Research and Hardware Implementation of Quasi-Monte-Carlo Gaussian Particle Filter

Li Qian; Ji Hong-bing; Guo Hui

doi:10.3724/SP.J.1146.2009.01002

Volume 32 Issue 7

Aug. 2010

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Article Navigation > Journal of Electronics & Information Technology > 2010 > 32(7): 1737-1741

Li Qian, Ji Hong-bing, Guo Hui. Research and Hardware Implementation of Quasi-Monte-Carlo Gaussian Particle Filter[J]. Journal of Electronics & Information Technology, 2010, 32(7): 1737-1741. doi: 10.3724/SP.J.1146.2009.01002

Citation:

Li Qian, Ji Hong-bing, Guo Hui. Research and Hardware Implementation of Quasi-Monte-Carlo Gaussian Particle Filter[J]. Journal of Electronics & Information Technology, 2010, 32(7): 1737-1741. doi: 10.3724/SP.J.1146.2009.01002

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Research and Hardware Implementation of Quasi-Monte-Carlo Gaussian Particle Filter

doi: 10.3724/SP.J.1146.2009.01002

Received Date: 2009-07-14
Rev Recd Date: 2009-12-01
Publish Date: 2010-07-19

Abstract

Abstract

A large amount of computation of particle filter limits its engineering application. According to this problem, Quasi-Monte Carlo (QMC) sampling is used to replace Monte Carlo (MC) sampling, reducing the required computation. Quasi-Monte-Carlo Gaussian Particle Filter (QMC-GPF) algorithm parallel architecture is proposed. Based on the parallel architecture, this paper lays emphasis on the implementation of the algorithm on FPGA in detail. Base 2 is used to generate Faure sequences, thus instead of multiplication and mod only bitwise XOR, which is easily to realize on FPGA, is needed to generate the sequences. Look-up tables are used in calculating the complex functions such as exponential function, which makes full use of the large number of Block RAM of FPGA. The parallel structure is designed to compute the elements of the Cholesky decomposition matrix. Infrared imaging dim small target tracking is realized on FPGA and the results show the efficiency and real time of the design.

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References(1)

References

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