Joint state and parameter estimation for hidden wide-sense stationary ARMA processes under unknown noise

• Published in: 2017 20th International Conference on Information Fusion (Fusion) pp. 1 - 8
• Main Authors: Shuhui Li, Xiaoxue Feng, Feng Pan
• Format: Conference Proceeding
• Language: English
• Published: International Society of Information Fusion (ISIF) 01.07.2017
• Subjects: EM algorithm; Gaussian mixture model; Hidden Markov models; Kernel; Parameter estimation; particle filter; Particle filters; Probability; Signal processing algorithms; Smoothing methods; wide-sense stationary ARMA processes

The wide-sense auto-regressive moving-average (ARMA) model is widely applied into varieties of fields. The unknown bounded parameter estimation of an ARMA model is an extremely vital research subject. Up to recent, most research is conducted with the known disturbing environment noise or the model o...