基于MS-IMMIKF的MEMS陀螺输出信号消噪处理

MEMS Gyro's Output Signal De-noising Processing Based on MS-IMMIKF

陀螺用于感知稳定平台相对于惯性空间的角速率,进而得到稳定平台在惯性空间的姿态,通过反馈稳定平台伺服控制机构相应的控制量来实现对稳定平台的稳定控制。通过分析陀螺随机误差对稳定平台精度影响,提出了一种新的陀螺随机漂移处理方法。首先针对机动跟踪领域的Singer模型中人为设定机动频率α的不合理性,对机动频率α进行在线估计,提出基于Modified Singer(MS)模型直接对陀螺输出进行建模的方法;在此基础上建立基于交互式多模型(IMM)的改进卡尔曼滤波算法(MS-IMMIKF)对陀螺输出随机误差进行处理,并对交互式多模型的改进卡尔曼滤波(IKF)进行理论分析和推导;通过数值仿真分析和稳定平台中某型号陀螺试验验证和仿真计算得出,静态滤波后均方根误差达到0.022 7°/s,证明了该方法对陀螺漂移处理的有效性与可行性;最后通过稳定平台动静态稳定试验表明,MS-IMMIKF滤波算法对提高稳定平台精度有效且实用。

By controlling the amount of feedback to the corresponding stable platform servo control mechanism to achieve stability control for stabilizing the platform,gyro is used to measure angular velocity of stabilized platform with respect to inertial space and then get the gesture of stabilized platform in inertial space. By analyzing the influence of gyro random error on the stabilized platform accuracy in detail,a processing method for gyro random drift was proposed. Firstly,owing to the values of the parameter α is artificially unreasonable of the Singer model in the field of motor tracking,the parameter ofα in real time was estimated adaptively,and a new method of gyro modeling output module directly was established based Modified Singer（ MS）; secondly,on the basis of the established output model,an improved Kalman filter（ IKF） based on interacting multiple model（ IMM） was applied to the gyro＇s output random error processing, and the theoretical analysis and derivation of the algorithm were explained in detail. Through numerical simulations and verification experiments of a certain type of gyro stabilized platform analysis,the static filter root mean square error is only 0. 022 7° / s,and this method was proved to be feasible and effective with the gyro drift treatments; finally,through the dynamic and static steady experiments of gyro stabilized platform,the MS-IMMIKF filtering algorithm was proved to be effective and practical for improving the accuracy of the stable platform. The dynamic and static steady experiments of gyro stabilized platform results show that the accuracy of the stable platform could be controlled within error of 3° and 2. 5° respectively after the MS-IMMIKF algorithm filtering.