Algorithms for Optimum Detection of Signals in Gaussian Noise
Abstract
Algorithms are presented for detection of signals in Gaussian noise. The signals can be Gaussian or nonGaussian. The algorithms are derived from a general solution to the continuous-time problem, and are approximations to the continuous-time likelihood ratio. They do not require knowledge of the probability distributions for the signal-plus-noise process, but instead require knowledge (or estimation) of a function. Independent sampling is not assumed. One algorithm is fully adaptive to the signal-plus-noise process. The algorithms have the potential of providing significant performance improvements, as compared to classical detection methods, when the signal-plus-noise process is broadband (stationary or nonstationary), and particularly when it is nonGaussian.
Document Details
- Document Type
- Technical Report
- Publication Date
- Mar 01, 1991
- Accession Number
- ADA256896
Entities
People
- C. R. Baker
Organizations
- University of North Carolina at Chapel Hill