Multichannel Detection of Partially Correlated Signals in Clutter
In-house rept. Nov 1991-Oct 1992
ROME LAB ROME NY
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This report considers the Gaussian multichannel binary detection problem in which the signal and non-white clutter noise are Gaussian vector processes with unknown statistics. A generalized likelihood ratio using multichannel innovations processes is implemented via a model-based approach where the signal and clutter are assumed to be characterized by autoregressive vector processes with arbitrary temporal and cross-channel correlation. The innovations processes are obtained through linear estimation using multichannel parameter estimates. Detection performance is considered as the estimates approach steadystate with increasing data block sample sizes. Results for two- channel signal and clutter noise vectors with various temporal and cross-channel correlation are obtained using a Monte-Carlo procedure. In the transient-state estimation with limited data, the detection results are considered as a function of the data batch sizes used in parameter estimation. Furthermore, it is noted that the detection performance in the transient-state is related to that of the estimator which, in turn, has its own dependence upon process correlation. The results provide insight regarding the factors which control the processing time requirements to achieve a specified level of detection performance.... Detection, Multichannel processes, Estimation, Model-based detection, Innovations.
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