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.