基于分块EnKF的非线性目标跟踪算法

崔波; 张家树; 杨宇

doi:10.3969/j.issn.0258-2724.2013.05.013

基于分块EnKF的非线性目标跟踪算法

doi: 10.3969/j.issn.0258-2724.2013.05.013

基金项目:

国家自然科学基金资助项目(60971104)

中央高校基本科研业务费专项资金资助项目(SWJTU11BR179)

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出版历程
- 收稿日期: 2012-01-14
- 刊出日期: 2013-10-25

Nonlinear Target Tracking Algorithm Based on Block Ensemble Kalman Filter

摘要

摘要: 针对滤波航迹的相关性以及初始状态的选择会对跟踪性能产生影响的问题,将集合卡尔曼滤波算法引入到非线性目标跟踪领域,验证了其可行性和有效性,提出了基于分块集合卡尔曼滤波的非线性目标跟踪算法.采用分块思想生成初始集合,使用协方差矩阵加权方法解决分块间的航迹相关问题.仿真结果表明基于分块集合卡尔曼滤波的目标跟踪算法计算复杂度和以往的集合卡尔曼滤波算法同阶的情况下可以提供更高的运动参数估计精度,解决了粒子滤波算法计算量大难以进行实时跟踪的问题.
- 集合卡尔曼滤波 /
- 非线性滤波 /
- 目标跟踪 /
- 分块
Abstract: As target tracking performance depends on correlated tracks and the selection of filter initial states, ensemble Kalman filter was introduced to nonlinear target tracking system, and the feasibility and validity of the system were verified. A new target tracking algorithm based on block ensemble Kalman filter was proposed, where initial ensemble was produced by the block method, and covariance matrix weighting was used for all the blocks in the target tracking process. The simulation results show that the algorithm based on block ensemble Kalman filter has the same computational complexity as previous ensemble Kalman filter while offers higher estimation accuracy for motion parameters, and can fulfill real-time tracking in contrast to high computational complexity of particle filter.
- ensemble Kalman filter /
- nonlinear filtering /
- target tracking /
- block

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参考文献(15)

GORDON N, SALMOND D. Novel approach to non-linear and non-Gaussian Bayesian state estimation[J]. Proc of institute electric engineering, 1993, 140(2): 107-113.

WANG Xuezhi, DARKO M, SICKI R E, et al. Efficient and enhanced multi-target tracking with Doppler measurements[J]. IEEE Transactions on Aerospace and Electronic Systems, 2009, 45(4): 1400-1417.

FOO P H. Combining the interacting multiple model method with particle filters for maneuvering target tracking with a multistatic radar system[J]. IET Radar, Sonar and Navigation, 2011, 5(7): 697-706.

EVENSEN G. Sequential data assimilation with a nonlinear quasigeostrophic model using Monte Carlo methods to forecast error statistics[J]. Journal of Geophysical Research, 1994, 99(C5): 10143-10162.

EVENSEN G. The ensemble Kalman filter: theoretical formulation and practical implementation[J]. Ocean Dynamics, 2003, 53(4): 343-367.

JOHN D B, WADE T C, ZHAN Xiwu, et al. Evaluating the utility of remotely sensed soil moisture retrievals for operational agricultural drought monitoring[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2010, 3(1): 57-66.

MITCHELL H L, HOUTEKAMER P L, PELLERIN G. Ensemble size, balance, and model-error representation in an ensemble Kalman filter[J]. Monthly Weather Review, 2002, 130(11): 2791-2808.

SNYDER C, ZHANG F. Assimilation of simulated Doppler radar observations with an ensemble kalman filter[J]. Monthly Weather Review, 2003, 131(8): 1663-1677.

DOWELL D C, Zhang Fuqing, WICKER L J. Wind and temperature retrievals in the 17 May 1981 Arcadia, Oklahoma, supercell: Ensemble Kalman filter experiments[J]. Monthly Weather Review, 2004, 132(8): 1982-2005.

CUI Bo, ZHANG Jiashu. The improved ensemble Kalman filter for multi-sensor target tracking[C]//2008 International Symposium on Information Science and Engineering. Washington D C: IEEE Computer Society, 2008: 263-265.

ZHOU Yucheng, XU Jiahe, JING Yuanwei, et al. Extended target tracking using an IMM based nonlinear kalman Filters[C]//2010 American Control Conference. Marriott Waterfront. Baltimore: [s.n.], 2010: 6876-6881.

XU Jiahe, ZHOU Yucheng, JING Yuanwei. Extended target tracking for high resolution sensor based ensemble kalman filters[C]//Chinese Control and Decision Conference. Xuzhou: IEEE, 2010: 3308-3313.

PORNSARAYOUTH S, YAMAKITA M. Ensemble Kalman filter for multisensor fusion with multistep delayed measurements[C]//IEEE Aerospace Conference. Big Sky, MT: IEEE, 2011: 1-10.

张学峰,黄大吉,章本照. 集合数据同化方法的发展与应用概述[J]. 海洋学研究,2007,25(1): 88-94. ZHANG Xuefeng, HUANG Daji, ZHANG Benzhao. The developments and applications of ensemble-based data assimilation methods[J]. Journal of Marine Sciences, 2007, 25(1): 88-94.

杜航原,郝燕玲,赵玉新. 基于集合卡尔曼滤波的改进粒子滤波算法[J]. 系统工程与电子技术,2011,33(7): 1653-1657. DU Hangyuan, HAO Yanling, ZHAO Yuxin. Improved particle filter based on ensemble Kalman filter[J]. Systems Engineering and Electronics, 2011, 33(7): 1653-1657.

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文章访问数: 1034
HTML全文浏览量: 89
PDF下载量: 494
被引次数: 0

基于分块EnKF的非线性目标跟踪算法

doi: 10.3969/j.issn.0258-2724.2013.05.013

计量

出版历程

Nonlinear Target Tracking Algorithm Based on Block Ensemble Kalman Filter

计量

出版历程

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