Simulate LPF with injected signals

We can, of course, do the same injections but with the full LPF model and with noise switched on.

In Topic 2 you already saw how to simulate an LPF model with all the noises switched on. If we combine that with the signal injections we've been doing in this topic, then a simulation with noise and injected signals can be done like this:

      % Create a standard LPF model
      lpf = ssm(plist('built-in', 'LPF'));
      % Generate suitable covariance matrix for all inputs
      cov = lpf.generateCovariance;
      % Create some time-series analysis object to inject
      aSignal = ao(plist('tsfcn', '1e-7*sin(2*pi*0.005*t)', 'fs', 1/lpf.timestep, 'nsecs', 10000));       
      % Create the plist to configure simulate      
      sim_pl = plist('AOS', aSignal, 'AOS Variable Names', 'GUIDANCE.ifo_x1', ... 
                     'return outputs', {'DELAY_IFO.x1', 'DELAY_IFO.x12'}, ...
                     'CPSD variable names', cov.find('names'), ...
                     'CPSD', cov.find('cov'));
      % Run the simulation      
      out = simulate(lpf, sim_pl);
We can then plot the two IFO outputs by doing:
      % Unpack the outputs      
      [o1, o12] = unpack(out);
      % Plot on subplots      
      plot_pl = plist('arrangement', 'subplots');
      iplot(o1, o12, plot_pl);
This should result in a plot like this:

We can estimate the spectral densities of these two signals and we will should a signal at 5mHz:
      % Create a PSD plist with 16 averages, linear segment-wise detrending       
      psd_pl = plist('navs', 16, 'order', 1, 'win', 'BH92');
      % Estimate PSDs      
      [o1_xx, o12_xx] = psd(o1, o12, psd_pl);
      % Plot the ASDs      
      iplot(sqrt(o1_xx), sqrt(o12_xx));
This should result in a plot like this:

©LTP Team