Iterative linear parameter estimation for multichannel systems - symbolic system model in frequency domain

  
  pl = plist('hostname', 'lpsdas01.esac.esa.int', 'database', 'ex6');

  o1_1 = ao(pl.pset('binary', 'yes', 'id', 169));
o12_1 = ao(pl.pset('binary', 'yes', 'id', 170));

o1_2 = ao(pl.pset('binary', 'yes', 'id', 171));
o12_2 = ao(pl.pset('binary', 'yes', 'id', 172));

  is1 = matrix(pl.pset('binary', 'yes', 'id', 173));
is2 = matrix(pl.pset('binary', 'yes', 'id', 180));

  fil1 = filterbank(pl.pset('binary', 'yes', 'id', 191));
fil2 = filterbank(pl.pset('binary', 'yes', 'id', 192));
fil3 = filterbank(miir());
% build matrix
wf = matrix(fil1,fil3,fil3,fil2,plist('shape',[2 2]));

  
  % empty ao
eao = ao();

% exp_3_1
os1 = matrix(o1_1,o12_1,plist('shape',[2 1]));

%  exp_3_2
os2 = matrix(o1_2,o12_2,plist('shape',[2 1]));

% Input signals
iS = collection(is1,is2);

% Fit Params
usedparams = {'A1','A2','S21','w1','w12','del1','del2'};

nsecs = os1.objs(1).data.nsecs;
fs = os1.objs(1).data.fs;
npad = nsecs*fs;

% set bounded params
bdparams = {'del1','del2'};
bdvals   = {[0.1 0.3],[0.1 0.3]};

  
  H = matrix(plist('built-in','ifo2ifo', 'Version', 'LSS v4.9.2 Phys Params'));

  
  plfit = plist(...
  'FitParams',usedparams,...
  'Model',H,...
  'Input',iS,...
  'WhiteningFilter',wf,...
  'tol',1,...
  'Nloops',10,...
  'Npad',npad,...
  'Ncut',1e4);

opars1 = linfitsvd(os1,os2,plfit);

  
  H2 = matrix(plist('built-in','ifo2ifo', 'Version', 'LSS v4.9.2 Phys Params Alias'));

  
  plalias = plist('nsecs',nsecs,'npad',npad,'fs',fs);
for ii=1:numel(H2.objs)
  H2.objs(ii).assignalias(H2.objs(ii),plalias);
end

plfit2 = plist(...
  'FitParams',usedparams,...
  'Model',H2,...
  'BoundedParams',bdparams,...
  'BoundVals',bdvals,...
  'Input',iS,...
  'WhiteningFilter',wf,...
  'tol',1,...
  'Nloops',10,... % maximum number of fit iterations
  'Npad',npad,...
  'Ncut',1e4); % number of data points to skip at the starting of the series to avoid whitening filter transient

opars2 = linfitsvd(os1,os2,plfit2);