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In this section we perform system identification to estimate desired parameters. We follow two approaches. The Marcov Chain Monte Carlo and the Linear Parameter Estimation with Singular Value Decomposition. For both approaches, some definitions are needed. First we load all the objects created for this simulation.

clear all %% Load Objects (signals and model) in = matrix('input.mat'); out = matrix('output.mat'); noise = matrix('noise.mat'); mdl = ssm('fitting_model.mat');

% Input & Output names inNames = { 'GUIDANCE.IFO_x1' 'GUIDANCE.IFO_x12'}; outNames = {'DELAY_IFO.x1' 'DELAY_IFO.x12'};

% Define the parameters to estimate and the starting values. params = {'FEEPS_XX', 'CAPACT_TM2_XX', 'IFO_X12X1', 'EOM_TM1_STIFF_XX', 'EOM_TM2_STIFF_XX'}; values = [1 1 0.0001 1.e-6 1.e-6];

Calculate expected covariance of the parameters (FIM) | Parameter estimation with MCMC |

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