A Denoising Method for Fiber Optic Gyroscope Based on Variational Mode Decomposition and Beetle Swarm Antenna Search Algorithm

Abstract

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 K 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.

Research on the recognition model of exercise fatigue based on the fusion of sEMG and ECG signals

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Temperature Drift Compensation of Fiber Optic Gyroscopes Based on an Improved Method

“…The experimental results demonstrated that the novel method is superior to both the EMD-HRD and EMD-SFT approaches with a higher SNR ratio. Song et al [ 18 , 19 ] proposed a hybrid algorithm of an optimized local mean decomposition–kernel principal component analysis (OLMD–KPCA) method. The Allan variance analysis results indicated that the Q, N, and B reduced from 12.915 to 2.429 × 10 −1 , 1.8 × 10 −2 to 5.061 × 10 −4 , and 3.01 × 10 −1 to 1 × 10 −2 based on the X axis; from 7.680 to 1.38 × 10 −1 , 1.2 × 10 −2 to 2.647 × 10 −4 , and 1.72 × 10 −1 to 6 × 10 −3 based on the Y axis; and from 7.093 to 1.25 × 10 −1 , 1 × 10 −2 to 1.549 × 10 −4 , and 1.53 × 10 −1 to 7 × 10 −3 based on the X axis, respectively.…”

An Improved Beetle Antennae Search Algorithm Based on the Elite Selection Mechanism and the Neighbor Mobility Strategy for Global Optimization Problems

“…In addition, its program code is simple and can quickly obtain the optimal solution under the condition of a stable convergence [20][21][22][23]. At present, it has been successfully applied to a variety of industrial engineering optimization problems, and has a high potential research value [24][25][26][27][28][29]. Ameer Tamoor Khan et al proposed the quantum beetle antennae search algorithm and applied it in the constrain portfolio problem [30].…”