基于自适应H_∞滤波的组合导航方法研究  

Research on integrated navigation method based on adaptive H_∞ filter

作　　者：刘晓光[1,2,3] 胡静涛[1,2,3] 王鹤[1,2,3] 

机构地区：[1]中国科学院沈阳自动化研究所,沈阳110016 [2]中国科学院大学,北京100049 [3]中国科学院网络化控制系统重点实验室,沈阳110016

出　　处：《仪器仪表学报》2014年第5期1013-1021,共9页Chinese Journal of Scientific Instrument

基　　金：国家863计划(2013AA040403);"十二五"国家科技支撑计划(2011BAD20B06)资助项目

摘　　要：针对常规鲁棒滤波参数由初始设定而使滤波具有较大保守性的问题,提出了一种基于自适应鲁棒滤波的多传感器组合导航方法。该方法能够自动调节鲁棒滤波器参数,从而兼顾系统的精度和鲁棒性。通过运动学分析,建立了试验车的二维运动学模型,基于所建立的运动学模型,采用自适应鲁棒滤波的方法,能够实现对试验车位置、速度和航向角的精确估计。最后对实验车实际行走的数据进行了仿真对比实验,实验结果表明该方法能够紧密地跟踪滤波器的性能,自适应地调整滤波器参数的取值。与卡尔曼滤波相比,在系统噪声统计特性未知的情况下,该方法能够提供优于卡尔曼滤波的导航精度。Aiming at the problem that conventional robust filter is more conservative because the parameter is set initially,a multi-sensor integrated navigation method based on adaptive H∞ filter is proposed.The method can automatically adjust the robust filter parameters,thus both the accuracy and robustness of the system are considered.Based on the kinematics analysis,the two-dimensional kinematic model of the test vehicle was established.The position,speed and heading angle of the test vehicle can be estimated based on the estab lished kinematics model using adaptive robust filtering method.Finally,the simulation experiment was carried out based on the actual walking data.The results show that the method can track the performance of the filter closely,and adaptively adjust the filter parameters.Compared with Kalman filter,the method can provide better navigation accuracy under the condition that the noise statistical characteristic of the system is unknown.

关 键 词：组合导航 H∞鲁棒滤波 自适应 数据融合 

分 类 号：S24[农业科学—农业电气化与自动化] TP273[农业科学—农业工程]