Non-linear signal detection algorithms on background additive-multiplicative non-gaussian noise
Володимир Васильович Палагін · 2012
The problem of signal detection on the background noise is one of the most important and actual in many application s . The most complete model of the interaction of signals and noise is additive-multiplicative model, which is typical for many practical cases. The task signal processing is more difficult when considering Non-Gaussian noise, both additive and multiplicative character. Therefore, the actual task is the development of methods and algorithms for statistical signal processing on the background additive-multiplicative Non-Gaussian noise in radar, sonar, geophysics, systems of mobile communications. This paper illustrates the use of an alternative approach to the description of random variables - moment– cumulant description. In this work the additional question of efficient algorithms for signal detection on a background additive-multiplicative Non-Gaussian noise are consider. Purpose of work is improve the efficiency of systems Signal Detection on a background additive-multiplicative Non-Gaussian noise on the basis of polynomial decision rules, that are optimal for the moment criterion. Presented synthesis of decision rules radiosignal detection on the background additive-multiplicative Non-Gaussian noise to the degree stochastic polynomial . It is shown that with increasing degree stochastic polynomial and considering Non-Gaussian noise, value of the criterion of the upper limit of probability of errors of the first and second kind decreases, indicating a decrease in the probability of errors synthesized nonlinear decision rules and increase their effectiveness in comparison with linear decision rules