The estimation and control of terrestrial inertial navigation system errors due to vertical deflections

1968 ◽  
Vol 13 (4) ◽  
pp. 329-338 ◽  
Author(s):  
R. Nash
Keyword(s):  
Navigation System ◽  
Inertial Navigation System ◽  
Inertial Navigation ◽  
Estimation And Control ◽  
And Control ◽  
System Errors

scholarly journals Synergetic Concept of Algorithms Autonomous Inertial Navigation Systems

2012 ◽  
Vol 19 (2) ◽  
pp. 185-197
Author(s):  
Sergey Yakushin
Keyword(s):  
Positive Feedback ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Inertial Navigation ◽  
Feedback Effect ◽  
Navigation Systems ◽  
Inertial Navigation Systems ◽  
System Errors ◽  
The Impact ◽  
Positive Feedback Effect

Abstract Errors of INS output parameters lead to a positive feedback effect of errors and eventually to an even more dramatic increase in system errors. To reduce the impact of this problem on the error output parameters of INS, in this paper, we propose and study a new concept of constructing algorithms for autonomous INS, which is called as synergetic concept. In the paper the synergetic concept of inertial system’s algorithm is presented and investigated by implementing its into strapdown inertial navigation system (SDINS).

Integration of the inertial navigation system with consecutive images of a camera by relative position and attitude updating

2019 ◽  
Vol 233 (15) ◽  
pp. 5592-5605
Author(s):  
Habib Ghanbarpour Asl ◽  
Abbas Dehghani Firouzabadi
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Unscented Kalman Filter ◽  
Inertial Navigation ◽  
New Method ◽  
Measurement Unit ◽  
Measurement Equation ◽  
Implicit Measurement ◽  
System Errors

This paper introduces a new method for improving the inertial navigation system errors using information provided by the camera. An unscented Kalman filter is used for integrating the inertial measurement unit data with the features’ constraints extracted from the camera’s image. The constraints, in our approach, comprise epipolar geometry of two consecutive images with more than 65% coverage. Tracking down a known feature in two consecutive images results in emergence of stochastic epipolar constraint. It emerges in the form of an implicit measurement equation of the Kalman filter. Correctly matching features of the two images is necessary for reducing the navigation system errors because they act as external information for the inertial navigation system. A new method has been presented in this study based on the covariance analysis of the matched feature rays’ intersection points on the ground, which sieves the false matched features. Then, the inertial navigation system and matched feature information is integrated through the unscented Kalman filter filter and the states of the vehicle (attitude, position, and velocity) are corrected according to the last image. In this paper, the relative navigation parameters against the absolute one have been corrected. To avoid increasing dimensions of the covariance matrix, sequential updating procedure is used in the measurement equation. The simulation results show good performance of the proposed algorithm, which can be easily utilized for real flights.

Sign in / Sign up

Export Citation Format

Share Document