Study On Multi-modal Noise In Electronic System

In the past decade, the processing of signal has been one of the fastest developing subjects. The traditional signal processing is based on three suppose: linearity, Gaussianity and stationarity. While the modern signal processing non-linearity, non-Gaussianity and non-stationarity. Especially, the signal with the non-Gaussian noise has caught most people's eyes.This paper gives some perfection and complement to the theory of communication & signal processing based on bimodal hybrid noise. Then multi-modal noise has been propose, and is a hybrid noise consisting of many kinds of additive noises. It belongs to non-Gaussian noise. Generally,the method used to study non-Gaussian noise is using high-order statistics(HOS). The paper uses the method of modern signal processing. Main content of this paper is as follows:(1)It reviews present conditions of development of communication systems, and introduces the history and current situation of the processing technology of noise, and provides the perspective of the theory of multi-modal noise.(2)The thesis systematically studied and discussed a variety of non-Gaussian noise theory. Then illustrate all kinds of noise and put forward exact algorithm to separate the noise and signal. We also systematically described the basic theory of statistical signal detection and the adjudging rules of statistical signal.(3) Research and analysis of the bimodal noise, leads to the four models of the multi-modal hybrid noise. Then in-depth analyze the statistical properties of multi-modal noise in the absolute mean and average power. So it is the foundation for future research.(4)Research of the adaptive algorithm and LMS Newton algorithm, and the improved algorithm has been proposed, which is making the noise and signal well separated.(5)It studies estimation based on Bayesian theory, in many problems, used observed value with noise to filter and estimate on systems status, people often adopt state space method to simulated system. The paper provides a kind of particle filter with MLP fusion to realize to signal estimation.