Optimal detection in colored non-Gaussian noise with unknown parameters
Document Type
Conference Proceeding
Date of Original Version
1-1-1987
Abstract
The problem of detecting a signal, known except for amplitude, in incompletely characterized non-Gaussian noise is addressed. The use of a generalized likelihood ratio test or its asymptotically equivalent form, the Rao test, is shown to produce a detector that has the identical asymptotic performance as a generalized likelihood ratio test designed with a priori knowledge of the unknown noise parameters. Since the latter clairvoyant detector always produces an upper bound on performance, the generalized likelihood ratio test is claimed to be optimum. An example is given in which the noise is modeled as an autoregressive process with a mixed-Gaussian noise excitation. Results of a computer simulation are described that verify the theory.
Publication Title, e.g., Journal
ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Citation/Publisher Attribution
Kay, Steven, and Debasis Sengupta. "Optimal detection in colored non-Gaussian noise with unknown parameters." ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings (1987): 1087-1090. doi: 10.1109/ICASSP.1987.1169781.