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基于多传感器信息融合的水下运动体速度测量
引用本文:徐,侃,徐国华,夏英凯等.基于多传感器信息融合的水下运动体速度测量[J].传感器与微系统,2014(5):144-147.
作者姓名:    徐国华  夏英凯等
作者单位:华中科技大学船舶与海洋工程学院轮机工程系,湖北武汉430074
基金项目:国家自然科学基金资助项目(51079061)
摘    要:针对水下运动体测速中存在的难度大、精度低等问题,提出一种基于加速度计和机械测速装置的融合测速方法。该方法实质上是一种改良的模糊自适应卡尔曼滤波,在常规卡尔曼滤波的基础上,引入了自适应参数,并通过模糊控制器对残差的监控来实时调整自适应参数。经Matlab仿真和实际试验证实:该方法可以有效地提高卡尔曼滤波器的跟踪性,并改善滤波效果,适用于通用的水下运动体测速。

关 键 词:加速度计  机械测速装置  水下运动体  融合测速  模糊自适应  卡尔曼滤波

Velocity measurement of underwater vehicle based on multi-sensor information fusion
Affiliation:XU Kan, XU Guo-hua, XIA Ying-kai, ZHA0 Yin ( Department of Marine Engineering, School of Naval Archlteeture and Ocean Engineering, I-Iuazhong University of Science and Technology, Wuhan 430074, Chlna)
Abstract:A fuse velocity measurement method based on accelerometer and mechanical velocity measuring device is proposed ,aiming at superior difficulty and low accuracy exist in the velocity measurement of underwater vehicle. This method is essentially a modified fuzzy adaptive Kalman filtering,by introducing adaptive parameters to general Kalman filtering, which is adjusted by fuzzy controller through monitoring of residuals. Through the Matlab simulation and experimental verification,this method can effectively improve the traceability of the Kalman filter and improve the filtering effect, which is suitable for general velocity measurement of underwater vehicle.
Keywords:accelerometer  mechanical velocity measuring device  underwater vehicle  fuse velocitymeasurement  fuzzy adaptive  Kalman filtering
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