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Volume 31 Issue 1
Dec.  2010
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Hong Shao-hua, Shi Zhi-guo, Chen Kang-sheng. Simplified Algorithm and Hardware Implementation for Particle Filter Applied to Bearings-only Tracking[J]. Journal of Electronics & Information Technology, 2009, 31(1): 96-100. doi: 10.3724/SP.J.1146.2007.01166
Citation: Hong Shao-hua, Shi Zhi-guo, Chen Kang-sheng. Simplified Algorithm and Hardware Implementation for Particle Filter Applied to Bearings-only Tracking[J]. Journal of Electronics & Information Technology, 2009, 31(1): 96-100. doi: 10.3724/SP.J.1146.2007.01166

Simplified Algorithm and Hardware Implementation for Particle Filter Applied to Bearings-only Tracking

doi: 10.3724/SP.J.1146.2007.01166
  • Received Date: 2007-07-16
  • Rev Recd Date: 2008-09-29
  • Publish Date: 2009-01-19
  • A simplified particle filter algorithm, which introduces a compact threshold-based resampling algorithm and features lower computing power and hardware complexity, is proposed for the bearings-only tracking problem. Based on the proposed algorithm, this paper lays emphasis on the efficient hardware implementation of particle filters on FPGA platform, and presents the hardware architecture of the resample/sample unit and the whole system. Simulation results show that the simplified algorithm outperforms the extended Kalman filter. Experimental study indicates that the implemented particle filter can be used to solve the bearings-only tracking problem and has rather fast processing rate.
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  • Song T L and Speyer J L. A stochastic analysis of a modifiedgain extended Kalman filter with application to estimationwith bearings-only measurements. IEEE Trans. on AutomaticControl, 1985, AC-30(10): 940-949.[2]郭福成, 李宗化, 孙仲康. 无源定位跟踪中修正协方差扩展卡尔曼滤波算法[J].电子与信息学报.2004, 26(6):917-922浏览[3]Ristic B, Arulampalam S, and Gordon N. Beyond theKalman Filter: Particle Filters for Tracking Applications.Boston, London: Artech House, 2004, Chapter 5-12.[4]Doucet A, de Freitas N, and Gordon N (Eds.). SequentialMonte Carlo Methods in Practice. New York: Springer,2001, Chapter 15-26.[5]Doucet A and Wang X D. Monte Carlo methods for signalprocessing: A review in the statistical signal processingcontext. IEEE Signal Processing Magazine, 2005, 22(6):152-170.[6]Gordon N, Salmond D, and Smith A. Novel approach tononlinear/non-Gaussian Bayesian state estimation[J].IEEProceedings on Radar and Signal Processing.1993, 140(2):107-113[7]Zhai Y and Yeary M. A new particle filter tracking algorithmfor DOA sensor system. Proc. of Instrumentation andMeasurement Technology, Warsaw, 2007: 1-4.[8]Bolic M, Athalye A, and Djuric P M, et al.. Algorithmicmodification of particle filters for hardware implementation.Proc. of the European Signal Processing. Conference, Vienna,Austria, 2004: 1641-1646.[9]Bolic M, Djuric P M, and Hong S. Resampling algorithms forparticle filters: A computational complexity perspective.EURASIP Journal of Applied Signal Processing, 2004, (15):2267-2277.[10]Athalye A, Bolic M, and Hong S, et al.. Generic hardwarearchitectures for sampling and resampling in particle filters.EURASIP Journal of Applied Signal Processing, 2005, (17):2888-2902.
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