Improved Auxiliary Particle Filtering: Applications to Time-varying Spectral Analysis
نویسندگان
چکیده
This paper addresses optimal estimation for time varying autoregressive (TVAR) models. First, we propose a statistical model on the time evolution of the frequencies, moduli and real poles instead of a standard model on the AR coefficients as it makes more sense from a physical viewpoint. Second, optimal estimation involves solving a complex optimal filtering problem which does not admit any closed-form solution. We propose a new particle filtering scheme which is an improvement over the so-called auxiliary particle filter. The hyperparameters tuning the evolution of the model parameters are also estimated on-line so as to robustify the model. Simulations demonstrate the efficiency of both our model and algorithm.
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