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 -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.
Highlights
The Strapdown Inertial Navigation System (SINS) has been applied in various fields and developed rapidly because of its independence and accuracy [1,2,3,4,5]
This paper introduces a Gravitational Apparent Motion (GAM)-based self-alignment method by a novel adaptive filter called effective Intrinsic Mode Function (IMF) selection based on Complementary Ensemble Empirical Mode Decomposition (CEEMD)-l2 Probability Density Function (PDF)
Aimed at the problem caused by noise influence in the conventional GAM-based alignment method, a novel denoising method combining the l2 -norm with the similarity measure of the PDF
Summary
The Strapdown Inertial Navigation System (SINS) has been applied in various fields and developed rapidly because of its independence and accuracy [1,2,3,4,5]. The self-alignment, as the basis of SINS, is required to obtain the initial attitude accurately and quickly only by utilizing the measurements from the Inertial Measurement Unit (IMU). Due to the harsh environments and measurement interference, achieving high precision initial alignment within a short time is a great challenge. The self-alignment process contains two consecutive phases: coarse alignment and fine alignment [4,6]. The main purpose of coarse alignment is to calculate the rough attitude angles rapidly followed by the Kalman filter-based fine alignment. The precision of coarse alignment determines the accuracy and time of fine alignment. The classical static and quasi-static bases alignment methods can achieve satisfactory results, but they cannot be used for a swaying base due to the disturbance of waves, and so on
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