init_sovvsslms.m

卡尔曼滤波器设计的一个例子

% [w,x,d,y,e,g,mu]=init_sovvsslms(L1,L2,w0,x0,d0,mu0,g0)
%
%     Creates and initializes the variables required for the 
%     Second Order Volterra Variable Step Size Least Mean 
%     Squares adaptive algorithm.
% 	
% Input Parameters [Size] :: 
%   L1  : memory length of the linear part of the adaptive filter
%   L2  : memory length of the non-linear part of the adaptive filter
%   w0  : initial coefficient vector [L1 + sum(1:L2) x 1] 
%   x0  : initial input samples vector [L1 + sum(1:L2) x 1] 
%   d0  : initial desired sample [1 x 1] 
%   mu0 : initial step-size vector [L1 + sum(1:L2) x 1]
%   g0  : initial gradient vector [L1 + sum(1:L2) x 1]
%
% Output parameters [default] ::
%   w   : initialized filter coefficients [zeros]
%   x   : initialized input vector [zeros]
%   d   : initialized desired sample [white noise]
%   y   : Initialized filter output 
%   e   : initialized error sample [e = d - y]
%   g   : initialized gradient vector [zeros]
%   mu  : initialized step-size vector [zeros]
%
% SEE ALSO ASPTSOVVSSLMS.

%       Author : John Garas PhD.
%       Version 2.1, Release October 2002.
%       Copyright (c) DSP ALGORITHMS 2000-2002.