基于EKF残差的无人机GPS故障诊断方法  

A Fault Diagnosis Method for UAV GPS Based on Extended Kalman Filter Residual

作　　者：杨苏桥 郑恩辉 田琛 李易平 YANG Suqiao;ZHENG Enhui;TIAN Chen;LI Yiping(School of Mechanical and Electrical Engineering,China Jiliang University,Hangzhou 310000,China)

机构地区：[1]中国计量大学机电工程学院,杭州310000

出　　处：《电光与控制》2024年第7期61-65,72,共6页Electronics Optics & Control

摘　　要：针对无人机多传感器融合导航过程传感器故障的问题,提出了一种由惯性导航传感器诊断GPS传感器故障的方法,实现自身传感器之间的故障互诊。在无人机导航对传感器数据进行位姿解算时,针对扩展卡尔曼滤波(EKF)在预测与更新过程中产生的状态残差信息,即惯导与GPS两者解算的位置信息,设计一种改进序贯概率比检验(SPRT)的传感器故障诊断方法,改善了SPRT算法对残差信息突变的灵敏性以及处于多故障情形下的诊断可持续性。数据仿真实验表明:相较于传统方法和其他改进算法,该方法可以快速、准确地检测出故障发生和消失的时间,并可持续诊断故障,极大地提高了无人机飞行安全。Aiming at the sensor fault problem of Unmanned Aerial Vehicle(UAV)during multi-sensor fusion navigation a method for diagnosing GPS sensor faults using inertial navigation sensors is proposed to achieve mutual fault diagnosis between sensors.When performing pose calculation on sensor data during UAV navigation considering the status residual information generated by Extended Kalman Filter(EKF)in the prediction and update process of sensor data in navigation namely the position information calculated by both inertial navigation and GPS a sensor fault diagnosis method based on improved Sequential Probability Ratio Test(SPRT)is designed.It improves the sensitivity of the SPRT algorithm to abrupt changes in residual information and the diagnostic sustainability in multiple fault situations.Data simulation experiments show that compared with traditional methods and other improved algorithms this method can detect the time when faults occur and disappear quickly and accurately and continuously diagnose faults which improves the flight safety of UAVs greatly.

关 键 词：无人机 传感器故障诊断 序贯概率比检验 扩展卡尔曼滤波 

分 类 号：V241[航空宇航科学与技术—飞行器设计] TP277[自动化与计算机技术—检测技术与自动化装置]