Đang chuẩn bị liên kết để tải về tài liệu:
Báo cáo hóa học: " Global Sampling for Sequential Filtering over Discrete State Space"

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: Global Sampling for Sequential Filtering over Discrete State Space | EURASIP Journal on Applied Signal Processing 2004 15 2242-2254 2004 Hindawi Publishing Corporation Global Sampling for Sequential Filtering over Discrete State Space Pascal Cheung-Mon-Chan Ecole Nationale Superieure des Telecommunications 46 rue Barrault 75634 Paris Cedex 13 France Email pcheung@tsi.enst.fr Eric Moulines Ecole Nationale Superieure des Telecommunications 46 rue Barrault 75634 Paris Cedex 13 France Email moulines@tsi.enst.fr Received 21 June 2003 Revised 22 January 2004 In many situations there is a need to approximate a sequence of probability measures over a growing product of finite spaces. Whereas it is in general possible to determine analytic expressions for these probability measures the number of computations needed to evaluate these quantities grows exponentially thus precluding real-time implementation. Sequential Monte Carlo techniques SMC which consist in approximating the flow of probability measures by the empirical distribution of a finite set of particles are attractive techniques for addressing this type of problems. In this paper we present a simple implementation of the sequential importance sampling resampling SISR technique for approximating these distributions this method relies on the fact that the space being finite it is possible to consider every offspring of the trajectory of particles. The procedure is straightforward to implement and well-suited for practical implementation. A limited Monte Carlo experiment is carried out to support our findings. Keywords and phrases particle filters sequential importance sampling sequential Monte Carlo sampling sequential filtering conditionally linear Gaussian state-space models autoregressive models. 1. INTRODUCTION State-space models have been around for quite a long time to model dynamic systems. State-space models are used in a variety of fields such as computer vision financial data analysis mobile communication radar systems among others. A main challenge is to design efficient .