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: A New Class of Particle Filters for Random Dynamic Systems with Unknown Statistics | EURASIP Journal on Applied Signal Processing 2004 15 2278-2294 2004 Hindawi Publishing Corporation A New Class of Particle Filters for Random Dynamic Systems with Unknown Statistics Joaquin Miguez Departamento de Electronica e Sistemas Universidade da Coruna Facultade de Informatica Campus de Elvina s n 15071 A Coruna Spain Email jmiguez@ Monica F. Bugallo Department of Electrical and Computer Engineering State University of New York at Stony Brook Stony Brook NY 11794-2350 USA Email monica@ Petar M. Djuric Department of Electrical and Computer Engineering State University of New York at Stony Brook Stony Brook nY 11794-2350 USA Email djuric@ Received 4 May 2003 Revised 29 January 2004 In recent years particle filtering has become a powerful tool for tracking signals and time-varying parameters of random dynamic systems. These methods require a mathematical representation of the dynamics of the system evolution together with assumptions of probabilistic models. In this paper we present a new class of particle filtering methods that do not assume explicit mathematical forms of the probability distributions of the noise in the system. As a consequence the proposed techniques are simpler more robust and more flexible than standard particle filters. Apart from the theoretical development of specific methods in the new class we provide computer simulation results that demonstrate the performance of the algorithms in the problem of autonomous positioning of a vehicle in a 2-dimensional space. Keywords and phrases particle filtering dynamic systems online estimation stochastic optimization. 1. INTRODUCTION Many problems in signal processing can be stated in terms of the estimation of an unobserved discrete-time random signal in a dynamic system of the form Xt fx xt-1 Ut t 1 2 . 1 yt fy xt Vt t 1 2 . 2 where a xt e Rix is the signal of interest which represents the system state at time t b fx Rix - Ix c Rix is a possibly nonlinear state .