基于核密度的动态初始化重置粒子滤波

doi:10.3724/SP.J.1087.2012.00295

计算机应用 ›› 2012, Vol. 32 ›› Issue (01): 295-298.DOI: 10.3724/SP.J.1087.2012.00295

• 典型应用 • 上一篇

基于核密度的动态初始化重置粒子滤波

白剑锋,南建国,邬蒙,查翔

空军工程大学工程学院，西安 710038

收稿日期:2011-06-22 修回日期:2011-09-03 发布日期:2012-02-06 出版日期:2012-01-01
通讯作者: 白剑锋
作者简介:白剑锋(1985-)，男，河北沧州人，硕士研究生，主要研究方向：目标跟踪、信息融合；南建国(1969-)，男，陕西西安人，副教授，博士，主要研究方向：目标跟踪、信息融合；邬蒙(1979-)，男，陕西西安人，讲师，硕士，主要研究方向：目标跟踪、信息融合；查翔(1988-)，男，山东泰安人，硕士研究生，主要研究方向：目标跟踪、信息融合。

Dynamic initialization reset algorithm for particle filtering based on kernel density

BAI Jian-fengNAN Jian-guo²,WU Meng²,ZHA Xiang²

Received:2011-06-22 Revised:2011-09-03 Online:2012-02-06 Published:2012-01-01
Contact: BAI Jian-feng

摘要/Abstract

摘要： 针对粒子滤波过程中，长时间的重采样造成的粒子多样性枯竭，由此导致目标跟踪中出现的精度下降及跟踪轨迹大幅振荡的现象，通过对采样粒子分布规律的研究，根据粒子枯竭的程度设置重置门限，在滤波过程中实时地检测粒子枯竭参数，当粒子的枯竭超过设置门限时，采用重置初始化粒子的方法来缓解采样粒子的枯竭趋势，有效地增加了长时间大量重采样后粒子的多样性，避免了粒子所含信息过多的丢失，显著地提高了粒子滤波的精度，在二维目标跟踪模型中应用所提算法并进行仿真实验，仿真结果证明了算法的可行有效。

关键词: 核密度, 粒子滤波, 采样粒子, 机动目标跟踪, 重采样

Abstract: It has been found that the accuracy of particle filtering is much lower when the maneuvering target tracking process has been executed for a long time. The reason for this problem is that the diversity of the sampled particles is rapidly lost because of the excessive resampling. Therefore, the track of the maneuvering target estimated by the particle filtering will be widely wiggly from the true one. Through the research of the distribution of the sampled particles, a new algorithm was proposed. And a detected threshold was set to detect if the particle was dried up badly. When the particle was dried up badly, the particles of the state-space would be reset to relax the degree, so the new particles could contain more distribution information. The new algorithm has a high capability in the simulation of the 2-D maneuvering target tracking.

Key words: kernel density, particle filtering, sampled particle, maneuvering target tracking, resampling

中图分类号:

白剑锋南建国邬蒙查翔. 基于核密度的动态初始化重置粒子滤波[J]. 计算机应用, 2012, 32(01): 295-298.

BAI Jian-feng NAN Jian-guo WU Meng ZHA Xiang. Dynamic initialization reset algorithm for particle filtering based on kernel density[J]. Journal of Computer Applications, 2012, 32(01): 295-298.

参考文献

[1]

JOHANSEN A M, DOUCET A. A note on auxiliary particle filters ［J］. Statistics and Probability Letters, 2008, 78(12): 1498-1504.

[2]

KWORK C, FOX D, MEILA M. Real-time particle filters ［J］. Proceedings of the IEEE, 2004, 92(3): 469-484.

[3]

DOUCET A, JOHANSEN A M. A tutorial on particle filtering and smoothing: Fifteen years later ［R］. Vancouver, BC: University of British Columbia, Department of Statistics, 2008: 12-14.

[4]

DOUCET A, BRIERS M, SENECAL S. Efficient block sampling strategies for sequential Monte Carlo methods ［J］. Journal of Computational and Graphical Statistics, 2006, 15(3): 693-711.

[5]

VO B-N, VO B-T, SINGH S. Sequential Monte Carlo methods for static parameter estimation in random set models ［C］// Proceedings of the 2004 Intelligent Sensors, Sensor Networks and Information Processing Conference. Washington, DC: IEEE Computer Society, 2004: 313-318.

[6]

CLARK D E, BELL J. Convergence results for the particle PHD filter ［J］. IEEE Transactions on Signal Processing, 2006, 54(7): 2652-2661.

[7]

冯驰,王萌,汲清波,等.粒子滤波器重采样算法的分析与比较［J］.系统仿真学报,2009,21(4):1101-1110.

[8]

KARLSSON R, BERGMAN N. Auxiliary particle filters for tracking a maneuvering target ［C］// Proceedings of the 39th IEEE Conference on Decision and Control. Washington, DC: IEEE Computer Society, 2000: 3891-3895.

[9]

FEARNHEAD P. Computational methods for complex stochastic systems: A review of some alternatives to MCMC ［J］. Statistics and Computing, 2008, 18(2): 151-171.

[10]

杜正聪,唐斌,李可.混合退火粒子滤波器［J］.物理学报,2006,55(3):999 -1004.

[11]

BRASNETT P, MIHAYLOVA L, BULL D, et al. Sequential Monte Carlo tracking by fusing multiple cues in video sequences ［J］. Image and Vision Computing, 2007, 25(8): 1217-1227.

[12]

KIM J S, SERPEDIN E, SHIN D R. Improved particle filtering-based estimation of the number of competing stations in IEEE 802.11 networks ［J］. IEEE Signal Processing Letters, 2008, 15(1): 87-90.

[13]

莫以为,萧德云.基于进化粒子滤波器的混合系统故障诊断［J］.控制与决策,2004,19(6):611-615.

[14]

胡士强,敬忠良.粒子滤波算法综述［J］.控制与决策,2005,20(4):361-365.

[15]

GIREMUS A, TOURNERET J Y, CALMETTES V. A particle filtering approach for joint detection/estimation of multipath effects on GPS measurements ［J］. IEEE Transactions on Signal Processing, 2007, 55(4): 1275-1285.

[16]

DUAN ZHUOHUA, CAI ZIXING, YU JINXIA. An adaptive particle filter for soft fault compensation of mobile robots ［J］. Science in China Series F: Information Sciences, 2008, 51(12): 2033-2046.

基于核密度的动态初始化重置粒子滤波

Dynamic initialization reset algorithm for particle filtering based on kernel density

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

[1]	周德运, 刘斌, 苏茜. 基于“新息误差”的粒子流滤波算法[J]. 计算机应用, 2020, 40(11): 3127-3132.
[2]	韩玉兰, 韩崇昭. 非线性系统的多扩展目标跟踪算法[J]. 计算机应用, 2019, 39(5): 1318-1324.
[3]	刘靖, 肖冠烽. 基于Spark与粒子滤波算法的公交到站时间预测系统[J]. 计算机应用, 2019, 39(2): 429-435.
[4]	许朝, 孟凡荣, 袁冠, 李月娥, 刘肖. 融合地点影响力的兴趣点推荐算法[J]. 计算机应用, 2019, 39(11): 3178-3183.
[5]	阮琨, 王玫, 罗丽燕, 熊璐琦, 宋浠瑜. 基于智能手机的地磁/WiFi/PDR的室内定位算法[J]. 计算机应用, 2018, 38(9): 2598-2602.
[6]	刘盼, 张榜, 黄超, 杨卫军, 徐正蓺. 室内环境约束的行人航向粒子滤波修正方法[J]. 计算机应用, 2018, 38(12): 3360-3366.
[7]	陈宏昆, 察豪, 刘立国, 孟薇. 基于船舶自动识别系统信息和Hough变换的海上船舶航道提取[J]. 计算机应用, 2018, 38(11): 3332-3335.
[8]	魏昊, 徐庆. 基于冗余分组的轨迹摘要算法[J]. 计算机应用, 2017, 37(5): 1503-1506.
[9]	高国栋, 林明, 许兰. 基于似然分布调整的粒子群优化粒子滤波新方法[J]. 计算机应用, 2017, 37(4): 980-985.
[10]	周瑞, 李志强, 罗磊. 基于粒子滤波的WiFi行人航位推算融合室内定位[J]. 计算机应用, 2016, 36(5): 1188-1191.
[11]	韩雪, 程奇峰, 赵婷婷, 张利民. 基于粒子滤波重采样与变异操作的改进粒子群算法[J]. 计算机应用, 2016, 36(4): 1008-1014.
[12]	陈伟强, 陈军, 张闯, 宋立国, 谭卓理. 基于智能粒子滤波的多传感器信息融合算法[J]. 计算机应用, 2016, 36(12): 3358-3362.
[13]	赵海军, 崔梦天, 李明东, 李佳. 基于改进的洪泛广播和粒子滤波的无线传感器网络节点定位[J]. 计算机应用, 2016, 36(10): 2659-2663.
[14]	李奕诺, 肖如良, 倪友聪, 苏小敏, 杜欣, 蔡声镇. 基于动态环境衰减的粒子滤波室内定位算法[J]. 计算机应用, 2015, 35(9): 2465-2469.
[15]	杨大勇, 杨建华, 卢伟. 基于动态阈值的核密度估计前景检测算法[J]. 计算机应用, 2015, 35(7): 2033-2038.