Title

Asymptotically Optimal Detection In Incompletely Characterized Non-Gaussian Noise

Document Type

Article

Date of Original Version

1-1-1989

Abstract

The problem of detecting a signal known except for amplitude in non-Gaussian noise is addressed. The noise samples are assumed to be independent and identically distributed with a probability density function known except for a few parameters. Using a generalized likelihood ratio test, it is proven that for a symmetric noise probability density function, the detection performance is asymptotically equivalent to that obtained for a detector designed with a priori knowledge of the noise parameters. A computationally more efficient but equivalent test is proposed and a computer simulation performed to illustrate the theory. © 1989 IEEE

Publication Title

IEEE Transactions on Acoustics, Speech, and Signal Processing

Volume

37

Issue

5

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