Likelihood Ratios and Signal Detection for Nongaussian Processes.
Abstract
The emphasis is on development of likelihood ratios and detection algorithms for problems involving nonGaussian data. The first problem considered is that of detecting a nonGaussian signal in Gaussian noise. This frequently arises in active sonar; it could also be important for passive sonar. General results are presented on nonsingular detection and likelihood ratio. A recursive discrete-time detection algorithm is obtained and is shown to be a likelihood ratio detector when the signal-plus-noise is Gaussian. The second major problem considered is that of detecting a signal in spherically-invariant noise (SIN). This is a model which has been proposed for some impulsive-plus-Gaussian environments, and is closely linked to detection problems encountered in some active sonar applications. General results on nonsingular detection and likelihood ratio are first obtained. For detection of a known signal, the behavior of the discrete-time likelihood ratio is analyzed as the sample size increases. Constant-false-alarm-probability detectors are given, and an example based on sonar data illustrates the potential loss due to using a Gaussian model when the noise is actually nonGaussian SIN.
Document Details
- Document Type
- Technical Report
- Publication Date
- Mar 01, 1986
- Accession Number
- ADA170315
Entities
People
- A. F. Gualtierotti
- C. R. Baker
Organizations
- University of North Carolina at Chapel Hill