一种改进的高斯逆威沙特概率假设密度扩展目标跟踪算法

李文娟; 吕靖; 顾红; 苏卫民; 马超; 杨建超

doi:10.11999/JEIT170883

一种改进的高斯逆威沙特概率假设密度扩展目标跟踪算法

doi: 10.11999/JEIT170883

基金项目:

国家自然科学基金(61471198, 61671246)，江苏省自然科学基金(BK20160847, BK20170855)

计量
- 文章访问数: 1733
- HTML全文浏览量: 195
- PDF下载量: 258
- 被引次数: 0
出版历程
- 收稿日期: 2017-09-19
- 修回日期: 2018-03-16
- 刊出日期: 2018-06-19

Improved Gaussian Inverse Wishart Probability Hypothesis Density for Extended Target Tracking

Funds:

The National Natural Science Foundation of China (61471198, 61671246), The Natural Science Foundation of Jiangsu Province (BK20160847, BK20170855)

摘要

摘要: 假设扩展目标(ET)的扩展和量测数目分别为椭圆和泊松模型，高斯逆威沙特概率假设密度(GIW-PHD)能够估计扩展目标的运动和扩展状态。然而，该滤波器对空间邻近目标的数目、非椭圆目标和受到遮挡目标的扩展估计不够准确。针对这些问题，该文提出一种改进的GIW-PHD。首先，假设目标扩展为一个相同尺寸的参考椭圆，通过设计新的散射矩阵得到改进的随机矩阵(RM)方法。然后，将改进的RM方法与假设量测数目服从多伯努利分布的ET-PHD结合，得到改进的GIW-PHD滤波器。仿真和实验结果表明，与传统GIW-PHD相比，改进的GIW- PHD估计的目标数目和量测数目较多，扩展较大的椭圆和非椭圆目标的扩展更准确。
- 扩展目标跟踪 /
- 高斯逆威沙特概率假设密度 /
- 随机矩阵 /
- 多伯努利分布
Abstract: Assumed that extension and measurement number of Extended Targets (ET) are respectively modeled as ellipse and Poisson, a Gaussian Inverse Wishart Probability Hypothesis Density (GIW-PHD) filter can estimate kinematic and extension states. However, for the number of spatially close targets and the extensions of non-ellipsoidal and occluded targets, the results estimated by this filter are not accurate enough. In view of these problems, an improved GIW-PHD filter is proposed in this paper. Firstly, assumed that target extension is modeled as a reference ellipse of the same size, a modified Random Matrix (RM) method is obtained by devising a new scatter matrix. Then, combining the improved RM method with the ET-PHD based on a measurement number multi-Bernoulli model, the improved GIW-PHD filter is obtained. Simulated and experimental results show that, compared with the traditional GIW-PHD, the improved GIW-PHD filter can obtain more accurate estimates in target number and the extensions of ellipsoidal and non-ellipsoidal targets with large measurement number and extensions.
- Extended target tracking /
- Gaussian Inverse Wishart Probability Hypothesis Density (GIW-PHD) /
- Random Matrix (RM) /
- Multi-Bernoulli distribution

HTML全文

参考文献(18)

GILHOLM K and SALMOND D. Spatial distribution model for tracking extended objects[J]. IET Radar, Sonar Navigation, 2005, 152(5): 364-371. doi: 10.1049/ip-rsn: 20045114.

MAHLER R. Statistical Multisource-Multitarget Information Fusion[M]. Norwood, MA: Artech House, 2007: 193-360.

VO B and MA W. The Gaussian mixture probability hypothesis density filter[J]. IEEE Transactions on Signal Processing, 2006, 54(11): 4091-4104. doi: 10.1109/TSP.2006. 881190.

GRANSTROM K, LUNDQUIST C, and ORGUNER U. Extended target tracking using a Gaussian mixture PHD filter[J]. IEEE Transactions on Aerospace and Electronic Systems, 2012, 48(4): 3268-3286. doi: 10.1109/TAES.2012. 6324703.

李文娟, 顾红, 苏卫民. 基于多伯努利概率假设密度的扩展目标跟踪方法[J]. 电子与信息学报, 2016, 38(12): 3114-3121. doi: 10.11999/JEIT160372.

LI Wenjuan, GU Hong, and SU Weimin. Extended target tracking method based on multi-Bernoulli probability hypothesis density[J]. Journal of Electronics Information Technology, 2016, 38(12): 3114-3121. doi: 10.11999/JEIT 160372.

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. doi: 10.1109/TAES.2008.4655362.

FELDMANN M, FRANKEN D, and KOCH W. Tracking of extended objects and group targets using random matrices[J]. IEEE Transactions on Signal Processing, 2011, 59(4): 1409-1420. doi: 10.1109/TSP.2010.2101064.

BEARD M, REUTER S, GRANSTROM K, et al. Multiple extended target tracking with labeled random finite sets[J]. IEEE Transactions on Signal Processing, 2016, 64(7): 1638-1653. doi: 10.1109/TSP.2015.2505683.

LAN Jian and LI Xiaorong. Tracking of maneuvering non- ellipsoidal extended object or target group using random matrix[J]. IEEE Transactions on Signal Processing, 2014, 62(9): 2450-2463. doi: 10.1109/TSP.2014.2309561.

GRANSTROM K, WILLETT C, and BAR-SHALOM Y. An extended target tracking model with multiple random matrices and unified kinematics[C]. International Conference on Information Fusion, Washington, USA, 2015: 1007-1014.

GRANSTROM K and ORGUNER U. A PHD filter for tracking multiple extended targets using random matrices[J]. IEEE Transactions on Signal Processing, 2012, 60(11): 5657-5671. doi: 10.1109/TSP.2012.2212888.

LI Peng, GE Hongwei, YANG Jinlong, et al. Shape selection partitioning algorithm for Gaussian inverse Wishart probability hypothesis density filter for extended target tracking[J]. Radar, Sonar Navigation, 2016, 10(9): 1041-1051. doi: 10.1049/iet-spr.2015.0503.

VIVONE G, BRACA P, GRANSTROM K, et al. Multistatic Bayesian extended target[J]. IEEE Transactions on Aerospace and Electronic Systems, 2016, 52(6): 2626-2643. doi: 10.1109/TAES.2016.150724.

VIVONE G and BRACA P. Joint probabilistic data association tracker for extended target tracking applied to X-band Marine radar data[J]. IEEE Journal of Oceanic Engineering, 2016, 41(4): 1007-1019. doi: 10.1109/JOE.2015. 2503499.

GRANSTROM K, NATALE A, BRACA P, et al. PHD extended target tracking using an incoherent X-band radar: preliminary real-world experimental results[C]. International Conference on Information Fusion, Salamanca, Spain, 2014: 1-8.

GRANSTROM K, NATALE A, BRACA P, et al. Gamma Gaussian inverse Wishart probability hypothesis density for extended target tracking using X-band marine radar data[J]. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(12): 6617-6631. doi: 10.1109/TGRS.2015.2444794.

GALATI G, LEONARDI M, CAVALLIN A, et al. Airport surveillance processing chain for high resolution radar[J]. IEEE Transactions on Aerospace and Electronic Systems, 2010, 46(3): 1522-1533. doi: 10.1109/TAES.2010.5545207.

施引文献

资源附件(0)

访问统计