基于SCKF的4旋翼无人机的姿态估计
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摘要
获得准确的姿态角对于无人机的控制来说是十分重要的.考虑到平方根容积卡尔曼滤波算法(square-root cubature Kalman filter,SCKF),既能够克服扩展卡尔曼滤波(EKF)方法因线性化带来的误差,具有更好的非线性滤波功能,又在传统容积卡尔曼滤波方法中加入了平方根技术,从而能够有效提高数值计算的稳定性,并降低了算法的复杂度.该文将SCKF算法应用于4旋翼无人机的姿态估计中,提出了一种新的4旋翼无人机的姿态估计方法,并进行了仿真实验.实验结果表明:该方法相比传统的EKF方法滤波精度更高,相比较传统的容积卡尔曼滤波(CKF)、无迹卡尔曼滤波(UKF)方法计算时间更短.
It is very important to get the accurate attitude angle for the control of unmanned aerial vehicle(UAV).Considering the square-root cubature Kalman filter(SCKF),which can overcome the errors caused by the extended Kalman filter(EKF) due to linearization,and has better nonlinear filtering function.Also,the square root technique is added in the traditional cubature Kalman filtering method,which can effectively improve the stability of numerical calculation and reduce the complexity of the algorithm.So that in this paper,SCKF is applied to attitude estimation of quadrotor UAV,a new attitude estimation method for quadrotor UAV is proposed and simulated.The experimental results show that this method has higher filtering precision than the traditional EKF method.Compared with traditional cubature Kalman filtering(CKF) and unscented Kalman filtering(UKF),the computation time of this method is shorter.
It is very important to get the accurate attitude angle for the control of unmanned aerial vehicle(UAV).Considering the square-root cubature Kalman filter(SCKF),which can overcome the errors caused by the extended Kalman filter(EKF) due to linearization,and has better nonlinear filtering function.Also,the square root technique is added in the traditional cubature Kalman filtering method,which can effectively improve the stability of numerical calculation and reduce the complexity of the algorithm.So that in this paper,SCKF is applied to attitude estimation of quadrotor UAV,a new attitude estimation method for quadrotor UAV is proposed and simulated.The experimental results show that this method has higher filtering precision than the traditional EKF method.Compared with traditional cubature Kalman filtering(CKF) and unscented Kalman filtering(UKF),the computation time of this method is shorter.
引文
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[14] 魏喜庆,宋申民.基于容积卡尔曼滤波的卫星姿态估计 [J].宇航学报,2013,34(2):193-200.
[15] 李洪阳,何潇.基于SCKF方法的非线性随机动态系统故障诊断方法 [J].山东大学学报:工学版,2017,47(5):130-135.
[16] 刘华,缪晨,吴文.平方根嵌入式容积卡尔曼粒子滤波算法 [J].南京理工大学学报:自然科学版,2015,34(4):471-476.
[17] 安军,杨振瑞,周毅博,等.基于平方根容积卡尔曼滤波的发电机动态状态估计 [J].电工技术学报,2017,32(12):234-240.
[18] 熊志刚,黄树彩,赵炜,等.均方根嵌入式容积粒子PHD多目标跟踪方法 [J].自动化学报,2017,43(2):238-247.
[19] 袁晓波,张超,詹银虎.强跟踪自适应SRCKF的卫星姿态确定算法 [J].测绘科学,2018(3):6-11.
[20] 左宗玉.四旋翼无人飞行器自适应轨迹跟踪控制 [C].Proceedings of the 30th Chinese Control Conference,Yantai,2011:2435-2439.
[21] 王仲伦.基于反步法的四旋翼无人机容错控制 [D].南昌:华东交通大学,2017.
[2] 彭孝东,张铁民,李继宇,等.基于传感器校正与融合的农用小型无人机姿态估计算法 [J].自动化学报,2015,41(4):854-860.
[3] Valenti R G,Dryanovski I,Xiao J.Keeping a good attitude:a quaternion-based orientation filter for IMUs and MARGs [J].Sensors,2015,15(8):19302-19330.
[4] 金舒灿,胡越黎,张贺.基于EKF的四旋翼姿态解算仿真与设计 [J].电子技术应用,2017,43(9):127-131.
[5] Li Jian,Wei Xinguo,Zhang Guangjun.An extended Kalman filter-based attitude tracking algorithm for star sensors [J].Sensors,2017,17(8):1921.
[6] Valenti R G,Dryanovski I,Xiao Jizhong.A linear Kalman filter for MARG orientation estimation using the algebraic Quaternion algorithm [J].IEEE Transactions on Instrumentation and Measurement,2016,65(2):467-481.
[7] Goodarzi F A,Lee T.Global formulation of an extended Kalman filter on SE(3) for geometric control of a quadrotor UAV [J].Journal of Intelligent and Robotic Systems,2017,88(2/3/4):1-19.
[8] Julier S J,Uhlmann J K.Unscented filtering and nonlinear estimation [J].Proceedings of the IEEE,2004,92(3):401-422.
[9] Yuan Xuebin,Yu Shuai,Zhang Shengzhi,et al.Quaternion-based unscented Kalman filter for accurate indoor heading estimation using wearable multi-sensor system [J].Sensors,2015,15(5):10872-10890.
[10] Garcia R V,Kuga H K,Silva W R,et al.Unscented Kalman filter and smoothing applied to attitude estimation of artificial satellites [J].Computational and Applied Mathematics,2018,37(4):1-10.
[11] 鱼少少,裴军,胡超.非线性滤波在SINS中的应用 [J].天文研究与技术,2018,15(1):104-110.
[12] Arasaratnam I,Haykin S.Cubature Kalman filters [J].IEEE Transactions on Automatic Control,2009,54(6):1254-1269.
[13] 石章松,吴中红,刘健,等.微小型飞行器多传感器融合容积姿态估计 [J].现代防御技术,2017,45(3):34-40.
[14] 魏喜庆,宋申民.基于容积卡尔曼滤波的卫星姿态估计 [J].宇航学报,2013,34(2):193-200.
[15] 李洪阳,何潇.基于SCKF方法的非线性随机动态系统故障诊断方法 [J].山东大学学报:工学版,2017,47(5):130-135.
[16] 刘华,缪晨,吴文.平方根嵌入式容积卡尔曼粒子滤波算法 [J].南京理工大学学报:自然科学版,2015,34(4):471-476.
[17] 安军,杨振瑞,周毅博,等.基于平方根容积卡尔曼滤波的发电机动态状态估计 [J].电工技术学报,2017,32(12):234-240.
[18] 熊志刚,黄树彩,赵炜,等.均方根嵌入式容积粒子PHD多目标跟踪方法 [J].自动化学报,2017,43(2):238-247.
[19] 袁晓波,张超,詹银虎.强跟踪自适应SRCKF的卫星姿态确定算法 [J].测绘科学,2018(3):6-11.
[20] 左宗玉.四旋翼无人飞行器自适应轨迹跟踪控制 [C].Proceedings of the 30th Chinese Control Conference,Yantai,2011:2435-2439.
[21] 王仲伦.基于反步法的四旋翼无人机容错控制 [D].南昌:华东交通大学,2017.
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