基于AIGWO-IMMUKF的目标跟踪算法

游航航; 韩其松; 余敏建; 龙宏志; 杨海燕; 李朋永

doi:10.13700/j.bh.1001-5965.2019.0405

基于AIGWO-IMMUKF的目标跟踪算法

doi: 10.13700/j.bh.1001-5965.2019.0405

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

基金项目:

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

详细信息

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

通讯作者:
韩其松. E-mail:afeu_yh@126.com

中图分类号: TN953;V24
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出版历程
- 收稿日期: 2019-07-19
- 录用日期: 2020-04-10
- 网络出版日期: 2020-10-20

Target tracking algorithm based on AIGWO-IMMUKF

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

Funds:

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

More Information

Corresponding author: HAN Qisong. E-mail:afeu_yh@126.com

摘要

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。
- 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.
- target tracking /
- Unscented Kalman Filter(UKF) /
- Interacting Multiple Model(IMM) /
- Grey Wolf Optimizer(GWO) /
- filter parameter /
- transition probability

HTML全文

图 1 灰狼位置更新示意图

Figure 1. Schematic diagram of gray wolf location update

下载: 全尺寸图片幻灯片

图 2 AIGWO-IMMUKF算法流程示意图

Figure 2. Flowchart of AIGWO-IMMUKF algorithm

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图 3 跟踪轨迹三维空间示意图

Figure 3. Sketch map of tracking trajectory in 3D space

下载: 全尺寸图片幻灯片

图 4 X、Y方向位置RSME随时间变化曲线

Figure 4. Change of X, Y-direction position RSME with time

下载: 全尺寸图片幻灯片

图 5 X、Y方向速度RSME随时间变化曲线

Figure 5. Change of X, Y-direction velocity RSME with time

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图 6 算法时效性对比

Figure 6. Comparison of algorithm timeliness

下载: 全尺寸图片幻灯片

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