基于AIGWO-IMMUKF的目标跟踪算法

doi:10.13700/j.bh.1001-5965.2019.0405

北京航空航天大学学报 ›› 2020, Vol. 46 ›› Issue (10): 1826-1833.doi: 10.13700/j.bh.1001-5965.2019.0405

基于AIGWO-IMMUKF的目标跟踪算法

游航航^1,2, 韩其松¹, 余敏建¹, 龙宏志³, 杨海燕¹, 李朋永²

1. 空军工程大学空管领航学院, 西安 710051;
2. 中国人民解放军 94498部队, 南阳 473000;
3. 中国人民解放军 95380部队, 湛江 524000

收稿日期:2019-07-19 发布日期:2020-10-29
通讯作者: 韩其松 E-mail:afeu_yh@126.com
作者简介:游航航男,硕士,助理工程师。主要研究方向:航空兵指挥;韩其松男,硕士,讲师。主要研究方向:航空兵指挥自动化;余敏建男,硕士,教授。主要研究方向:航空兵指挥自动化;杨海燕女,博士,副教授。主要研究方向:空天态势与威胁评估;李朋永男,本科,工程师。主要研究方向:空域管理。
基金资助:
国家自然科学基金（61472441）；装备预研领域基金（61403110304）；空军工程大学校长基金（XZJY2018031）

Target tracking algorithm based on AIGWO-IMMUKF

YOU Hanghang^1,2, HAN Qisong¹, YU Minjian¹, LONG Hongzhi³, YANG Haiyan¹, LI Pengyong²

1. Air Traffic Control and Navigation College, Air Force Engineering University, Xi'an 710051, China;
2. Unit 94498 of the PLA, Nanyang 473000, China;
3. Unit 95380 of the PLA, Zhanjiang 524000, China

Received:2019-07-19 Published:2020-10-29
Supported by:
National Natural Science Foundation of China (61472441); Fund for Equipment Pre-research Field of China(61403110304); President's Fund of Air Force Engineering University (XZJY2018031)

摘要/Abstract

摘要： 针对目标跟踪算法中滤波器选择和模型设计问题，提出了一种具有自适应性的交互式多模型无迹卡尔曼滤波（IMMUKF）目标跟踪算法。首先，介绍了IMMUKF的算法步骤；其次，提出运用改进的灰狼优化（IGWO）算法优化其中的滤波参数，通过构造调节因子建立了时变的Markov状态转移概率，形成了AIGWO-IMMUKF算法，并给出其算法流程；最后，将所提AIGWO-IMMUKF算法与传统算法在相同条件下进行仿真，得出位置、速度均方根误差曲线，以及时效性对比。结果表明，所提AIGWO-IMMUKF算法克服了传统IMMUKF算法的不足，提升了算法性能，精度和时效性都更优。

关键词: 目标跟踪, 无迹卡尔曼滤波(UKF), 交互式多模型(IMM), 灰狼优化(GWO), 滤波参数, 转移概率

Abstract: Aimed at the problem of filter selection and model design in target tracking algorithm, an adaptive Interacting Multiple Model-Unscented Kalman Filter (IMMUKF) target tracking algorithm is proposed. First, the algorithm steps of IMMUKF are introduced. Second, the Improved Grey Wolf Optimizer (IGWO) is proposed to optimize the filter parameters, and the time-varying Markov state transition probability is established by constructing the adjustment factor. Then, the AIGWO-IMMUKF algorithm is formed and its algorithm flowchart is given. Finally, the AIGWO-IMMUKF algorithm proposed in this paper and the traditional method are simulated under the same conditions, and the root mean square error curves of position and velocity as well as the timeliness comparison chart are obtained. The results show that AIGWO-IMMUKF algorithm overcomes the shortcomings of traditional IMMUKF, improves the performance of the algorithm, and has better accuracy and timeliness.

Key words: target tracking, Unscented Kalman Filter(UKF), Interacting Multiple Model(IMM), Grey Wolf Optimizer(GWO), filter parameter, transition probability

中图分类号:

TN953
V24

游航航, 韩其松, 余敏建, 龙宏志, 杨海燕, 李朋永. 基于AIGWO-IMMUKF的目标跟踪算法[J]. 北京航空航天大学学报, 2020, 46(10): 1826-1833.

YOU Hanghang, HAN Qisong, YU Minjian, LONG Hongzhi, YANG Haiyan, LI Pengyong. Target tracking algorithm based on AIGWO-IMMUKF[J]. Journal of Beijing University of Aeronautics and Astronautics, 2020, 46(10): 1826-1833.

参考文献

[1] 刘楠.基于机动目标跟踪模型的自适应滤波算法[D].杭州:浙江理工大学,2016.LIU N.Adaptive filter algorithm based on maneuvering target tracking model[D].Hangzhou:Zhejiang Sci-Tech University,2016(in Chinese).
[2] 江宝安,万群.基于UKF-IMM的双红外机动目标跟踪算法[J].系统工程与电子技术,2008,30(8):1454-1459.JIANG B A,WAN Q.Maneuvering target passive tracking with dual infrared observers using IMM algorithm based on UKF[J].Systems Engineering and Electronics,2008,30(8):1454-1459(in Chinese).
[3] 刘昌云.雷达机动目标运动模型与跟踪算法研究[D].西安:西安电子科技大学,2014.LIU C Y.Study on motion model and tracking algorithms of radar maneuvering target[D].Xi'an:Xidian University,2014(in Chinese).
[4] 万琴,王耀南.基于卡尔曼滤波器的运动目标检测与跟踪[J].湖南大学学报(自然科学版),2007,34(3):36-40.WAN Q,WANG Y N.Moving objects detecting and tracking based on Kalman filter[J].Journal of Hunan University(Natural Sciences),2007,34(3):36-40(in Chinese).
[5] 张勇刚,黄玉龙,武哲民,等.一种高阶无迹卡尔曼滤波方法[J].自动化学报,2014,40(5):838-848.ZHANG Y G,HUANG Y L,WU Z M,et al.A high order unscented Kalman filtering method[J].Acta Automatica Sinica,2014,40(5):838-848(in Chinese).
[6] 刘向阳.几种典型非线性滤波算法及性能分析[J].舰船电子工程,2019,39(7):32-36.LIU X Y.Several typical nonlinear filtering algorithms and performance analysis[J].Ship Electronic Engineering,2019,39(7):32-36(in Chinese).
[7] 刘铮.UKF算法及其改进算法的研究[D].长沙:中南大学,2009.LIU Z.Research on UKF algorithm and its improved algorithm[D].Changsha:Central South University,2009(in Chinese).
[8] 张园.空中单机动目标跟踪算法的研究[D].大连:大连海事大学,2014.ZHANG Y.The research on tracking algorithms for single aerial maneuvering target[D].Dalian:Dalian Maritime University,2014(in Chinese).
[9] MALLESWARAN M,VAIDEHI V,IRWIN S,et al.IMM-UKF-TFS model-based approach for intelligent navigation[J].The Journal of Navigation,2013,66(6):19.
[10] XU T L.Federated IMM-UKF algorithm for multi-sensor data fusion[J].Advanced Materials Research,2013,753-755:2117-2120.
[11] ZHANG Y,CHEN H S,LUO Y.A novel infrared landmark indoor positioning method based on improved IMM-UKF[J].Applied Mechanics and Materials,2014,511-512:880-885.
[12] 徐晓苏,闫琳宇,吴晓飞,等.基于粒子群优化的UKF在SINS/GPS组合导航中的应用[J].中国惯性技术学报,2018,26(2):196-201.XU X S,YAN L Y,WU X F,et al.Unscented Kalman filter based on particle swarm optimization algorithm in SINS/GPS integrated navigation system[J].Journal of Chinese Inertial Technology,2018,26(2):196-201(in Chinese).
[13] 郭继峰,李忠志,张国强,等.基于深度置信网络的卡尔曼滤波算法改进[J].计算机应用与软件,2019,36(6):248-253. GUO J F,LI Z Z,ZHANG G Q,et al.Improvement of Kalman filter algorithm based on deep belief network[J].Computer Applications and Software,2019,36(6):248-253(in Chinese).
[14] 左东广,韩崇昭,郑林,等.基于时变马尔科夫转移概率的机动目标多模型跟踪[J].西安交通大学学报,2003,37(8):824-828.ZUO D G,HAN C Z,ZHENG L,et al.Maneuvering target tracking based on time-varying Markov transition probabilities[J].Journal of Xi'an Jiaotong University,2003,37(8):824-828(in Chinese).
[15] 郭志,董春云,蔡远利,等.时变转移概率IMM-SRCKF机动目标跟踪算法[J].系统工程与电子技术,2015,37(1):24-30. GUO Z,DONG C Y,CAI Y L,et al.Time-varying transition probability based IMM-SRCKF algorithm for maneuvering target tracking[J].Systems Engineering and Electronics,2015,37(1):24-30(in Chinese).
[16] 臧荣春,崔平远.马尔可夫参数自适应IFIMM算法研究[J].电子学报,2006,34(3):521-524.ZANG R C,CUI P Y.Research on adaptive Markov parameter IFIMM algorithm[J].Acta Electronica Sinica,2006,34(3):521-524(in Chinese).
[17] 潘泉,杨峰,叶亮,等.一类非线性滤波器——UKF综述[J].控制与决策,2005,20(5):481-489.PAN Q,YANG F,YE L,et al.Survey of a kind of nonlinear filters-UKF[J].Control and Decision,2005,20(5):481-489(in Chinese).
[18] MIRJALILI S,MIRJALILI S M,LEWIS A.Grey wolf optimizer[J].Advances in Engineering Software,2014,69:46-61.
[19] 姚鹏,王宏伦.基于改进流体扰动算法与灰狼优化的无人机三维航路规划[J].控制与决策,2016,31(4):701-708.YAO P,WANG H L.Three-dimensional path planning for UAV based on improved interfered fluid dynamical system and grey wolf optimizer[J].Control and Decision,2016,31(4):701-708(in Chinese).
[20] BIAN X Q,ZHANG L,DU Z M,et al.Prediction of sulfur solubility in supercritical sour gases using grey wolf optimizer-based support vector machine[J].Journal of Molecular Liquids,2018,261:431-438.
[21] LIU H,WU H,LI Y.Smart wind speed forecasting using EWT decomposition,GWO evolutionary optimization,RELM learning and IEWT reconstruction[J].Energy Conversion and Management,2018,161:266-283.
[22] 魏政磊,赵辉,黄汉桥,等.基于SAGWO算法的UCAVs动态协同任务分配[J].北京航空航天大学学报,2018,44(8):1651-1664.WEI Z L,ZHAO H,HUANG H Q,et al.Dynamic UCAVs cooperative task allocation based on SAGWO algorithm[J].Journal of Beijing University of Aeronautics and Astronautics,2018,44(8):1651-1664(in Chinese).
[23] 张凤娇,魏民祥,赵万忠.基于蚁群优化UKF算法的汽车状态估计[J].中国机械工程,2015,26(22):3046-3050.ZHANG F J,WEI M X,ZHAO W Z.Vehicle state estimation based on ant colony optimization algorithm[J].China Mechanical Engineering,2015,26(22):3046-3050(in Chinese).
[24] LI W,JIA Y.An information theoretic approach to interacting multiple model estimation[J]. IEEE Transactions on Aerospace and Electronic Systems,2015,51(3):1811-1825.
[25] 封普文,黄长强,曹林平,等.基于自适应马尔可夫参数交互多模型算法的弹道导弹跟踪研究[J].兵工学报,2014,35(12):2041-2049.FENG P W,HUANG C Q,CAO L P,et al.Research on ballistic missile tracking based on adaptive Markov parameter IMM[J].Acta Armamentarii,2014,35(12):2041-2049(in Chinese).
[26] 刘娟丽.基于交互多模型的被动多传感器机动目标跟踪算法研究[D].西安:西安电子科技大学,2010.LIU J L.Research on maneuvering target tracking based on interacting multiple model for multiple passive sensors[D].Xi'an:Xidian University,2010(in Chinese).
[27] 张振兴,杨任农,张彬超,等.空战飞行对敌目标逼近航迹跟踪仿真[J].空军工程大学学报(自然科学版),2018,19(2):33-37.ZHANG Z X,YANG R N,ZHANG B C,et al.Air combat trajectory predict simulation for enemy target approach[J].Journal of Air Force Engineering University(Natural Science Edition),2018,19(2):33-37(in Chinese).

基于AIGWO-IMMUKF的目标跟踪算法

Target tracking algorithm based on AIGWO-IMMUKF

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

[1]	周千里, 张文靖, 赵路平, 田乃倩, 王蓉. 面向个体人员特征的跨模态目标跟踪算法[J]. 北京航空航天大学学报, 2020, 46(9): 1635-1642.
[2]	王进花, 朱恩昌, 曹洁, 余萍. 基于修正IMM的风机变桨系统故障诊断方法[J]. 北京航空航天大学学报, 2020, 46(8): 1460-1468.
[3]	刘阳, 李怀建, 杜小菁. 一种基于自适应滤波的GPS滚转角估计方法[J]. 北京航空航天大学学报, 2020, 46(6): 1177-1183.
[4]	姚辉, 席建祥, 王成, 胡来红. 二阶多智能体系统自抗扰编队跟踪与避撞控制[J]. 北京航空航天大学学报, 2020, 46(5): 960-977.
[5]	刘芳, 孙亚楠, 王洪娟, 韩笑. 基于残差学习的自适应无人机目标跟踪算法[J]. 北京航空航天大学学报, 2020, 46(10): 1874-1882.
[6]	庞策, 单甘霖, 段修生. 多传感器协同识别跟踪多目标管理方法[J]. 北京航空航天大学学报, 2019, 45(8): 1674-1680.
[7]	王琪, 汪立新, 周小刚, 沈强. 混合式惯导系统姿态四元数连续自标定模型[J]. 北京航空航天大学学报, 2019, 45(7): 1424-1434.
[8]	杜明洋, 毕大平, 潘继飞, 王渊博. 自适应关联波门机动群目标跟踪算法[J]. 北京航空航天大学学报, 2019, 45(7): 1435-1443.
[9]	叶文, 蔡晨光, 杨平, 李建利. 融合高斯过程回归的UKF估计方法[J]. 北京航空航天大学学报, 2019, 45(6): 1081-1087.
[10]	李惠峰, 易文峰, 程晓明. 基于近似动态规划的目标追踪控制算法[J]. 北京航空航天大学学报, 2019, 45(3): 597-605.
[11]	肖楚晗, 李炯, 雷虎民, 王华吉. 基于AVSIMM算法的高超声速再入滑翔目标跟踪[J]. 北京航空航天大学学报, 2019, 45(2): 413-421.
[12]	欧能杰, 汪圣利, 张直. 基于非等维状态的IMM混合估计方法[J]. 北京航空航天大学学报, 2019, 45(10): 2115-2122.
[13]	王国宏, 白杰, 孙殿星. 速度欺骗干扰下的机动目标跟踪技术[J]. 北京航空航天大学学报, 2018, 44(7): 1359-1370.
[14]	刘畅, 杨锁昌, 汪连栋, 张宽桥. 基于自适应强跟踪CQKF的目标跟踪算法[J]. 北京航空航天大学学报, 2018, 44(5): 982-990.
[15]	周卫东, 刘璐, 唐佳. 基于模糊逻辑的交互式多模型滤波算法[J]. 北京航空航天大学学报, 2018, 44(3): 413-419.