Title :
A class of memoryless robust detectors in dependent noise
Author :
Cheung, Julian ; Kurz, Ludwik
Author_Institution :
Dept. of Electr. Eng., New York Inst. of Technol., NY, USA
fDate :
5/1/1994 12:00:00 AM
Abstract :
A new general approach to the formulation of a detector in non-Gaussian noise satisfying the strong mixing condition is introduced. The only statistical knowledge required for the optimal design is a set of parameters which can be estimated from data and recursively updated. This eliminates the requirement that exact distributions be known and leads naturally to efficient adaptive detectors
Keywords :
noise; signal detection; adaptive detectors; dependent noise; memoryless robust detectors; nonGaussian noise; parameter estimation; signal detection; statistical knowledge; strong mixing condition; Additive noise; Detectors; Niobium; Noise robustness; Probability; Random variables; Recursive estimation; Signal processing; Space stations; Statistical analysis;
Journal_Title :
Signal Processing, IEEE Transactions on
