Adaptive signal processing
Results on AR-modelling of nonstationary signals
Signal Processing
Adaptive filter theory (3rd ed.)
Adaptive filter theory (3rd ed.)
Bayesian forecasting and dynamic models (2nd ed.)
Bayesian forecasting and dynamic models (2nd ed.)
Statistical Digital Signal Processing and Modeling
Statistical Digital Signal Processing and Modeling
Digital Audio Restoration: A Statistical Model Based Approach
Digital Audio Restoration: A Statistical Model Based Approach
On sequential Monte Carlo sampling methods for Bayesian filtering
Statistics and Computing
Monte Carlo filtering and smoothing with application to time-varying spectral estimation
ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 02
Parameter estimation for autoregressive Gaussian-mixture processes: the EMAX algorithm
IEEE Transactions on Signal Processing
Modeling of non-stationary autoregressive alpha-stable processes by particle filters
Digital Signal Processing
Modeling non-Gaussian time-varying vector autoregressive processes by particle filtering
Multidimensional Systems and Signal Processing
A learning method of support vector machine based on particle filters
ICNC'09 Proceedings of the 5th international conference on Natural computation
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Parameter estimation of time-varying non-Gaussian autoregressive processes can be a highly nonlinear problem. The problem gets even more difficult if the functional form of the time variation of the process parameters is unknown. In this paper, we address parameter estimation of such processes by particle filtering, where posterior densities are approximated by sets of samples (particles) and particle weights. These sets are updated as new measurements become available using the principle of sequential importance sampling. From the samples and their weights we can compute a wide variety of estimates of the unknowns. In absence of exact modeling of the time variation of the process parameters, we exploit the concept of forgetting factors so that recent measurements affect current estimates more than older measurements. We investigate the performance of the proposed approach on autoregressive processes whose parameters change abruptly at unknown instants and with driving noises, which are Gaussian mixtures or Laplacian processes.