基于事件的卡尔曼滤波算法及其在移动小车室外导航中的应用研究

THE EVENT-TRIGGERED KALMAN FILTERING ALGORITHM AND ITS APPLICATION ON OUTDOOR NAVIGATION OF MOBILE CARS

为解决GPS信号中断所导致的定位失准、导航精度降低的问题,减少完成室外导航任务所需时间,提出一种基于事件触发机制的卡尔曼滤波算法(KF)。将小车的状态变化作为KF的触发事件,当状态偏差超过一定阈值时,KF估计器响应并给出新的状态估计;设计能提供模拟GPS信号的估计器,在GPS信号中断时为KF估计器提供连续可用的位置信息使滤波过程不受影响。实验结果表明,相比于传统卡尔曼滤波,基于事件的卡尔曼滤波算法不仅能够应对GPS信号中断的情况,保证室外导航的精度,同时能够降低算法的时间复杂度。

To solve the problems of inaccurate positioning and poor navigation accuracy caused by GPS signal interruption,and reduce the time required to complete outdoor navigation tasks,an event-triggered Kalman filtering(KF)algorithm is proposed.The state change of the car was used as the trigger event of KF,and the KF estimator responded and gave a new state estimation only when the car's state deviation exceeded a certain threshold.A signal estimator which could provide simulated GPS signals was designed to provide the KF estimator with continuously available position information,to ensure the filtering process would not be unaffected.Experimental results show that compared with traditional KF,the event-triggered KF can deal with the GPS signals interruption to ensure the accuracy of outdoor navigation and reduce the time complexity of the algorithm.