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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: Resampling Algorithms for Particle Filters: A Computational Complexity Perspective | EURASIP Journal on Applied Signal Processing 2004 15 2261-2211 2004 Hindawi Publishing Corporation Resampling Algorithms for Particle Filters A Computational Complexity Perspective Miodrag Bolic Department of Electrical and Computer Engineering State University of New York at Stony Brook Stony Brook NY 11794-2350 USA Email mbolic@ece.sunysb.edu 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@ece.sunysb.edu Sangjin Hong Department of Electrical and Computer Engineering State University of New York at Stony Brook Stony Brook NY 11794-2350 UsA Email snjhong@ece.sunysb.edu Received 30 April 2003 Revised 28 January 2004 Newly developed resampling algorithms for particle filters suitable for real-time implementation are described and their analysis is presented. The new algorithms reduce the complexity of both hardware and DSP realization through addressing common issues such as decreasing the number of operations and memory access. Moreover the algorithms allow for use of higher sampling frequencies by overlapping in time the resampling step with the other particle filtering steps. Since resampling is not dependent on any particular application the analysis is appropriate for all types of particle filters that use resampling. The performance of the algorithms is evaluated on particle filters applied to bearings-only tracking and joint detection and estimation in wireless communications. We have demonstrated that the proposed algorithms reduce the complexity without performance degradation. Keywords and phrases particle filters resampling computational complexity sequential implementation. 1. INTRODUCTION Particle filters PFs are very suitable for nonlinear and or non-Gaussian applications. In their operation the main principle is recursive generation of random measures which approximate the distributions of the unknowns. The random measures are composed of particles