Application of Extended Kalman Filtering to a Dynamic Laboratory Calibration of an Inertial Navigation System

1973 ◽  
Author(s):  
Frazier J. Hellings
Keyword(s):  
Kalman Filtering ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Inertial Navigation ◽  
Extended Kalman Filtering ◽  
Laboratory Calibration

Integration of Navigation Data

1995 ◽  
Vol 48 (1) ◽  
pp. 114-135 ◽  
Author(s):  
A. Svensson ◽  
J. Holst
Keyword(s):  
Measurement System ◽  
Navigation System ◽  
Measurement Errors ◽  
Inertial Navigation System ◽  
Inertial Navigation ◽  
Measuring Instruments ◽  
Extended Kalman Filtering ◽  
Satellite Navigation System ◽  
Navigation Data ◽  
System A

This article treats integration of navigation data from a variety of sensors in a submarine using extended Kalman filtering in order to improve the accuracy of position, velocity and heading estimates. The problem has been restricted to planar motion. The measurement system consists of an inertial navigation system, a gyro compass, a passive log, an active log and a satellite navigation system. These subsystems are briefly described and models for the measurement errors are given.Four different extended Kalman filters have been tested by computer simulations. The simulations distinctly show that the passive subsystems alone are insufficient to improve the estimate of the position obtained from the inertial navigation system. A log measuring the velocity relative to the ground or a position determining system are needed. The improvement depends on the accuracy of the measuring instruments, the extent of time the instrument can be used and which filter is being used. The most complex filter, which contains fourteen states, eight to describe the motion of the submarine and six to describe the measurement system, including a model of the inertial navigation system, works very well.

The Study of Kalman Filtering Algorithm in the Initial Alignment of Strapdown Inertial Navigation System

2015 ◽  
Vol 740 ◽  
pp. 596-599 ◽  
Author(s):  
Shi Qi An ◽  
Jun Kai Zhang
Keyword(s):  
Kalman Filtering ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Inertial Navigation ◽  
Strapdown Inertial Navigation System ◽  
Initial Alignment ◽  
Filtering Algorithm ◽  
Kalman Filtering Algorithm ◽  
Navigation Accuracy ◽  
Strapdown Inertial Navigation

According to the principle and the method of initial alignment of strapdown inertial navigation system, proposed based on Sage-Husa adaptive kalman filter algorithm. The measured simulation data, compared with those of kalman filtering algorithm, show that the optimized algorithm can optimize the noise estimation, revise accumulated error of strapdown inertial navigation system, and greatly improve the navigation accuracy.

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