Asymptotically Optimal Detection In Incompletely Characterized Non-Gaussian Noise
Date of Original Version
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, e.g., Journal
IEEE Transactions on Acoustics, Speech, and Signal Processing
Kay, Steven M.. "Asymptotically Optimal Detection In Incompletely Characterized Non-Gaussian Noise." IEEE Transactions on Acoustics, Speech, and Signal Processing 37, 5 (1989): 627-633. doi: 10.1109/29.17554.