GAM-Based Mooring Alignment for SINS Based on An Improved CEEMD Denoising Method

Abstract

To solve the self-alignment problem of the Strapdown Inertial Navigation System (SINS), a novel adaptive filter based on Complementary Ensemble Empirical Mode Decomposition (CEEMD) is proposed. The Gravitational Apparent Motion (GAM) is used in the coarse alignment, and the problem of obtaining the attitude matrix between the body frame and the navigation frame is attributed to obtaining the matrix between the initial body frame and the current navigation frame using two gravitational apparent motion vectors at different moments. However, the accuracy and time of this alignment method always suffer from the measurement noise of sensors. Thus, a novel adaptive filter based on CEEMD using an l 2 -norm to calculate the similarity measure between the Probability Density Function (PDF) of each Intrinsic Mode Function (IMF) and the original signal is proposed to denoise the measurements of the accelerometer. Furthermore, the advantage of this filter is verified by comparing with other conventional denoising methods, such as PDF-based EMD (EMD-PDF) and the Finite Impulse Response (FIR) digital low-pass filter method. The results of the simulation and experiments indicate that the proposed method performs better than the conventional methods in both alignment time and alignment accuracy.

Empirical Mode Decomposition-Based Filter Applied to Multifocal Electroretinograms in Multiple Sclerosis Diagnosis

“…Of the 4 approximations obtained with Equation (4), the one containing the useful information is selected according to the principle of partial reconstruction. There are several options, such as energy-based methods [44], correlation-based methods [45][46][47], probability density function based methods [48], entropy [49], higher order statistics [50], mutual information [51], and mutual information entropy [52]. In this paper, the method described in [47] has been used, selecting as the filtered signal the one among the 4 approximations obtained in Equation (4) that has the highest Pearson correlation coefficient with the signal from the same sector of the normative database.…”

A Novel Coarse Alignment Method for SINS Using Special Orthogonal Group Optimal Estimation

“…Currently, the coarse alignment methods for SINS on a swing base can be divided into two categories: one category is to directly determine the initial attitude matrix through the gravity vectors of two or more moments by the TRIAD algorithm [ 8 , 9 , 10 , 11 , 12 ]. In [ 8 ], the initial rotation matrix is decomposed by attitude matrix decomposition to isolate the influence of the swing base and estimate the rotation matrix by two noncollinear gravity vectors.…”

“…In [ 8 ], the initial rotation matrix is decomposed by attitude matrix decomposition to isolate the influence of the swing base and estimate the rotation matrix by two noncollinear gravity vectors. On this basis, [ 9 , 10 , 11 ] one can reduce the impact of the inertial measurement unit (IMU) output noise on the estimation results by adding a low-pass filter. Different from other studies [ 9 , 10 , 11 ], the authors of [ 12 ] used an LMS adaptive filter to reduce sensor noise, and the authors of [ 13 ] computed two initial rotation matrices using the gravity vectors from multiple moments and weighted averaged according to the standard deviation observed at each time, which improves the alignment accuracy.…”

“…On this basis, [ 9 , 10 , 11 ] one can reduce the impact of the inertial measurement unit (IMU) output noise on the estimation results by adding a low-pass filter. Different from other studies [ 9 , 10 , 11 ], the authors of [ 12 ] used an LMS adaptive filter to reduce sensor noise, and the authors of [ 13 ] computed two initial rotation matrices using the gravity vectors from multiple moments and weighted averaged according to the standard deviation observed at each time, which improves the alignment accuracy. In an earlier study [ 14 ], to extract the effective observation vectors from the inertial sensors’ outputs, a method for parameter recognition and vector reconstruction was designed, where an adaptive Kalman filter was employed to estimate the unknown parameters.…”

Noise Reduction for Modal Parameter Identification of the Measured FRFs Using the Modal Peak-Based Hankel-SVD Method

“…To eliminate the noise and purify the measured FRF signals, the noise reduction method should be exploited. In the past decades, many noise reduction methods such as the traditional low/ high-pass filter [3], singular value decomposition (SVD) [4,5], minimum mean square error (MMSE) [6,7], Wiener filter [8,9], wavelet transform (WT) [10,11], empirical mode decomposition (EMD) [12,13], independent component analysis (ICA) [14,15], and deep recurrent neural network (DRNN) [16,17] have been developed. Among these methods, the SVD-related filters draw the most noticeable attention for their convenience, simplicity, and nonparametric properties and have been widely used in the noise reduction of various signals in engineering.…”