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Khan Kalantary, S., mohammadkhani, H. (2021). A Novel Approach for Estimating the Initial Alignment of INS based on the Kalman Filter and an Estimator with Unknown Input. Electronic and Cyber Defense, 17(1), 1-11.
saeed Khan Kalantary; hassan mohammadkhani. "A Novel Approach for Estimating the Initial Alignment of INS based on the Kalman Filter and an Estimator with Unknown Input". Electronic and Cyber Defense, 17, 1, 2021, 1-11.
Khan Kalantary, S., mohammadkhani, H. (2021). 'A Novel Approach for Estimating the Initial Alignment of INS based on the Kalman Filter and an Estimator with Unknown Input', Electronic and Cyber Defense, 17(1), pp. 1-11.
Khan Kalantary, S., mohammadkhani, H. A Novel Approach for Estimating the Initial Alignment of INS based on the Kalman Filter and an Estimator with Unknown Input. Electronic and Cyber Defense, 2021; 17(1): 1-11.

A Novel Approach for Estimating the Initial Alignment of INS based on the Kalman Filter and an Estimator with Unknown Input

مکانیک هوافضا
Volume 17, Issue 1 - Serial Number 63, May 2021, Pages 1-11    PDF (554.87 K)
Document Type: Dynamics, Vibrations, and Control
Authors
saeed Khan Kalantary1; hassan mohammadkhani* 2
1Department of Electrical and Computer Engineering, K. N T University of Technology
2imam hossein university
Receive Date: 18 January 2020,  Revise Date: 21 June 2020,  Accept Date: 23 May 2021 
Abstract
Accurate determination of the initial alignment for an inertial navigation system (INS), plays an important role in the accuracy of the navigation system, because inertial navigation is a blind navigation and is highly dependent on the accuracy of the initial estimation conditions. This paper presents a new method to increase accuracy and convergence speed of the initial alignment in an inertial navigation system. This new method includes two steps to estimate the initial alignment. These steps combine the Kalman method and a filter to estimate the states of a system with an uncertain input. In the first step, the estimations of horizontal misalignment angles are obtained by the Kalman filter. In the second step, the estimation which is produced by the Kalman filter, is used as an input to design an equivalent system for the INS. Finally, a filter is used to estimate the states of a system with an unknown input for the estimation of the azimuth alignment angle. Simulations show that this method not only increases the speed of estimation, but it also produces noticeable accuracy.
Keywords
Inertial Navigation; Initial Alignment; Azimuth Misalignment; Horizontal Misalignment; Kalman filter; State Estimation with Unknown Input
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