Multichannel detection using a model-based approach

The Gaussian multichannel binary detection problem is considered. A multichannel generalized likelihood ratio is implemented using a model-based approach where the signal is assumed to be characterized by an autoregressive vector process. Detection performance is obtained for the special case where the underlying processes are assumed to have known autoregressive process parameters. Specifically, results for two-channel signal vectors containing various temporal and cross-channel correlation are obtained using a Monte Carlo procedure. These results are plotted versus signal-to-noise ratio and are shown to be bounded by available optimal detection curves. The two-channel detection results are shown to decrease as (S/N)₂ decreases and approach the superior angle channel performance asymptotically. A likelihood ratio for a more general class of processes with correlated Gaussian quadrature components is noted