rls3.m

《自适应滤波算法与实现》（第二版）源码

%RLS3 Problem 1.1.1.2.3
%
%   'ifile.mat' - input file containing:
%      I - members of ensemble
%      K - iterations
%      a1 - coefficient of input AR process
%      sigmax - standard deviation of input
%      Wo - coefficient vector of plant
%      sigman - standard deviation of measurement noise
%      lambda - forgetting factor
% 
%   'ofile.mat' - output file containing:
%      ind - sample indexes 
%      M - misadjustment 

clear all	% clear memory
load ifile;	% read input variables
sigmav=sigmax*sqrt(1-a1^2);	
		% standard deviation of input to AR process
L=length(Wo);		% plant and filter length
N=L-1;			% plant and filter order
delta=1/sigmax^2;	% initialization factor for SD
ilambda=1/lambda;	% auxiliary constant
MSE=zeros(K,1);		% prepare to accumulate MSE*I
MSEmin=zeros(K,1);	% prepare to accumulate MSEmin*I

for i=1:I,		% ensemble
   X=zeros(L,1);    	% initial memory
   SD=delta*eye(L);	
	% initial inverse of deterministic correlation matrix
   W=zeros(L,1);	% initial coefficient vector
   v=randn(K,1)*sigmav;		% input to AR process
   x=filter([1,0],[1,a1],v);	% input 
   n=randn(K,1)*sigman;		% measurement noise 
   for k=1:K,		% iterations
      X=[x(k)		
         X(1:N)];	% new input vector
      d=Wo'*X;		% desired signal sample
      ep=d+n(k)-W'*X;	% a priori error sample
      psi=SD*X;
      SD=ilambda*(SD-psi*psi'/(lambda+psi'*X));
	% new inverse of deterministic correlation matrix
      W=W+ep*SD*X;		% new coefficient vector
      MSE(k)=MSE(k)+ep^2;	% accumulate MSE*I
      MSEmin(k)=MSEmin(k)+(n(k))^2;	% accumulate MSEmin*I
   end
end

ind=0:(K-1);		% sample indexes
M=MSE./MSEmin-1;	% calculate misadjustment
save ofile ind M;	% write output variables