基于自适应KF算法的模态切换水下机器人导航系统
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摘要
针对新型水下检测以及作业机器人在导航精度、体积方面的要求,设计了一套基于微机电器件的组合导航系统.系统为抑制陀螺漂移而采用了互补滤波方法,以四元数为估计对象设计卡尔曼滤波器.文中采用改进的自适应卡尔曼滤波器,增大新近数据的作用,减小陈旧数据的作用,避免滤波发散,提高导航精度.通过水池实验表明互补滤波和自适应卡尔曼滤波结合能够获得比较精确、稳定的水下机器人导航信息.同时,基于实测数据进行的算法仿真表明改进后的渐消记忆指数加权自适应卡尔曼滤波可以在一定程度上改善导航效果.
Based on the requirements for the navigation precision and volume of detection and working of the novel underwater vehicle,a set of integrated inertial navigation system based on MEMS devices has been designed.Complementary filters have been used to restrain the drifts of gyroscope and the quaternions are selected as estimating values to design a Kalman Filter. This paper applies an improved adaptive Kalman Filter( AKF),stressing the effects of new data with adding weights to gradually leave old data behind to avoid divergence and to further improve the navigation effects. The pool experimental results indicates that the complementary filter and the Kalman filter can get stable and high-precision effects. Meanwhile,algorithm simulations based on the real measured data demonstrate that the improvement of fading memory AKF based on exponential weighting can improve the navigation results.
Based on the requirements for the navigation precision and volume of detection and working of the novel underwater vehicle,a set of integrated inertial navigation system based on MEMS devices has been designed.Complementary filters have been used to restrain the drifts of gyroscope and the quaternions are selected as estimating values to design a Kalman Filter. This paper applies an improved adaptive Kalman Filter( AKF),stressing the effects of new data with adding weights to gradually leave old data behind to avoid divergence and to further improve the navigation effects. The pool experimental results indicates that the complementary filter and the Kalman filter can get stable and high-precision effects. Meanwhile,algorithm simulations based on the real measured data demonstrate that the improvement of fading memory AKF based on exponential weighting can improve the navigation results.
引文
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[2]WENDEL J,MEISTER O,SCHLAILE C,et al.An integrated GPS/MEMS-IMU navigation system for an autonomous helicopter[J].Aerospace Science and Technology,2006,10(6):527-533.
[3]孙玉山,代天娇,赵志平.水下机器人航位推算导航系统及误差分析[J].船舶工程,2010,32(5):67-72.SUN Yushan,DAI Tianjiao,ZHAO Zhiping.Error analysis of dead-reckoning navigation system for autonomous underwater vehicle[J].Ship Engineering,2010,32(5):67-72.(in Chinese)
[4]唐国元,徐正武,黄道敏,等.基于姿态控制力矩陀螺的水下航行体运动建模方法研究[J].中国造船,2014,55(2):18-27.TANG Guoyuan,XU Zhengwu,HUANG Daomin,et al.Research on the modeling approach for underwater craft motion based on control moment gyros[J].Shipbuilding of China,2014,55(2):18-27.(in Chinese)
[5]丁君.基于微惯性传感器的姿态算法研究[D].上海交通大学,2013:1-6.
[6]吕印新,肖前贵,胡寿松.基于四元数互补滤波的无人机姿态解算[J].燕山大学学报,2014,38(2):175-180.LV Yinxin,XIAO Qiangui,HU Shousong.Attitude estimation based on quaternion complementary filter for UAV[J].Journal of Yanshan University,2014,38(2):175-180.(in Chinese)
[7]孙金秋,游有鹏,傅忠云.基于共轭梯度法和互补滤波相结合的姿态解算算法[J].传感技术学报,2014,27(4):524-528.SUN Jinqiu,YOU Youpeng,FU Zhongyun.Attitude estimation based on conjugate gradient and complementary filter[J].Chinese Journal of Sensors and actuators,2014,27(4):524-528.(in Chinese)
[8]梁延德,程敏,何福本,等.基于互补滤波器的四旋翼飞行器姿态解算[J].传感器与微系统,2011,30(11):56-58.LIANG Yande,CHENG Min,HE Fuben,et al.Attitude estimation of a quad-rotor aircraft based on complementary filter[J].Transducer and Microsystem Technologies,2011,30(11):56-58.(in Chinese)
[9]刘建业,曾庆化,赵伟,等.导航系统理论与应用[M].西北工业大学出版社,2010:20-23.
[10]王冲,曾庆军.自适应滤波算法在AUV组合导航中的方法[J].中南大学学报:自然科学版,2013,44(S2):155-159.WANG Chong,ZENG Qingjun.Methods of adaptive filter to integrated navigation system of autonomous underwater vehicle[J]Journal of Central South University(Science and Technology),2013,44(S2):155-159.(in Chinese)
[11]李晔,苏玉民,万磊,等.自适应卡尔曼滤波技术在水下机器人运动控制中的应用[J].中国造船,2006,47(4):83-88.LI Ye,SU Yumin,WAN Lei,et al.Application of adaptive kalman filter in underwater vehicle motion control[J].Shipbuilding of China,2006,47(4):83-88.(in Chinese)
[12]蔡佳,黄长强,井会锁,等.基于指数加权的改进衰减记忆自适应滤波算法[J].探测与控制学报,2013,35(4):21-26.CAI Jia,HUANG Changqiang,JING Huisuo,et al.Adaptive fading memory Kalman filtering algorithm based on exponential weighting[J].Journal of Detection&Control,2013,35(4):21-26.(in Chinese)