基于ET-PHD的自适应联合跟踪与分类算法

樊鹏飞; 李鸿艳

doi:10.16383/j.aas.2018.c170371

基于ET-PHD的自适应联合跟踪与分类算法

doi: 10.16383/j.aas.2018.c170371

樊鹏飞^,,
李鸿艳

1.
空军工程大学信息与导航学院西安 710077

基金项目:

陕西省自然科学基础研究计划（2015JM6332）资助

详细信息

作者简介:
李鸿艳空军工程大学信息与导航学院副教授.主要研究方向为信息融合,目标跟踪.E-mail:lihongyan71@sohu.com

通讯作者:
樊鹏飞空军工程大学信息与导航学院硕士研究生.主要研究方向为扩展目标跟踪.本文通信作者.E-mail:13072972016@163.com

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- 被引次数: 0
出版历程
- 收稿日期: 2017-07-05
- 修回日期: 2017-11-17
- 刊出日期: 2019-02-20

Adaptive Joint Tracking and Classification Algorithm Using ET-PHD Filter

FAN Peng-Fei^,,
LI Hong-Yan

1.
Information and Navigation College, Air Force Engineering University, Xi'an 710077

Funds:

Supported by Natural Science Basic Research Plan in Shaanxi Province (2015JM6332)

摘要

摘要: 针对新生目标强度先验未知的扩展目标（Extended target，ET）联合跟踪与分类（Joint tracking and classification，JTC）问题，提出一种基于扩展目标概率假设密度（Extended target-probability hypothesis density，ET-PHD）滤波器的自适应联合跟踪与分类算法，并给出其高斯混合实现方法.算法利用量测信息生成新生目标强度，在滤波预测阶段对存活目标和新生目标分别按照其类别进行传播，再引入属性量测信息，用位置和属性的联合量测似然函数代替单目标位置似然函数，对预测后所有目标强度进行联合更新，之后按照类别进行高斯项的删减与合并，提取相应类别目标的状态集.仿真结果表明，提出的自适应算法改进了概率假设密度滤波器在扩展目标跟踪中的性能.
- 扩展目标 /
- 联合跟踪与分类 /
- 新生目标强度 /
- 概率假设密度
Abstract: For joint tracking and classification (JTC) of extended targets and unknown target birth intensity, an adaptive algorithm based on the extended target probability hypothesis density (ET-PHD) filter is proposed with the Gaussian mixture implementation. The main idea is to approximate the birth intensity by using received measurements, and in the prediction stage the persistent and the newborn targets are distinguished to propagate according to their classes. Then the target classification information is integrated into the update stage and the combined measurement likelihood is used to substitute the single target position likelihood. A joint update is implemented by all predicted posterior intensities and then the Gaussian mixture components are pruned and merged according to their classes to characterize corresponding target state sets. Simulation results show that the adaptive algorithm can improve the performance of probability hypothesis density filter in the extended target tracking.
- Extended target (ET) /
- joint tracking and classification (JTC) /
- target birth intensity /
- probability hypothesis density (PHD)

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参考文献(21)

[1]	Shan Gan-Lin, Mei Wei, Wang Chun-Ping. Advance and challenge in joint target tracking and classification. Acta Armamentarii, 2007, 28(6):733-738(单甘霖, 梅卫, 王春平. 联合目标跟踪与分类技术的进展及存在问题. 兵工学报, 2007, 28(6):733-738)
[2]	Lundquist C, Orguner U, Gustafsson F. Extended target tracking using polynomials with applications to road-map estimation. IEEE Transactions on Signal Processing, 2011, 59(1):15-26
[3]	Lan J, Li X R. Joint tracking and classification of extended object using random matrix. In:Proceedings of the 16th International Conference on Information Fusion (FUSION). Istanbul, Turkey:IEEE, 2013. 1550-1557
[4]	Lan J, Li X R. Joint tracking and classification of non-ellipsoidal extended object using random matrix. In:Proceedings of the 17th International Conference on Information Fusion (FUSION). Salamanca, Spain:IEEE, 2014. 1-8
[5]	Cao W, Lan J, Li X R. Extended object tracking and classification based on recursive joint decision and estimation. In:Proceedings of the 16th International Conference on Information Fusion (FUSION). Istanbul, Turkey:IEEE, 2013. 1670-1677
[6]	Sun L F, Lan J, Li X R. Joint tracking and classification of extended object based on support functions. In:Proceedings of the 17th International Conference on Information Fusion (FUSION). Salamanca, Spain:IEEE, 2014. 1-8
[7]	Mahler R P S. Multitarget Bayes filtering via first-order multitarget moments. IEEE Transactions on Aerospace and Electronic Systems, 2003, 39(4):1152-1178
[8]	Yang Wei, Fu Yao-Wen, Li Xiang, Long Jian-Qian. Joint detection, tracking and classification algorithm for multiple maneuvering targets based on LGJMS-GMPHDF. Journal of Electronics & Information Technology, 2012, 34(2):398-403(杨威, 付耀文, 黎湘, 龙建乾. 基于LGJMS-GMPHDF的多机动目标联合检测、跟踪与分类算法. 电子与信息学报, 2012, 34(2):398-403)
[9]	Yang Wei, Fu Yao-Wen, Li Xiang, Long Jian-Qian. Joint detection, tracking and classification algorithm of multiple maneuvering targets using MMPHDF. Scientia Sinica Informationis, 2012, 42(7):893-906(杨威, 付耀文, 黎湘, 龙建乾. 基于MMPHDF的多机动目标联合检测、跟踪与分类算法. 中国科学:信息科学, 2012, 42(7):893-906)
[10]	Gilholm K, Salmond D. Spatial distribution model for tracking extended objects. IEEE Proceedings-Radar, Sonar, and Navigation, 2005, 152(5):364-371
[11]	Mahler R. PHD filters for nonstandard targets, I:extended targets. In:Proceedings of the 12th International Conference on Information Fusion (FUSION). Seattle, WA, USA:IEEE, 2009. 915-921
[12]	Granstrom K, Lundquist C, Orguner U. Extended target tracking using a Gaussian-mixture PHD filter. IEEE Transactions on Aerospace and Electronic Systems, 2012, 48(4):3268-3286
[13]	Lian Feng, Han Chong-Zhao, Liu Wei-Feng, Yuan Xiang-Hui. Convergence analysis of the Gaussian mixture extended-target probability hypothesis density filter. Acta Automatica Sinica, 2012, 38(8):1343-1352(连峰, 韩崇昭, 刘伟峰, 元向辉. 高斯混合扩展目标概率假设密度滤波器的收敛性分析. 自动化学报, 2012, 38(8):1343-1352)
[14]	Lian F, Han C Z, Liu W F, Liu J, Sun J. Unified cardinalized probability hypothesis density filters for extended targets and unresolved targets. Signal Processing, 2012, 92(7):1729-1744
[15]	Tian Sen-Ping, Zhou Bo, Qi Qi-Feng. Gaussian mixture PHD filter based tracking multiple maneuvering extended targets. Journal of Central South University (Science and Technology), 2013, 44(12):4923-4929(田森平, 周波, 戚其丰. 基于高斯混合PHD滤波的多机动扩展目标跟踪. 中南大学学报(自然科学版), 2013, 44(12):4923-4929)
[16]	Ristic B, Clark D, Vo B N, Vo B T. Adaptive target birth intensity for PHD and CPHD filters. IEEE Transactions on Aerospace and Electronic Systems, 2012, 48(2):1656-1668
[17]	Xu Cong-An, Xiong Wei, Liu Yu, He You. A single measurement PHD filter with unknown target birth intensity. Acta Electronica Sinica, 2016, 44(10):2300-2307(徐从安, 熊伟, 刘瑜, 何友. 新生目标强度未知的单量测PHD滤波器. 电子学报, 2016, 44(10):2300-2307)
[18]	He X F, Tharmarasa R, Pelletier M, Kirubarajan T. Two-level automatic multiple target joint tracking and classification. In:Proceedings of the 2010 SPIE, the International Society for Optical Engineering. 7698. Orlando, FL, USA:SPIE, 2010, 7698:Article No. 769800
[19]	Magnant C, Giremus A, Grivel E, Ratton L, Joseph B. Multi-target tracking using a PHD-based joint tracking and classification algorithm. In:Proceedings of the 2016 IEEE Radar Conference (RadarConf). Philadelphia, PA, USA:IEEE, 2016. 1-6
[20]	Xu L, Krzyzak A, Suen C Y. Methods of combining multiple classifiers and their applications to handwriting recognition. IEEE Transactions on Systems, Man, and Cybernetics, 1992, 22(3):418-435
[21]	Schuhmacher D, Vo B T, Vo B N. A consistent metric for performance evaluation of multi-object filters. IEEE Transactions on Signal Processing, 2008, 56(8):3447-3457