基于Hausdorff距离的光纤陀螺阈值滤波算法

Threshold Filtering Algorithm of FOG Based on Hausdorff Distance

要实现采矿机组的智能化,必须对采矿机械的位姿进行实时准确地控制。由于开采工作面环境复杂,可选用捷联惯性导航技术来实现对采矿机械的监测。光纤陀螺作为捷联惯导系统的核心元件,其随机噪声制约着惯性导航系统的精度。传统的建模滤波及小波消噪方法由于其自身的局限性,不能保证准确性和去噪效果。所以结合经验模态分解(Empirical Mode Decomposition,EMD),提出了Hausdorff距离(Hausdorff Distance,HD)筛选准则与阈值滤波相结合的去噪算法。该算法以本征模态函数(Intrinsic Mode Function,IMF)与原始信号概率密度函数(Probability Density Function,PDF)的Hausdorff距离为判别依据,对所有IMF进行筛选,之后引入阈值对筛选出的IMF进行滤波处理,最后将其与余项重构。通过试验比较了软硬阈值的滤波效果,确定了该算法采用硬阈值。为验证算法的有效性,将该算法与其他3种方法进行比较,仿真信号与实测陀螺静态漂移数据的试验结果表明了该方法的优越性,能够有效降低陀螺的各项随机误差。

In order to realize the intellectualization of mining unit,the position and posture of mining machinery must be controlled in real time and accurately.Because the mining working face environment is complex,strapdown inertial navigation technology can be used to monitor mining machinery.As the core component of strapdown inertial navigation system,the random noise of fiber optic gyro restricts the precision of inertial navigation system.Traditional modeling filtering and wavelet denoising methods can not guarantee the accuracy and denoising result because of their own limitations.Combined with empirical mode decomposition(EMD),a denoising algorithm combining Hausdorff distance(HD)screening criteria and threshold filtering is proposed.In this algorithm,HD of probability density functions(PDF)which are the estimate of intrinsic mode function(IMF)and the original signal is used as the discrimination basis to screen all IMFs,and then thresholding is introduced to filter the selected IMFs and reconstruct it with remaining terms.The filtering results of soft and hard thresholds is compared through tests and the hard threshold are adopted in the algorithm.In order to verify the effectiveness of the algorithm,the algorithm is compared with EMD-Cor,EMD-CMSE and other methods.Simulation and test results show the superiority of the method,which can effectively reduce the random errors of gyro.