论文信息 - A flexible resampling mechanism for parallel particle filters

A flexible resampling mechanism for parallel particle filters

This paper presents an efficient flexible resampling architecture for parallel particle filtering. The architecture incorporates distributed, delayed resampling mechanisms for fast resampling processing. The architecture consists up to four resampling units and 16 processing elements. Their interconnection can be dynamically reconfigured. The architecture is designed and evaluated for bearing tracking example. The architecture is designed for 0.25 /spl mu/m CMOS technology.

Sangjin Hong | Shu-Shin Chin | S. Magesh

[1] N. G. Best,et al. Dynamic conditional independence models and Markov chain Monte Carlo methods , 1997 .

[2] Nicholas G. Polson,et al. A Monte Carlo Approach to Nonnormal and Nonlinear State-Space Modeling , 1992 .

[3] R. Kohn,et al. Markov chain Monte Carlo in conditionally Gaussian state space models , 1996 .

[4] Simon J. Godsill,et al. On sequential Monte Carlo sampling methods for Bayesian filtering , 2000, Stat. Comput..

[5] Stuart Haber,et al. A VLSI-efficient technique for generating multiple uncorrelated noise sources and its application to stochastic neural networks , 1991 .