期刊文献+

一种基于QMC-APF的检测前跟踪算法 被引量:6

Tracking Before Detection Algorithm Based on QMC-APF
下载PDF
导出
摘要 针对粒子滤波检测前跟踪算法中存在的粒子数目大,导致计算量和存储量大的问题,提出了一种基于拟蒙特卡罗的辅助粒子滤波检测前跟踪算法。该算法通过引入拟蒙特卡罗思想,产生低差异序列代替原来算法中的伪随机序列,使得粒子分布更加均匀,可以有效降低粒子数;采用辅助粒子滤波算法,对粒子进行两次加权操作。实验仿真表明,在对雷达弱目标进行检测与跟踪的过程中,该算法能够在保证算法性能的同时减少算法中的粒子数目,有效降低计算量和存储量。 The particle number is large in the tracking before detection algorithm based on particle filter, which result in a problem of a large amount of computation and storage, the auxiliary particle filter tracking before detection algorithm based on quasi-monte carlo is proposed. The quasi-monte carlo algorithm is proposed to replace the original pseudo-random sequence with low-discrepan- cy sequence in the algorithm, which can make a more uniform particle distribution and reduce the number of particles effectively. Using auxiliary particle filter to calculate the particle weight for the twice. The simulation results show that in the process of the radar weak target detection and tracking, the algorithm can guarantee the performance of the algorithm but also can reduce the number of particles in the algorithm, so that the amount of computation and storage are reduced effectively.
出处 《现代雷达》 CSCD 北大核心 2015年第2期33-36,共4页 Modern Radar
基金 国家自然科学基金资助项目(61174024) 浙江省信号处理重点实验室开放基金资助项目(ZJKL_4_SP-OP2014-01)
关键词 弱目标 拟蒙特卡罗 辅助粒子滤波 检测前跟踪 weak target quasi-Monte Carlo auxiliary particle filter tracking before detection
  • 相关文献

参考文献10

  • 1Salmond D J, Birch H. A particle filter for track-before-de-tect [ C ] // IEEE Proceedings of the American Control Con-ference. [S. 1. ] ; IEEE Press, 2001 (5) : 3755—3760. 被引量:1
  • 2胡洪涛,敬忠良,胡士强.基于辅助粒子滤波的红外小目标检测前跟踪算法[J].控制与决策,2005,20(11):1208-1211. 被引量:25
  • 3Huang D M, Pan Q. A new nonlinear filter algorithm basedon QMC quadrature [ C ] // 2008 International Conference onComputer Science and Software Engineering. Wuhan : IEEEPress, 2008: 190-193. 被引量:1
  • 4李倩,姬红兵,郭辉.拟蒙特卡罗-高斯粒子滤波算法研究及其硬件实现[J].电子与信息学报,2010,32(7):1737-1741. 被引量:5
  • 5Zhao Lingling,Ma Peijun,Su Xiaohong. Multiresolutionalquasi-Monte Carlo-based particle filters [ C ]// IEEE Inter-national Conference on Intelligent Computing and IntelligentSystems. Shanghai: IEEE Press,2009: 433-437. 被引量:1
  • 6Lin G H, Xu H F, Masao F. Monte carlo and quasi-montecarlo sampling methods for a class of stochastic mathematicalprograms with equilibrium constraints [ J ]. MathematicalMethods of Operations Research, 2008,67(3) : 423 - 441. 被引量:1
  • 7Boers Y,Driessen J N. Multi target particle filter track be-fore detect application [ J ]. IEE Proc Radar Sonar Naviga-tion, 2004, 151(6) : 351-357. 被引量:1
  • 8Boers Y, Driessen H. A particle-filter-based detection sch-eme [J ]. IEEE Signal Processing Letters, 2003,10(10):300-302. 被引量:1
  • 9Pitt M K, Shephard N. Filtering via simulation: auxiliaryparticle filters [ J ]. Journal of the American statistical asso-ciation, 1999, 94(446) ; 590-599. 被引量:1
  • 10Boers Y,Driessen H. A particle-filter-based detection sch-eme [ J ]. IEEE Signal Processing Letters, 2003 ,10( 10);300-302. 被引量:1

二级参考文献17

  • 1许彬,郑链,王永学,宋承天.红外序列图像小目标检测与跟踪技术综述[J].红外与激光工程,2004,33(5):482-487. 被引量:27
  • 2刘志刚,卢焕章,陈辉煌.一种低信噪比下点目标检测新算法[J].系统工程与电子技术,2004,26(11):1588-1591. 被引量:10
  • 3Yardim C, Gerstoft P, and Hodgkiss W S. Tracking refractivity from clutter using Kalman and particle filters. IEEE Transactions on Antennas and Propagation, 2008, 56(4): 1058-1070. 被引量:1
  • 4Kotecha J H and Djuric P M. Gaussian particle filtering. IEEE Transactions on Signal Processing, 2003, 51(10): 2592-2601. 被引量:1
  • 5Bolic M, Athalye A, Djuric P M, and Hong S. Algorithmic modification of particle filters for hardware implementation. Proc. of the European Signal Processing. Conference, Vienna, Austria, 2004: 1641-1646. 被引量:1
  • 6Lin G H, Xu H F, and Masao F. Monte Carlo and quasi-Monte Carlo sampling methods for a class of stochastic mathematical programs with equilibrium constraints. Mathematical Methods o/Operations Research, 2008, 67(3): 423-441. 被引量:1
  • 7Wu Y X, Hu X P, and Hu D W. Comments on Gaussian Particle Filtering. IEEE Transactions on Signal Processing, 2005, 53(8): 3350-3351. 被引量:1
  • 8Wolfgang J. Quasi-Monte Carlo sampling to improve the efficiency of Monte Carlo EM. Computational Statistics & Data Analysis, 2005, 48(4): 685-701. 被引量:1
  • 9Bolic M, Djuric P M, and Hong S. Resampling algorithms and architectures for distributed particle filters. IEEE Transactions on Signal Processing, 2005, 53(7): 2442-2450. 被引量:1
  • 10Wolfgang C S and Andreas U. Techniques for parallel quasi-Monte Carlo integration with digital sequences and associated problems. Mathematics and Computers in Simulation, 2001, 55(1-3): 249-257. 被引量:1

共引文献28

同被引文献36

引证文献6

二级引证文献6

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部