模糊Kalman滤波在无人机姿态测量中的应用研究  

Study on the attitude measurement of UAV by using fuzzy Kalman filter

作　　者：朱建新[1] 张秋阳[1] 王石林[1] 

机构地区：[1]中南大学机电工程学院,湖南长沙410083

出　　处：《广西大学学报（自然科学版）》2011年第2期228-233,共6页Journal of Guangxi University（Natural Science Edition）

基　　金：湖南省科技计划重点项目(2007FJ1006);湖南省高校科技成果产业化培育项目(09CY002)

摘　　要：针对目前无人机姿态测量所用捷联航姿系统,因陀螺漂移和算法误差的引入而导致结果发散的状况,采用组合航姿原理与信息融合理论,结合各传感器输出与载体运动状态相关的特性,提出了一种基于模糊Kalm an滤波技术的惯性测量组件与磁强计组合信息融合方法。通过多尺度的模糊推理规则判断出载体当前的运动状态,实时调整系统测量噪声方差,对其解算的姿态信息进行自适应校正,解决了低成本姿态测量系统存在的滤波发散与运动加速度耦合问题。经过试验且与高精度航姿系统测量结果进行比较,其最大误差由原来的±4°变成±1°,验证了该推理过程和融合算法的正确性。For the consideration of poor stability caused by the gyroscope drift or algorithm,of present strapdown attitude measurement used for the unmanned aerial vehicle（UAV）,the combination principle and information fusion theory was proposed by applying a fuzzy Kalman filter technique fusing data of both inertial measurement unit and magnetic sensor considering the characteristics that the sensors outputs was associated with the state of vehicle motion.The motion states could be distinguished by fuzzy reasoning,and adjusting the system measurements noise variance real-time.By fusing the attitudes calculated respectively by gyros and accelerators under fuzzy Kalman filter.This algorithm powerfully solves the problem of acceleration of the coupling of the low cost attitude measurement system.And the definition change from±4° to ±1°.

关 键 词：模糊推理 KALMAN滤波 姿态测量 信息融合 

分 类 号：V249.1[航空宇航科学与技术—飞行器设计] V279