陀螺随机误差补偿中一种改进混合降噪法

doi:10.19665/j.issn1001-2400.2022.05.008

摘要/Abstract

摘要：

为降低微机电系统陀螺测量数据中的随机误差,针对当载体运动状态突然改变导致陀螺传感器数据发生阶跃变化的情况,提出了一种改进的自适应噪声完备经验模态分解-前向线性预测滤波(CEEMDAN-FLP)的混合降噪方法。改进算法首先对于低阶噪声本征模态函数采用软阈值滤波,避免了常规方法将噪声本征模态函数直接去除引起高频信号丢失的问题,同时对混合本征模态函数采用前向线性预测滤波,避免阈值提升引起的过度滤波问题;最后对滤波结果与信号本征模态函数进行数据重构。通过仿真验证,表明改进算法滤波结果的均方根误差与滤波前相比减小了约51.53%,与经验模态分解滤波算法相比减小了约17.39%;通过实测数据验证,表明改进算法滤波后的陀螺数据与基于CEEMDAN的算法滤波后的陀螺数据分别用于姿态解算,在不明显增加运算负担的同时,改进算法姿态累积误差仅约是CEEMDAN算法姿态累积误差的20.56%。可见,改进算法可以有效地提高传感器的测量精度。

关键词: 经验模态分解, 自适应噪声完备经验模态分解, 本征模态函数, 前向线性预测滤波

Abstract:

In order to reduce the random error in the measurement data of a micro-electromechanical system(MEMS) gyroscope,an improved complete ensemble empirical mode decomposition with the adaptive noise-forward linear predictive filtering (CEEMDAN-FLP) hybrid noise reduction method is proposed when the sudden change of the carrier motion state causes the step change of gyro sensor data.The improved algorithm uses a soft interval thresholding filter for low-order noise intrinsic mode functions (IMFs),which avoids the problem of high frequency signal loss caused by the conventional method of removing noisy IMFs directly.At the same time,an FLP filter is used for the mixed IMFS to avoid excessive filtering caused by threshold elevation.Finally,data reconstruction is carried out between the filtering results and signal IMFs.Simulation results show that the root-mean-square error of the improved algorithm is reduced by 51.53% compared with the original signal,and by 17.39% compared with the EMD filtering algorithm.The measured data verify that the gyro data filtered by the improved algorithm and the gyro data filtered by the CEEMDAN algorithm are respectively used for attitude calculation,and that the attitude cumulative error of the improved algorithm is only 20.56% of the attitude cumulative error of the conventional algorithm without significantly increasing the operation burden.It can be seen that the improved algorithm can effectively improve the measurement accuracy of the sensor.

Key words: empirical mode decomposition, complete ensemble empirical mode decomposition with adaptive noise, intrinsic mode functions, forward linear prediction algorithm

中图分类号:

V441

田易,阎跃鹏,钟燕清,李继秀,孟真. 陀螺随机误差补偿中一种改进混合降噪法[J]. 西安电子科技大学学报, 2022, 49(5): 68-75.

TIAN Yi,YAN Yuepeng,ZHONG Yanqing,LI Jixiu,MENG Zhen. Improved hybrid method for gyro random noise compensation[J]. Journal of Xidian University, 2022, 49(5): 68-75.

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误差	原始信号	EMD	CEEMDAN	CEEMDAN-FLP	改进CEEMDAN-FLP
43 s处滚转角误差	2.62	4.58	2.59	1.41	0.91
57 s处滚转角误差	2.09	5.47	2.14	1.32	0.44