基于机场活动地图信息改进AIMM-UKF算法的移动目标跟踪

常鑫; 马光辉; 高建树; 郝世宇

doi:10.3963/j.jssn.1674-4861.2024.02.009

基于机场活动地图信息改进AIMM-UKF算法的移动目标跟踪

doi: 10.3963/j.jssn.1674-4861.2024.02.009

1.
中国民航大学交通科学与工程学院天津 300300
2.
中国民航大学电子信息与自动化学院天津 300300

基金项目:

国家重点研发计划项目 2021YFB2600500

详细信息

作者简介:
常鑫（1991—），博士，讲师. 研究方向：智能交通系统等. E-mail: xchang@cauc.edu.cn

通讯作者:
高建树(1966—)，博士，研究员. 研究方向：机场场面交通系统. E-mail: 2909488508@qq.com

中图分类号: V351.392
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- 文章访问数: 127
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- 被引次数: 0
出版历程
- 收稿日期: 2023-11-04
- 网络出版日期: 2024-09-14

Improved AIMM-UKF Mobile Target Tracking Algorithm Based on Airport Map Information

1.
School of Transportation Science and Engineering, Civil Aviation University of China, Tianjin 300300, China
2.
School of Electronic Information and Automation, Civil Aviation University of China, Tianjin 300300, China

摘要

摘要: 针对机场场面高密度交通以及多类型移动目标的特殊性，为保证机场自动化设备如无人驾驶技术在机场内的应用，需要进一步优化定位算法来提高移动目标的跟踪精度；通过分析现有的自适应交互式多模型- 无迹卡尔曼滤波跟踪算法（adaptive interactive multi-model-unscented Kalman filter algorithm，AIMM-UKF）在移动目标跟踪过程中模型匹配度和跟踪精度上的不足，研究了1种基于机场活动地图信息改进的自适应交互式多模型-无迹卡尔曼滤波跟踪算法。根据机场地图数据库（airport map database，AMDB）细化的机场操作规程文件，通过ArcGIS软件对某机场施工CAD图简化处理并利用二次多项式配准法对机场地图进行精确校正，完成高精度机场地图修正，将接收到的机场智能监控设备采集到的数据进行实时处理，结合高精度机场地图信息对发生位置偏移的移动目标的坐标信息进行修正，改变移动目标跟踪算法的观测值，在自适应修正马尔可夫转移概率矩阵的基础上，利用观测矩阵对其进行二次修正，提高移动目标跟踪精度和模型匹配度。经蒙特卡洛仿真实验表明：该改进算法利用高精度机场地图信息对移动目标的观测值进行修正，与自适应修正马尔可夫转移概率矩阵的交互式多模型-无迹卡尔曼滤波算法相比，位置的均方根误差（root mean square error，RMSE）平均降低了62.69%，速度的RMSE平均降低了56.84%。本文算法具有更高的模型匹配度和更佳的滤波效果，提高了场面移动目标的跟踪精度。
- 机场交通管控与运行 /
- 场面移动目标 /
- 机场地图数据库 /
- AIMM-UKF /
- 转移概率矩阵 /
- 观测矩阵
Abstract: Given the unique challenges posed by high-density traffic flow and diverse moving targets on airport surfaces, ensuring accurate tracking is essential for effective operation of airport automated equipment such as unmanned vehicles within airports. To address the limitations of the existing Adaptive Interactive Multi-Model-Unscented Kalman Filter algorithm (AIMM-UKF) in tracking moving targets in airport movement areas, an enhanced tracking algorithm is proposed by incorporating high precision airport map information into AIMM-UKF to improve tracking accuracy. Using the detailed airport operating procedures file from the airport map database (AMDB), the construction CAD drawing of an airport is simplified and accurately corrected with ArcGIS software and the second-order polynomial registration method to complete the high-precision airport map correction. The data collected by airport intelligent monitoring equipment is processed in real time, with the coordinate information of moving targets being corrected using the high-precision airport map information. This correction adjusts the observation values in the moving target tracking algorithm. Additionally, by incorporating adaptive correction of the Markov transition probability matrix and applying the observation matrix for secondary correction, tracking accuracy and model matching are improved. Monte Carlo simulation experiments have demonstrate that this improved algorithm utilizes high-precision airport map information to refine the observation values of moving targets. Compared with the Adaptive Correction Markov Transition Probability Matrix Interactive Multiple Model-Unscented Kalman Filter algorithm, this improved algorithm achieves an average reduction of 62.69% in the root mean square error (RMSE) of position and 56.84% in the RMSE of speed. In comparison, this algorithm exhibits superior model matching and superior filtering performance, significantly enhancing the tracking accuracy of moving targets within airport environments.
- airport traffic control and operation /
- surface moving target /
- AMDB /
- adaptive interacting multiple model-unscented Kalman (AIMM-UKF) /
- probability transition matrix /
- observation matrix

HTML全文

图 1 简化后的某机场场面地图

Figure 1. Simplified Airport scene

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图 2 机场地图配准流程图

Figure 2. Airport map registration flowchart

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图 3 移动目标观测值修正

Figure 3. Moving target observation correction

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图 4 整体算法流程图

Figure 4. Overall algorithm flowchart

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图 5 引导车路径规划图

Figure 5. Guided vehicle path plan

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图 6 跟踪轨迹效果对比

Figure 6. Comparison of tracking trajectory effects

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图 7 位置RMSE对比

Figure 7. Location RMSE comparison

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图 8 速度RSME对比

Figure 8. Speed RSME comparison

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图 9 AIMM-UKF模型概率

Figure 9. AIMM-UKF model probability

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图 10 改进后的AIMM-UKF模型概率

Figure 10. Improved AIMM-UKF model probability

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表 1 4种算法的位置与速度RMSE平均值对比

Table 1. Comparison of the positions of the four algorithms with the average speed RMSE

算法	位置RMSE平均值/m	速度RMSE平均值/（m/s）
传统IMM算法	27.862	1.797
文献[12]算法	15.285	0.972
AIMM-UKF	4.621	1.571
本文算法	1.724	0.678

下载: 导出CSV

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