Vehicle Mounted Strapdown Inertial Coarse Alignment Algorithm for LMD-KPCA-WT

Accurate initial alignment is a key step for inertial navigation systems to achieve accurate navigation and localization. However, the coarse alignment process suffers from the problems of long time and low accuracy under the vibration of vehicle engine and people walking, which affects the accuracy and efficiency of initial alignment. To solve this problem, this paper proposes a coarse alignment strategy based on the Local mean decomposition (LMD) and Kernel principal component analysis (KPCA) and Wavelet transform (WT) method, which aims to reduce the interference factors of fiber optic gyroscope and accelerometer in the coarse alignment process, so as to reduce the time required for the coarse alignment process. The aim of the coarse alignment strategy is to reduce the interference factors of the fiber optic gyro and accelerometer during the coarse alignment process, so as to improve the accuracy and efficiency of the initial alignment. Through experimental verification, the accuracy of yaw is improved by 65.09% after adopting this method in the case of serious external interference, which demonstrates the effectiveness and feasibility of this method. This research result is of great significance for improving the accuracy and reliability of inertial navigation systems.