摘要
针对传统机场场面监视手段的固有缺陷,为避免在机场发生航空器/车辆入侵跑道,将事件驱动型传感器网络引入到场面目标监视中,研究了1种基于灰色序列输入扩张状态观测器的场面目标跟踪方法。该方法采用灰色模型对速度序列进行建模,弱化了原始速度序列随机干扰的影响,并结合目标运动模型辨识理论设计了1类扩张状态观测器观测系统状态,实现了场面运动目标跟踪与模型参数辨识,应用仿真算例对模型和方法进行了验证,x轴速度误差峰值不超过2.6m/s,该方法具有所需数据少、预测精度高和无需先验信息的特点。
In view of the inherent defects in traditional airport surface surveillance to prevent aircrafts and vehicles from incurring runway, the event-driven sensor network is drawn into runway incursion preventing system to reconstruct the runway operating state. This paper proposed a method of target tracking on airport surface by applying an extended state observer （ESO） with grey-sequence input to estimating the target states. Using gray theory to build the speed se- quence model has weakened the random disturbance of original sequence. System states can be observed by ESO based on identification theory of moving object model. This method needs less data and has higher precision without prior informa tion while tracking moving target and identifying model parameter. Experimental results also prove that the model and the algorithm are feasible and effective with peak velocity error in x-axis less than 2.6 m/s.
出处
《交通信息与安全》
2014年第6期48-52,64,共6页
Journal of Transport Information and Safety
基金
国家自然科学基金委员会与中国民用航空总局联合资助项目(批准号:U1433125)
国家科技部科技支撑计划项目(批准号:2011BAH24B06)
中央高校基本科研业务费专项资金项目(批准号:NS2014065)
江苏省自然科学基金项目(批准号:BK20141413)
民航科技创新引导资金项目(批准号:14014J0340035)资助
关键词
交通工程
先进场面引导与控制系统
目标跟踪
GM(1
1)
扩张状态观测器
traffic engineering
advanced surface movement guidance and control system
target tracking
gray model（1,1）
extended state observer
