Abstract
In order to suppress the random shift error of laser gyro and improve the practical precision of inertial navigation system, an improved gyro filtering method is proposed by combining the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and principal component analysis (PCA). Firstly, the gyro signal is decomposed by CEEMDAN, and the noise energy of each intrinsic mode function (IMF) is estimated according to the distribution model of noise energy. Then, on basis of noise energy, the principal component analysis is used to remove the noise IMF to achieve the final denoising of gyro signal. In the proposed method, CEEMD can improve the mode mixing and denoising effect of gyro signal. Moreover, PCA is used to decompose each IMF. According to the noise energy, the noise of each IMF is removed adaptively to avoid the selection of noise IMF and better retain the useful information of the signal. The proposed method is completely dependent on the characteristics of gyro signal and has good adaptability and strong denoising ability. Furthermore, the filtered effect of different methods is analyzed by overlapping Allan variance. The experimental results show that the proposed method can suppress the gyro random drift more efficiently, and the effect of removing noise is better than EMD threshold method and EMD correlation coefficient method.
Highlights
Laser gyroscope is a new generation of inertial measurement element
The results show that the proposed method can suppress the gyro random drift more effectively than the existing empirical mode decomposition (EMD) noise reduction methods
The laser gyro signal denoising algorithm based on CEEMDAN and principal component analysis (PCA) proposed in this paper are as follows: Conduct CEEMDAN on the gyro signal x(t), and set its intrinsic modal function as intrinsic mode function (IMF) (k = 1,2 ⋯, K), and the remainder as R(t); Let ε(V ) = ε(IMF ), and calculate the energy ε(W ) (k ≥ 2) of the noise contained in IMF according to Eq (13)
Summary
Laser gyroscope is a new generation of inertial measurement element. Compared with traditional mechanical gyroscope, it has many outstanding advantages and has been widely used in many fields [1]. The wavelet denoising method was proposed and the wavelet coefficients were optimized through sparse redundancy representation [5], but the appropriate threshold value and wavelet basis function is difficult to be LASER GYRO SIGNAL FILTERING BY COMBINING CEEMDAN AND PRINCIPAL COMPONENT ANALYSIS. In reference [12], EMD algorithm with traditional time series modeling filtering method was used to compensate the error of gyroscope. In order to overcome the influence of EMD mode mixing and noise IMF selection on denoising, a gyro signal denoising method combining CEEMDAN and principal component analysis (PCA) is proposed in this paper. This method uses CEEMD instead of EMD to achieve almost perfect signal reconstruction. The results show that the proposed method can suppress the gyro random drift more effectively than the existing EMD noise reduction methods
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
