吉林大学学报(工学版) ›› 2015, Vol. 45 ›› Issue (5): 1696-1701.doi: 10.13229/j.cnki.jdxbgxb201505045

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Novel particle filter algorithm based on second-prediction

WU Yong, WANG Jun, CAO Yun-he, ZHANG Pei-chuan   

  1. National Laboratory of Radar Signal Processing, Xidian University, Xi'an 710071, China
  • Received:2014-02-20 Online:2015-09-01 Published:2015-09-01

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Abstract: In order to handle the problem that current observation information is not used in the particle sampling stage of the Particle Filter (PF), a novel particle filter algorithm based on Second-Prediction (SP-PF) is proposed and implemented on the Graphics Processing Unit (GPU). First, sampling from the state transition function is performed to obtain the predicted particles, and by least square estimation, a new generator of sampling particles is constructed. Then, on the basis of the predicted particles, the current observation information is introduced into the secondary particle prediction. After double predictions, each new particle is an unbiased estimate of the current state. Finally, the current state is estimated by means of weighting these particles, and after the completion of state estimation, resampling is conducted. Experimental results demonstrate that the proposed algorithm improves the precision of estimation compared with Standard Particle Filter (SPF), Auxiliary Particle Filter (APF) and Unscented Particle Filter (UPF). Furthermore, the processing efficiency of SP-PF is greatly raised by using GPU.

Key words: information processing technology, particle filter, graphics processing unit, least square estimation

CLC Number: 

  • TN911
[1] Sedai S, Bennamoum M, Huynh D Q. A Gausian process guided particle filter for tracking 3D human pose in video[J]. IEEE Transactions on Image Processing, 2013, 22(11): 4286-4300.
[2] Yardim C, Gerstoft P, Hodgkiss W S. Tracking refractivity from clutter using Kalman and particle filters[J]. IEEE Transactions on Antennas and Propagation, 2008, 56(4): 1060-1069.
[3] 夏楠, 邱天爽,李景春,等. 一种卡尔曼滤波与粒子滤波相结合的非线性滤波算法[J]. 电子学报, 2013, 41(1): 148-152. Xia Nan, Qiu Tian-shuang, Li Jing-chun, et al. A nonlinear filter algorithm combining the Kalman filter and the particle filter[J]. Acta Electronica Sinica, 2013, 41(1): 148-152.
[4] Kalman R E. A new approach to linear filtering and prediction problems[J]. Transactions of the AMSE Journal of Basic Engineering, 1960, 82(1): 35-45.
[5] Kotecha J H,Djuric P M. Gaussian particle filter[J]. IEEE Transactions on Signal Processing, 2003, 51(10): 2591-2601.
[6] Li Hong-wei, Wang Jun. Particle filter for manoeuvring target tracking via passive radar measurements with glint noise[J]. IET Radar Sonar and Navigation, 2012, 6(3): 180-189.
[7] Arulampalam M S,Maskell S,Gordon N, et al. A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking[J]. IEEE Transactions on Signal Processing, 2002, 50(2): 174-188.
[8] Georgy Jacques, Korenberg Michael J, Bayoumi Mohamed M. Low-cost three-dimensional navig- ation solution for RISS/GPS integration using mixture particle filter[J]. IEEE Transactions on Vehicular Technology, 2010, 59(2): 599-614.
[9] 戴连君,唐涛,蔡伯根. 基于自适应粒子滤波的北斗卫星信号周跳探测[J]. 吉林大学学报:工学版, 2013, 43(4): 1146-1152. Dai Lian-jun, Tang Tao, Cai Bo-gen. Cycle slip detection for BeiDou satellite based on adaptive particle filter[J]. Journal of Jilin University(Engineering and Technology Edition), 2013, 43(4): 1146-1152.
[10] Wang Ya-feng, Sun Fu-chun,Zhang You-an,et al. Central difference particle filter applied to transfer alignment for SINS on missiles[J]. IEEE Transactions on Aerospace and Electronic Systems,2012, 48(1): 375-387.
[11] Pitt M K,Shephard N. Filtering via simulation: auxiliary particle filters[J]. Journal of the American Statistical Association, 1999, 94(446): 590-599.
[12] Zhan Rong-hui, Xin Qin, Wan Jian-wei. Modified unscented particle filter for nonlinear Bayesian tracking[J]. Journal of Systems Engineering and Electronics, 2008, 19(1): 7-14.
[13] Li Hong-wei, Wang Jun,Su Hong-tao. Improved particle filter based on differential evolution[J].Electronics Letters, 2011, 47(19):1078-1079.
[14] 侯静, 景占荣, 羊彦. 远距离干扰环境下目标跟踪的扩展卡尔曼粒子滤波算法[J]. 电子与信息学报, 2013, 35(7): 1587-1592. Hou Jing, Jing Zhan-rong, Yang Yan. Extended Kalman particle filter algorithm for target tracking in stand-off jammer[J]. Journal of Electronics & Information Technology, 2013, 35(7): 1587-1592.
[15] 于洪波, 王国宏,孙 芸,等. 一种融合UKF和EKF的粒子滤波状态估计算法[J]. 系统工程与电子技术, 2013, 35(7): 1375-1379. Yu Hong-bo, Wang Guo-hong, Sun Yun, et al. Particle filtering algorithm of state estimation on fusion of UKF and EKF[J]. Systems Engineering and Electronics, 2013, 35(7): 1375-1379.
[16] 何友,修建娟,关欣,等. 雷达数据处理及应用[M]. 北京:电子工业出版社, 2013: 76-78.
[17] 李红伟, 王俊, 王海涛. 一种基于差分演化的粒子滤波算法[J]. 电子与信息学报, 2011, 33(7): 1639-1643. Li Hong-wei, Wang Jun, Wang Hai-tao. A new particle filter based on differential evolution method[J]. Journal of Electronics & Information on Technology, 2011, 33(7): 1639-1643.
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