Application of Wavelet Neural Network to Initial Alignment of Strapdown Inertial Navigation System

2008 ◽  
Author(s):  
Di Liu ◽  
Yuming Bo ◽  
Panlong Wu ◽  
Gaopeng Zhao
Keyword(s):  
Neural Network ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Inertial Navigation ◽  
Strapdown Inertial Navigation System ◽  
Wavelet Neural Network ◽  
Initial Alignment ◽  
Strapdown Inertial Navigation

scholarly journals Application of Elman Neural Network Based on Genetic Algorithm in Initial Alignment of SINS for Guided Projectile

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Lei Sun ◽  
Wenjun Yi ◽  
Dandan Yuan ◽  
Jun Guan
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Inertial Navigation ◽  
Strapdown Inertial Navigation System ◽  
Initial Alignment ◽  
Elman Neural Network ◽  
Network Algorithm ◽  
Neural Network Algorithm

The purpose of this paper is to present an in-flight initial alignment method for the guided projectiles, obtained after launching, and utilizing the characteristic of the inertial device of a strapdown inertial navigation system. This method uses an Elman neural network algorithm, optimized by genetic algorithm in the initial alignment calculation. The algorithm is discussed in details and applied to the initial alignment process of the proposed guided projectile. Simulation results show the advantages of the optimized Elman neural network algorithm for the initial alignment problem of the strapdown inertial navigation system. It can not only obtain the same high-precision alignment as the traditional Kalman filter but also improve the real-time performance of the system.

A fast alignment of marine strapdown inertial navigation system based on adaptive unscented Kalman Filter

2020 ◽  
pp. 014233122093429 ◽  
Author(s):  
Hossein Rahimi ◽  
Amir Ali Nikkhah ◽  
Kaveh Hooshmandi
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Unscented Kalman Filter ◽  
Inertial Navigation ◽  
Strapdown Inertial Navigation System ◽  
Initial Alignment ◽  
External Perturbations ◽  
Adaptive Unscented Kalman Filter ◽  
Strapdown Inertial Navigation

This study has presented an efficient adaptive unscented Kalman filter (AUKF) with the new measurement model for the strapdown inertial navigation system (SINS) to improve the initial alignment under the marine mooring conditions. Conventional methods of the accurate alignment in the ship’s SINS usually fail to succeed within an acceptable period of time due to the components of external perturbations caused by the movement of sea waves and wind waves. To speed up convergence, AUKF takes into account the impact of the dynamic acceleration on the filter and its gain adaptively tuned by considering the dynamic scale sensed by accelerometers. This approach considerably improved the corrections of the current residual error on the SINS and decreased the influence due to the external perturbations caused by the ship’s movement. Initial alignment algorithm based on AUKF is designed for large misalignment angles and verified by experimental data. The experimental test results show that the proposed algorithm enhanced the convergence speed of SINS initial alignment compared with some state-of-the-art existing approaches.

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