Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Editorial Petar M. Djuri´ c Department of Electrical and Computer Engineering, | EURASIP Journal on Applied Signal Processing 2004 15 2239-2241 2004 Hindawi Publishing Corporation Editorial Petar M. Djuric Department of Electrical and Computer Engineering Stony Brook University Stony Brook NY 11794 USA Email djuric@ Simon J. Godsill Department of Engineering University of Cambridge Cambridge CB2 1PZ UK Email sjg@ Arnaud Doucet Department of Engineering University of Cambridge Cambridge CB2 1PZ UK Email ad2@ In most problems of sequential signal processing measured or received data are processed in real time. Typically the data are modeled by state-space models with linear or nonlinear unknowns and noise sources that are assumed either Gaussian or non-Gaussian. When the models describing the data are linear and the noise is Gaussian the optimal solution is the renowned Kalman filter. For models that deviate from linearity and Gaussianity many different methods exist of which the best known perhaps is the extended Kalman filter. About a decade ago Gordon et al. published an article on nonlinear and non-Gaussian state estimation that captured much attention of the signal processing community 1 . The article introduced a method for sequential signal processing based on Monte Carlo sampling and showed that the method may have profound potential. Not surprisingly it has incited a great deal of research which has contributed to making sequential signal processing by Monte Carlo methods one of the most prominent developments in statistical signal processing in the recent years. The underlying idea of the method is the approximation of posterior densities by discrete random measures. The measures are composed of samples from the states of the unknowns and of weights associated with the samples. The samples are usually referred to as particles and the process of updating the random measures with the arrival of new data as particle filtering. One may view particle filtering as exploration of the space of unknowns .
