一种混合的扩展目标跟踪方法
收稿日期: 2013-07-23
修回日期: 2013-09-21
网络出版日期: 2013-09-24
基金资助
航空科学基金(20128058006)
A Hybrid Approach for Extended Object Tracking
Received date: 2013-07-23
Revised date: 2013-09-21
Online published: 2013-09-24
Supported by
Aeronautical Science Foundation of China (20128058006)
与传统的目标跟踪不同,扩展目标跟踪(EOT)不忽略目标的轮廓特征,同时对目标的质心运动学状态和轮廓特征进行估计。基于随机矩阵的扩展目标跟踪方法用随机正定矩阵来描述目标的轮廓特征,并且建立了适合扩展目标跟踪的量测模型。为了改善目标机动时的跟踪性能,根据椭圆(体)与正定矩阵的关系,提出基于椭圆(体)拟合的扩展目标跟踪方法。进一步地,为了综合上述两类方法的优点,提出一种混合的扩展目标跟踪方法,能够根据目标机动与否在两类方法中进行选择。仿真结果表明,该混合方法的轮廓特征估计误差低于前述两类方法,质心运动学状态的估计性能也更好。
李波睿 , 慕春棣 , 白天明 , 柳志娟 . 一种混合的扩展目标跟踪方法[J]. 航空学报, 2014 , 35(5) : 1336 -1346 . DOI: 10.7527/S1000-6893.2013.0400
Different from the traditional object tracking technology, extended object tracking (EOT) doesn't ignore the target's physical extension. Instead, EOT simultaneously estimates both the centroid's kinematical state and the physical extension of the target. A random matrix based EOT approach characterizes the physical extension with a random symmetrical positive definite matrix, i.e. the ellipse/ellipsoid, and establishes a measurement model which is suitable for EOT. In order to improve the tracking performance when the target maneuvers, an ellipse/ellipsoid fitting based EOT approach is proposed based on the relationship between the ellipse/ellipsoid and the symmetrical positive definite matrix. Furthermore, a hybrid approach for EOT is presented to combine the advantages of the abovementioned two EOT approaches. Simulation results show that the hybrid approach can appropriately decide whether the target is maneuvering and choose a better approach. The physical extension estimation error of the hybrid approach is lower than the other approaches, and the estimation performance of the centroid's kinematical state is also better.
[1] Bar-Shalom Y, Willett P K, Tian X. Tracking and data fusion: a handbook of algorithms[M]. Storrs: YBS Publishing, 2011: 1-99.
[2] Li X R, Jilkov V P. Survey of maneuvering target tracking. Part V: Multiple-model methods[J]. IEEE Transactions on Aerospace and Electronic Systems, 2005, 41(4): 1255-1321.
[3] Lan J, Mu C D. Input-adaptive models based multiple-model algorithm for maneuvering target tracking[J]. Chinese Journal of Electronics, 2009, 18(1): 84-88.
[4] Luo S H, Xu H, Xu Y, et al. Improved MMPHD method for tracking maneuvering targets[J]. Acta Aeronautica et Astronautica Sinica, 2012, 33(7): 1296-1304. (in Chinese) 罗少华, 徐晖, 徐洋, 等. 改进的MMPHD机动目标跟踪方法[J]. 航空学报, 2012, 33(7): 1296-1304.
[5] Sheng W D, Xu D, Zhou Y Y, et al. Gaussian-mixture probability hypothesis density filter based multitarget tracking algorithm for image plane of scanning optical sensor[J]. Acta Aeronautica et Astronautica Sinica, 2011, 32(3): 497-506. (in Chinese) 盛卫东, 许丹, 周一宇, 等. 基于高斯混合概率假设密度滤波的扫描型光学传感器像平面多目标跟踪算法[J]. 航空学报, 2011, 32(3): 497-506.
[6] Zhuang Z S, Zhang J Q, Yin J J. A kernel particle probability hypothesis density filter for multi-target tracking[J]. Acta Aeronautica et Astronautica Sinica, 2009, 30(7): 1264-1270.(in Chinese) 庄泽森, 张建秋, 尹建君. 多目标跟踪的核粒子概率假设密度滤波算法[J]. 航空学报, 2009, 30(7): 1264-1270.
[7] Li X R, Jilkov V P. Survey of maneuvering target tracking. Part I: Dynamic models[J]. IEEE Transactions on Aerospace and Electronic Systems, 2003, 39(4): 1333-1364.
[8] Koch J W. Bayesian approach to extended object and cluster tracking using random matrices[J]. IEEE Transactions on Aerospace and Electronic Systems, 2008, 44(3): 1042-1059.
[9] Mahler R. PHD filters for nonstandard targets, I: Extended targets//Proceedings of the 12th International Conference on Information Fusion. Seattle: IEEE Computer Society, 2009: 915-921.
[10] Mahler R. PHD filters for nonstandard targets, Ⅱ: Unresolved targets//Proceedings of the 12th International Conference on Information Fusion. Seattle: IEEE Computer Society, 2009: 922-929.
[11] Carmi A, Septier F, Godsill S J. The Gaussian mixture MCMC particle algorithm for dynamic cluster tracking[J]. Automatica, 2012, 48(10): 2454-2467.
[12] Baum M, Hanebeck U D. Shape tracking of extended objects and group targets with star-convex RHMs//Proceedings of the 14th International Conference on Information Fusion. Chicago: IEEE Computer Society, 2011: 1-8.
[13] Feldmann M, Frnken D, Koch W. Tracking of extended objects and group targets using random matrices[J]. IEEE Transactions on Signal Processing, 2011, 59(4): 1409-1420.
[14] Yu Z G, Li Y L, Zhan H S. Linear algebra and analytic geometry[M]. Beijing: Tsinghua University Press, 1998: 319-365. (in Chinese) 俞正光, 李永乐, 詹汉生. 线性代数与解析几何[M]. 北京: 清华大学出版社, 1998: 319-365.
[15] Feldmann M, Frnken D. Tracking of extended objects and group targets using random matrices-a new approach//Proceedings of the 11th International Conference on Information Fusion. Cologne: IEEE Computer Society, 2008: 1-8.
[16] Feldmann M, Frnken D. Advances on tracking of extended objects and group targets using random matrices//Proceedings of the 12th International Conference on Information Fusion. Seattle: IEEE Computer Society, 2009: 1029-1036.
[17] Lan J, Li X R. Tracking of extended object or target group using random matrix-Part I: New model and approach//Proceedings of the 15th International Conference on Information Fusion. Singapore: IEEE Computer Society, 2012: 2177-2184.
[18] Lan J, Li X R. Tracking of extended object or target group using random matrix-Part Ⅱ: Irregular object//Proceedings of the 15th International Conference on Information Fusion. Singapore: IEEE Computer Society, 2012: 2185-2192.
[19] Li B R, Bai T M, Bai Y Q, et al. Model parameter adaptive approach of extended object tracking using random matrix//Proceedings of 4th International Conference on Intelligent Control and Information Processing. Beijing: IEEE Computer Society, 2013: 241-246.
[20] Givens W. Computation of plane unitary rotations transforming a general to a triangular form[J]. Journal of the Society for Industrial and Applied Mathematics, 1958, 6(1): 26-50.
[21] Laub A J. Matrix analysis for scientists and engineers[M]. Philadelphia: Society for Industrial and Applied Mathematics, 2004: 139-150.
[22] Zhang X D. Matrix analysis and applications[M]. Beijing: Tsinghua University Press, 2004: 214-218. (in Chinese) 张贤达. 矩阵分析与应用[M]. 北京: 清华大学出版社, 2004: 214-218.
[23] Zhang Y J. Image engineering[M]. 2nd ed. Beijing: Tsinghua University Press, 2007: 60. (in Chinese) 章毓晋. 图像工程[M]. 2版. 北京: 清华大学出版社, 2007: 60.
[24] Abdi H, Williams L J. Principal component analysis[J]. Wiley Interdisciplinary Reviews: Computational Statistics, 2010, 2(4): 433-459.
[25] Zhang F Z. The Schur complement and its applications[M]. New York: Springer Science+Business Media, Inc., 2005: 1-3.
[26] Calafiore G. Approximation of n-dimensional data using spherical and ellipsoidal primitives[J]. IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans, 2002, 32(2): 269-278.
[27] Wilson E B. Probable inference, the law of succession, and statistical inference[J]. Journal of the American Statistical Association, 1927, 22(158): 209-212.
[28] Han C Z, Zhu H Y, Duan Z S, et al. Multi-source information fusion[M]. 2nd ed. Beijing: Tsinghua University Press, 2010: 259-260. (in Chinese) 韩崇昭, 朱洪艳, 段战胜, 等. 多源信息融合[M]. 2版. 北京: 清华大学出版社, 2010: 259-260.
/
〈 | 〉 |