基于递推最小二乘与互补滤波的姿态估计
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摘要
针对基于微机电系统(MEMS)的惯性导航系统中陀螺噪声较大导致姿态漂移的问题,本文基于递推最小二乘(RLS)与互补滤波器提出一种提高姿态估计精度的方法.该方法从陀螺去噪算法和姿态解算原理两个方面提高姿态估计精度:在陀螺去噪方面,为克服传统递推最小二乘的不足,提出一种随机加权的递推最小二乘法,利用随机加权实现对偏差的估计;在姿态解算方面,在传统互补滤波器的基础上通过自适应调整比例-积分(PI)参数来调整滤波器的交接频率,最终得到陀螺积分值的高通滤波和加速度计的低通滤波的叠加.转台静态和动态实验结果表明,使用本文所提方法后,有效降低了陀螺噪声,姿态估计精度明显提升.
Aiming at the problem of attitude drift caused by gyroscope noise in inertial navigation system based on micro-electromechanical system(MEMS),a method to improve attitude estimation based on recursive least squares(RLS)and complementary filter is proposed.The accuracy of attitude estimation is improved from the aspects of gyro de-noising algorithm and attitude solving principle:in terms of gyro de-noising,in order to overcome the deficiency of traditional recursive least squares,a random weighted recursive least squares method is proposed;in the aspect of attitude calculation,on the basis of the traditional complementary filter,the switching frequency of the filter is adj usted by adaptive proportionalintegral(PI)parameter adjustment,and finally the superposition of high pass filtering of gyro integral value and low pass filtering of accelerometer is obtained.The static and dynamic test results of the turntable showed that the proposed method can effectively reduce the noise of gyro and improve the accuracy of attitude estimation.
Aiming at the problem of attitude drift caused by gyroscope noise in inertial navigation system based on micro-electromechanical system(MEMS),a method to improve attitude estimation based on recursive least squares(RLS)and complementary filter is proposed.The accuracy of attitude estimation is improved from the aspects of gyro de-noising algorithm and attitude solving principle:in terms of gyro de-noising,in order to overcome the deficiency of traditional recursive least squares,a random weighted recursive least squares method is proposed;in the aspect of attitude calculation,on the basis of the traditional complementary filter,the switching frequency of the filter is adj usted by adaptive proportionalintegral(PI)parameter adjustment,and finally the superposition of high pass filtering of gyro integral value and low pass filtering of accelerometer is obtained.The static and dynamic test results of the turntable showed that the proposed method can effectively reduce the noise of gyro and improve the accuracy of attitude estimation.
引文
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[2]MAO B,WEI W J,TONG W J,et al.MEMS gyro denoising based on second generation wavelet transform.First International Conference on Pervasive Computing Signal Processing and Applica tions.Harbin:IEEE,2010:255-258.
[3]SUN J、XU X,LIU Y,et al.FOG random drift signal denoising based on the improved AR model and modified Sage-Husa adaptive Kalman filter.Sensors,2016,16(7):1073-1095.
[4]YANG G,LIU Y,WANG Y,et al.EMD interval thresholding denoising based on similarity measure to select relevant modes.Signal Processing,2015,109(C):95-109.
[5]AHMED H,TAHIR M.Accurate attitude estimation of a moving land vehicle using low-cost MEMS IMU sensors.IEEE Transactions on Intelligent Transportation Systems,2017,18(7):1723-1739.
[6]SHENG H,ZHANG T.MEMS-based low-cost strap-down AHRS research.Measurement,2015,59:63-72.http://dx.doi.org/10.1016/j.measurement.2014.09.041.
[7]ZHANG Dexian,NIE Guigen.Adaptive attitude quaternion Kalman filter based on factored quatemions.Control Theon&Applications,2018,35(3):367-374.(张德先,聂桂根.基于分解四元数的自适应姿态四元数卡尔曼滤波.控制理论与应用,2018,35(3):367-374.)
[8]HAN Zhiguo,ZHANG Ke,L(U|¨)Meibai,et al.Adaptive non-singular fast terminal sliding mode based tolerant control for spacecraft.Control Theory&Applications,2016,33(8):1046-1053.(韩治国,张科,吕梅柏,等.航天器自适应快速非奇异终端滑模容错控制.控制理论与应用,2016,33(8):1046-1053.)
[9]SHEN Xiaowei,YAO Mindi,ZHAO Peng.Antenna attitude estimation based on compleuentary filter for mobile satellite communication.Journal of Chinese Inertial Technology,2011,19(2):194-197.(沈晓卫,姚敏立,赵鹏.基于互补滤波的动中通天线姿态估计.中国惯性技术学报,2011,19(2):194-197.)
[10]LAN Jianjun,LI Jialu,LIU Ying,et al.Control of wheeled inverted pendulum based on adaptive complementary filter algorithm.Modular Machine Tool&Automatic Manufacturing Technique,2014,(6):66-68.DOI:10.13462/j.cnki.mmtamt.2014.06.018.(兰建军,李佳璐,刘颖,等.基于自适应互补滤波算法的轮式倒立摆控制.组合机床与自动化加工技术,2014,(6):66-68.DOI:10.13462/j.cnki.mmtamt.20 14.06.018.)
[11]WANG Li,ZHANG Zheng,SUN Ping.An adaptive complementary filter for attitude estimation.Control Engineering of China,2015,22(5):881-886.(王立,章政,孙平.一种自适应互补滤波姿态估计算法.控制工程,2015,22(5):881-886.)
[12]LI Yinguo,TANG Zhuoqun,HUANG Lei.Recursive least-squares and minimum-norm algorithm for system identification without persistent excitation condition.Control Theory&Applications,2009.26(4):365-370.(李银国,汤卓群,黄镭.非持续激励条件下系统辨识递推最小二乘最小范数算法.控制理论与应用,2009,26(4):365-370.)
[13]ELISEI-ILIESCU C,STANCIU C,PALEOLOGU C,et al.Robust variable-regularized RLS algorithms.Hands-free Speech Communications and Microphone Arrays.San Francisco:IEEE,2017:171-175.
[14]ZHENG Zhongguo.Random weighting method.Acta Mathematicae Applicatae Sinica,1987,10(2):247-253.(郑忠国.随机加权法.应用数学学报,1987,10(2):247-253.)
[15]MARTINEK R,KAHANKOVA R,NAZERAN H,et al.Noninvasive fetal monitoring:a maternal surface ECG electrode placement-based novel approach for optimization of adaptive filtercontrol parameters using the LMS and RLS algorithms.Sensors,2017,17(5):1154-1185.
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