一种新的MEMS陀螺仪信号去噪方法

发布时间：2018-08-16 16:48

【摘要】：为提高MEMS陀螺仪输出信号的去噪效果,将稀疏分解(sparse decomposition)与提升小波变换(lifting wavelet transform)相结合,提出了一种新的信号去噪方法.首先,建立MEMS陀螺带噪信号的误差模型,并利用小波提升正变换计算带噪信号的非稀疏的小波系数;然后,利用稀疏分解理论恢复小波系数的稀疏性;最后,再通过小波提升反变换重构信号,从而达到去噪的目的.考虑到梯度投影(gradient projection)算法具有全局最优解,运算效率更高,将梯度投影思想引入恢复信号稀疏性的过程中,提出了基于梯度投影的稀疏分解算法,给出了利用梯度投影算法进行信号系数分解的具体步骤,大大简化了计算复杂度,同时提升了算法的稳定性.为验证所提方法的性能,进行了MEMS陀螺信号去噪的静态实验和跑车实验.实验结果表明,此种方法在动静态条件下都可以有效地去除MEMS陀螺仪输出信号中的噪声,尤其是在静态条件下的去噪效果要优于小波阈值滤波方法.同时采用的梯度投影算法相比于正交匹配追踪算法和基追踪算法具有更高的运算效率.
[Abstract]:In order to improve the denoising effect of output signal of MEMS gyroscope, a new signal denoising method is proposed by combining sparse decomposition (sparse decomposition) with lifting wavelet transform (lifting wavelet transform). First of all, the error model of MEMS gyroscope noise signal is established, and the wavelet lifting positive transform is used to calculate the non-sparse wavelet coefficient of the noisy signal. Then, the sparse decomposition theory is used to restore the sparsity of the wavelet coefficient. Then the signal is reconstructed by lifting the wavelet transform to achieve the purpose of denoising. Considering that the gradient projection (gradient projection) algorithm has the global optimal solution and the higher computational efficiency, the gradient projection algorithm is introduced into the restoration process of signal sparsity, and a sparse decomposition algorithm based on gradient projection is proposed. The detailed steps of signal coefficient decomposition using gradient projection algorithm are given, which greatly simplifies the computational complexity and improves the stability of the algorithm. In order to verify the performance of the proposed method, static and sports car experiments of MEMS gyroscope signal denoising are carried out. The experimental results show that this method can effectively remove the noise in the output signal of MEMS gyroscope under dynamic and static conditions, especially in static condition, the denoising effect is better than the wavelet threshold filtering method. The gradient projection algorithm is more efficient than the orthogonal matching tracking algorithm and the base tracking algorithm.
【作者单位】：火箭军工程大学控制工程系;
【基金】：国家自然科学基金(61304001)
【分类号】：TN911.7;TN96

【相似文献】