Wang Pengfei, Gao Yanbin, Wu Menghao, Zhang Fan, Li Guangchun, Qin Chao
Collage of Automation, Harbin Engineering University, Harbin 150001, China.
Entropy (Basel). 2020 Jul 13;22(7):765. doi: 10.3390/e22070765.
Fiber optic gyroscope (FOG) is one of the important components of Inertial Navigation Systems (INS). In order to improve the accuracy of the INS, it is necessary to suppress the random error of the FOG signal. In this paper, a variational mode decomposition (VMD) denoising method based on beetle swarm antenna search (BSAS) algorithm is proposed to reduce the noise in FOG signal. Firstly, the BSAS algorithm is introduced in detail. Then, the permutation entropy of the band-limited intrinsic mode functions (BLIMFs) is taken as the optimization index, and two key parameters of VMD algorithm, including decomposition mode number and quadratic penalty factor α , are optimized by using the BSAS algorithm. Next, a new method based on Hausdorff distance (HD) between the probability density function (PDF) of all BLIMFs and that of the original signal is proposed in this paper to determine the relevant modes. Finally, the selected BLIMF components are reconstructed to get the denoised signal. In addition, the simulation results show that the proposed scheme is better than the existing schemes in terms of noise reduction performance. Two experiments further demonstrate the priority of the proposed scheme in the FOG noise reduction compared with other schemes.
光纤陀螺仪（FOG）是惯性导航系统（INS）的重要部件之一。为了提高INS的精度，有必要抑制FOG信号的随机误差。本文提出一种基于甲虫群天线搜索（BSAS）算法的变分模态分解（VMD）去噪方法，以降低FOG信号中的噪声。首先，详细介绍了BSAS算法。然后，将带限固有模态函数（BLIMFs）的排列熵作为优化指标，利用BSAS算法对VMD算法的两个关键参数，即分解模态数 和二次惩罚因子α 进行优化。接下来，本文提出一种基于所有BLIMFs的概率密度函数（PDF）与原始信号的PDF之间的豪斯多夫距离（HD）的新方法来确定相关模态。最后，对所选的BLIMF分量进行重构以获得去噪信号。此外，仿真结果表明，所提方案在降噪性能方面优于现有方案。两个实验进一步证明了所提方案在FOG降噪方面相对于其他方案的优越性。
