International Conference on Computer Technology and Development, 3rd (ICCTD 2011)
169 ML-DC Algorithm of Parameter Estimation for Gaussian Mixture Autoregressive Model
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With Gaussian mixture autoregressive model, the probability density and power spectrum density of non- Gaussian colored processes can be fit. Its parameters can be estimated through the ML-DC algorithm. After descriptions of the model and the estimation problem, maximum likelihood estimation of autoregressive parameters and the dynamic clutter algorithm for Gaussian mixture parameters are deduced, respectively. Based on them, ML-DC algorithm for coupling estimation between power spectrum density parameters and probability density parameters is built up. Finally, a numerical instance in simulation is illustrated where performance of estimation is discussed in detail.