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 , 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.
Highlights
Inertial navigation is an autonomous navigation method
For the sake of better determining the combination of parameters, this paper proposes a parameter optimization method based on the beetle swarm antenna search (BSAS) algorithm and permutation entropy
This paper presents a new method for the denoising of a Fiber optic gyroscope (FOG) signal
Summary
Narasimhappa et al proposed a robust adaptive Kalman filtering algorithm to filter the FOG signal directly [12] In this method, a new covariance matrix of measurement noise is designed to improve. The WT method can effectively reduce the random noise in FOG signal, but this method needs to preset wavelet basis function and decomposition level, so the adaptability is not good. Wu et al employed VMD to decompose the FOG signal, and the decomposed signal was filtered based on the generalized morphological filter (CGMF) They proved the feasibility of the proposed scheme through experiments [21]. A traditional trial and error method that depends on experience will lead to an unreliable result and will waste a lot of time It will greatly limits the performance of the VMD algorithm. In the last section, the conclusion of this paper is presented, and the future work is prospected
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