple input t argets are discussed. Computer simulations and experimental results of the chirp-encoded joint transform correlator for a scene containing m ultiple input targets are presented. The results are compared with a c onventional joint transform correlator for an input scene containing m ultiple targets.