基于机场活动地图信息改进AIMM-UKF算法的移动目标跟踪  

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

作　　者：常鑫 马光辉 高建树[1] 郝世宇 CHANG Xin;MA Guanghui;GAO Jianshu;HAO Shiyu(School of Transportation Science and Engineering,Civil Aviation University of China,Tianjin 300300,China;School of Electronic Information and Automation,Civil Aviation University of China,Tianjin 300300,China)

机构地区：[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

出　　处：《交通信息与安全》2024年第2期87-94,104,共9页Journal of Transport Information and Safety

基　　金：国家重点研发计划项目(2021YFB2600500)资助。

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。Given the unique challenges posed by high-density traffic flow and diverse moving targets on airport sur-faces,ensuring accurate tracking is essential for effective operation of airport automated equipment such as un-manned vehicles within airports.To address the limitations of the existing Adaptive Interactive Multi-Model-Un-scented 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 im-prove tracking accuracy.Using the detailed airport operating procedures file from the airport map database(AM-DB),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 observa-tion values in the moving target tracking algorithm.Additionally,by incorporating adaptive correction of the Mar-kov 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 algo-rithm 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.

关 键 词：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 

分 类 号：V351.392[航空宇航科学与技术—人机与环境工程]