WT-FLOSCMA (Orthogonal Wavelet Transform and Fraction Lower Order Statistics Based Constant Modulus Algorithm)

Abstract

The invention discloses a WT-FLOSCMA (Orthogonal Wavelet Transform and Fraction Lower Order Statistics Based Constant Modulus Algorithm), which comprises the following steps that a transmitting signal a (n) passes through an impulse response channel c (n) to obtain a channel output vector x (n); stable distributed alpha channel noise w (n) and the channel output vector x (n) are adopted to obtain an input signal y (n) of an orthogonal wavelet transformer (WT); after the input signal y (n) of a balancer is subject to orthogonal wavelet transformation, the input of the balancer is R (n), and the output of the balancer is z (n); and at the moment, a mean-square error of the WT-FLOSCMA is e(n) equals to /z(n)/ minus Rcm square root (Rcm equals to E {/a(n)/<4>} dividing E {/a(n)/<2>}, and the iterative format of a weight vector is f(n+1) equals to f(n) plus muR <-1>(n) /e (n)/<p-1> sgn (e(n))z (n) R (n)/ /z(n)/. In the invention, the fractional lower order statistics is utilized to suppress the stable alpha noise, the weight vector of blind equalization algorithm is optimized according to the minimum dispersion coefficient rule, orthogonal wavelet transformation is carried out on the input signal o the balancer, and the autocorrelation of the input signal of the balancer is reduced to quicken the convergency rate. The water sound channel simulation result shows that the performance of the method disclosed by the invention is obviously superior to that of the constant modulus algorithm.

Description

technical field [0001] The invention relates to an orthogonal wavelet blind equalization method based on fractional low-order statistics in an underwater acoustic environment. Background technique [0002] When equalizing the channel, it is usually assumed that the channel noise is Gaussian noise, but studies have shown that underwater acoustic environmental noise, low-frequency atmospheric noise, many biomedical noises, and man-made noises belong to non-Gaussian distribution, which can usually be described by α-stable distribution. Alpha-stable distribution (see: Literature [1] Changning Li, Gang Yu. A New Statistical Model for Rolling Element Bearing Fault Signals Based on Alpha-Stable Distribution [C]. Computer Modeling and Simulation, 2010.ICCMS'10.Second International Conference on, IEEE.2010, Vol.4: 386-390; literature [2] Jia Xu, Wei Han, Xiu-feng He, Ren-xi Chen. Small Target Detection in SAR Image Using the Alpha-stable Distribution Model [C] .Image Analysis and Si...

AI Technical Summary

Problems solved by technology

[0003] The purpose of the invention is to overcome the performance defect of the constant modulus method (C

Images