临近空间目标非弹道式机动模式跟踪滤波技术

doi:10.16182/j.issn1004731x.joss.201706030

摘要/Abstract

摘要： 由于临近空间高超声速飞行器采用非弹道式机动飞行方式,飞行速度、高度、加速度不断变化,目标机动具有长周期机动特点,临近空间目标运动轨迹往往呈现出“跳跃”特征,给地面防御系统对其定位和跟踪带来了巨大的困难,传统的机动模式跟踪滤波技术无法给出精确的目标状态估计,跟踪性能变差。通过对跟踪滤波方法进行概述,介绍临近空间目标四种典型的非弹道式机动模式,对不同机动模式跟踪滤波技术、机动模式未知情况下的跟踪滤波技术进行探讨,为进一步提高临近空间高超声速飞行器跟踪滤波精度,成功拦截临近空间高超声速飞行器提供保障。

关键词: 非弹道式, 临近空间, 机动模式, 跟踪滤波

Abstract: Because hypersonic vehicles in the near space often adopt the way of nonballistic maneuver flight with characteristics of high speed and lift-to-drag ratio, flying in atmosphere for a long time, whose motion trajectories are sometimes characterized by “hopping”, their flight altitudes, flight velocities and flight accelerations change. Targets maneuver are characterized by long period maneuver, which has brought great difficulties with ground defense system for maneuvering target localization and tracking. Traditional filtering algorithms obviously cannot give accurate target state estimation, tracking performance has declined. The overview of tracking filter ways was given, introducing four typical nonballistic maneuver models of near space targets, and discussing from two aspects including tracking filter ways of different maneuver models and tracking filter ways of unknown maneuver models. The guarantee was provided for improving the accuracy of tracking filter for hypersonic vehicles in the near space and successfully intercepting hypersonic vehicles in the near space.

Key words: nonballistic, near space, maneuver model, tracking filter

中图分类号:

TJ765.1

秦雷, 李君龙. 临近空间目标非弹道式机动模式跟踪滤波技术[J]. 系统仿真学报, 2017, 29(6): 1380-1385.

Qin Lei, Li Junlong. Maneuver Models Tracking Filter of Nonballistic Near Space Targets[J]. Journal of System Simulation, 2017, 29(6): 1380-1385.

参考文献

[1] 李为, 李一平, 封锡盛. 基于修正加权矩阵的三维解耦无偏量测转换交互式多模型算法[J]. 机器人, 2015, 37(2): 237-253.
(Li Wei, Li Yiping, Feng Xisheng.A Decoupled Interacting Multiple Model Algorithm with Unbiased 3D Converted Measurements Based on Modiﬁed Weighted Matrix[J]. Robot, 2015, 37(2): 237-253.)
[2] 张园, 郭晨, 刘淑波, 等. 基于S修正卡尔曼滤波的自适应网格模糊交互多模型算法研究[J]. 舰船科学技术, 2014, 36(3): 13-18.
(Zhang Yuan, Guo Chen, Liu Shu-bo, et al.Research on algorithm of adaptive grid and fuzzy interacting multiple model based on S-amended kalman filter[J]. Ship Science and Technology, 2014, 36(3): 13-18.)
[3] 刘瑞兰, 陈小惠. 基于模糊交互多模型的机动目标被动跟踪算法[J]. 电子测量与仪器学报, 2012, 26(10): 846-850.
(Liu Ruilan, Chen Xiaohui.Maneuvering target tracking algorithm based on fuzzy interacting multiple model[J]. Journal of Electronic Measurement and Instrument, 2012, 26(10): 846-850.)
[4] 张宇. 一种图像确认目标的多目标跟踪方法[J]. 电子测量与仪器学报, 2014, 28(6): 617-624.
(Zhang Yu.Image confirmed multi-target t racking algorithm[J]. Journal of Electronic Measurement and Instrumentation, 2014, 28(6): 617-624.)
[5] 梁建波, 王芳, 方喜龙, 等. 拦截弹状态变结构交互多模滤波估计方法[J]. 电光与控制, 2011, 18(7): 68-85.
(Liang Jianbo, Wang Fang, Fang Xilong, et al.A VSIMM Algorithm for State Estimation of Interceptor[J]. Electronics Optics ＆ Control, 2011, 18(7): 68-85.)
[6] 张园, 董受全, 钟志通, 等. 防发散无迹卡尔曼滤波自适应网格交互式多模型算法[J]. 火力与指挥控制, 2015, 40(2): 40-44.
(Zhang Yuan, Dong Shou-quan, Liu Shu-bo, et al.Adaptive Grid Interacting Multiple Model Algorithm Based on Anti-divergent UKF[J]. Fire Control & Command Control, 2015, 40(2): 40-44.)
[7] 毛征, 孟凡刚, 孟灿, 等. 基于高机动目标跟踪的改进变结构IMM算法[J]. 北京工业大学学报, 2015, 41(3): 353-360.
(Mao Zheng, Meng Fan-gang, Meng Can, et al.Improved Variable-structure IMM Algorithm Research Based on High Maneuvering Targets Tracking[J]. Journal of Beijing University of Technology, 2015, 41(3): 353-360.)
[8] 陈海, 单甘霖, 吉兵, 等. 图像增强的自适应网格交互式多模型算法[J]. 电光与控制, 2012, 19(8): 28-33.
(Chen Hai, Shan Ganlin, Ji Bing, et al.Image Enhanced Adaptive Grid Interacting Multiple Model Algorithm[J]. Electronics Optics ＆ Control, 2012, 19(8): 28-33.)
[9] 王硕, 刘丽. 基于机动转弯目标的自适应交互式多模型算法[J]. 计算机仿真, 2013, 30(4): 169-193.
(Wang Shuo, Liu Li.Adaptive Interacting Multiple Model Algorithm Based on Turn Maneuver Targets[J]. Computer Simulation, 2013, 30(4): 169-193.)
[10] Semerdjiev E, Mihaylova L, Li X-R.Variable- and fixed-structure augmented IMM algorithms using coordinated turn model[C]// Proceedings of International Conference on Information Fusion (Fusion 2000). Paris, France: IEEE, 2000: 25-32.
[11] H A P Blom, Y Bar-Shalom. The interacting multiple model algorithm for systems with Markovian switching coefficients [J]. IEEE Trans. Automatic Control (S0018-9286), 1988, AC-33(8): 780-783.
[12] R E Helmick, W D Blair, S A Hoffman. Fixed-interval smoothing for Markovian switching systems [J]. IEEE Trans. Information Theory (S0018-9448), 1995, IT-41(11): 1845-1855.
[13] X Zhang, W Hu, Z Zhao, et al.SVD Based Kalman Particle Filter for Robust Visual Tracking[C]// Proc. IEEE Conference on Pattern Recognition. USA: IEEE, 2008: 1-4.
[14] J Li, H Yu, L Zhou, et al.An Adaptive Unscented Particle Filter tracking Algorithm Based on Color Distribution and Wavelength Moment[C]// Proc. IEEE Conference on Industrial Electronics and Applications. USA: IEEE, 2007: 2218-2223.
[15] C Kim, R Sakthivel, W K Chung.Unscented FastSlam: A Robust Algorithm for the Simultaneous Localization and Mapping Problem[C]// Proc. IEEE International Conference on Robotics and Automation. USA: IEEE, 2007: 2439-2445.
[16] Gordon N J, Salmond D J, Smith A F M. Novel approach to nonlinear/non-Gaussian Bayesian state estimation[C]// IEE Proceedings F - Radar and Signal Processing. USA: IEEE, 1993: 107-113.
[17] Yang N, Tian W, Jin Z.An interacting multiple model particle filter for manoeuvring target location[J]. Measurement Science and Technology (S2157-1473), 2006, 17(6): 1307-1311.
[18] 党建武, 黄建国. 机动目标自适应高斯模型与跟踪算法[J]. 电讯技术, 2003(2): 109-113.
(Dang Jian-wu, Huang Jian-guo.An Adaptive Gauss Model and Tracking Algorithm for Maneuvering Target[J]. Telecommunication Engineering, 2003(2): 109-113.)
[19] D T Magill.Optimal Adaptive Estimation of Sampled Stochastic Processes[J]. IEEE Transactions on Automatic Control (S0018-9286), 1965, 10(4): 434-439.
[20] Simon Haykim.Kalman Filtering and Neural Networks[M]. USA: John Wiley & Sons, 2001.
[21] Doucet A, de Freitas N, Gordon N. Sequential Monte Carlo Methods in practice[M]. New York, USA: Springer-Verlay, 2001.
[22] Young P.Self-adaptive Kalman filter[J]. Electronics Letters (S0013-5194), 1979, 15(12): 358-360.
[23] Wang D, Zhou D H, Jin Y H.A strong tracking predictor for nonlinear processes with input time delay[J]. Computers and Chemical Engineering (S0098-1354), 2004, 18(12): 2523-2540.
[24] R L Moose.An adaptive state estimation solution to the maneuvering target problem[J]. IEEE Transactions on Automatic Control (S0018-9286), 1975, 20(6): 359-362.