Hi zym,
Unfortunately, there is no way of retrieving the individual particle likelihoods from the correct function.
As a workaround, you can retrieve the likelihood by evaluating the MeasurementLikelihoodFcn after the prediction step. This incurs a little extra overhead, but if your MeasurementLikelihoodFcn is simple, this might be acceptable for your application. See a small example here of evaluating the function between the prediction and correction steps and calculating the likelihood vector:
% Create a particle filter
pf = stateEstimatorPF;
% Initialize the particle filter at state [4 1 9] with unit covariance
initialize(pf, 5000, [4 1 9], eye(3), 'StateOrientation', 'row');
% Run one predict step
statePredicted = predict(pf);
% Assume we have a measurement [4.2 0.9 9]
% (Optional) calculate likelihood for each particle.
measurement = [4.2 0.9 9];
likelihood = feval(pf.MeasurementLikelihoodFcn, pf, pf.Particles, measurement)
% Run correct step with measurement
stateCorrected = correct(pf, measurement);
