@inproceedings{7cc4f74609984ca0aab8a45f0dedf926,
title = "Multichannel detection using a model-based approach",
abstract = "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 single channel performance asymptotically. A likelihood ratio for a more general class of processes with correlated Gaussian quadrature components is noted.",
author = "Michels, {J. H.} and P. Varshney and D. Weiner",
year = "1991",
doi = "10.1109/icassp.1991.150242",
language = "English (US)",
isbn = "0780300033",
series = "Proceedings - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing",
publisher = "IEEE Computer Society",
pages = "3553--3556",
booktitle = "Proceedings - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing",
address = "United States",
note = "Proceedings of the 1991 International Conference on Acoustics, Speech, and Signal Processing - ICASSP 91 ; Conference date: 14-05-1991 Through 17-05-1991",
}