scholarly journals Three-axis attitude determination via Kalman filtering of magnetometer data

1990 ◽  
Vol 13 (3) ◽  
pp. 506-514 ◽  
Author(s):  
Mark L. Psiaki ◽  
Francois Martel ◽  
Parimal K. Pal
Keyword(s):  
Kalman Filtering ◽  
Attitude Determination ◽  
Magnetometer Data

Rocket attitude determination by a Fourier method

1979 ◽  
Vol 57 (5) ◽  
pp. 728-732 ◽  
Author(s):  
D. W. Green ◽  
B. G. Wilson
Keyword(s):  
Optical Sensor ◽  
Additional Data ◽  
Attitude Determination ◽  
Fourier Method ◽  
Angular Motion ◽  
Sounding Rocket ◽  
Free Flight ◽  
Transverse Axis ◽  
Magnetometer Data

It is shown that the parameters completely specifying the angular motion of a symmetrical sounding rocket in torque-free flight may in general be determined from a Fourier-transformed segment of transverse-axis magnetometer data, with limited additional data from an auxiliary optical sensor. The method is simple and rapid, and an example is worked out.

Incorporation of Neural Network State Estimator for GPS Attitude Determination

2004 ◽  
Vol 57 (1) ◽  
pp. 117-134 ◽  
Author(s):  
Dah-Jing Jwo ◽  
Chun-Fan Pai
Keyword(s):  
Neural Network ◽  
Kalman Filter ◽  
Least Squares ◽  
Kalman Filtering ◽  
Attitude Determination ◽  
Least Squares Method ◽  
State Estimator ◽  
Estimation Errors ◽  
Network State ◽  
The Neural Network

The Global Positioning System (GPS) can be employed as a free attitude determination interferometer when carrier phase measurements are utilized. Conventional approaches for the baseline vectors are essentially based on the least-squares or Kalman filtering methods. The raw attitude solutions are inherently noisy if the solutions of baseline vectors are obtained based on the least-squares method. The Kalman filter attempts to minimize the error variance of the estimation errors and will provide the optimal result while it is required that the complete a priori knowledge of both the process noise and measurement noise covariance matrices are available. In this article, a neural network state estimator, which replaces the Kalman filter, will be incorporated into the attitude determination mechanism for estimating the attitude angles from the noisy raw attitude solutions. Employing the neural network estimator improves robustness compared to the Kalman filtering method when uncertainty in noise statistical knowledge exists. Simulation is conducted and a comparative evaluation based on the neural network estimator and Kalman filter is provided.

Multi-Source Information Fusion Based on Kalman Filtering

2015 ◽  
Vol 727-728 ◽  
pp. 863-866
Author(s):  
Meng Meng Zhou ◽  
G.M. Gao ◽  
Hong Bo Yang
Keyword(s):  
Kalman Filtering ◽  
Information Fusion ◽  
Attitude Determination ◽  
Low Frequency ◽  
Fusion Method ◽  
Source Information ◽  
Satellite Platform ◽  
Pointing Accuracy ◽  
Micro Vibration ◽  
Better Than

Thehigh-frequency angular micro-vibration on satellite platform results in theoptical axis pointing decreasing accuracy. The Kalman filtering based on attitudeinformation fusion method is presented to solve this case and improve the pointing accuracy of attitude determination. Thesimulation results indicate that the estimated accuracy of Kalman filteringattitude information fusion method is better than the result only fromconventional low frequency sensor. Accordingly, the attitude information fusionmethod is verified and accuracy.

Attitude determination by kalman filtering

Automatica ◽  
1970 ◽  
Vol 6 (3) ◽  
pp. 419-430 ◽  
Author(s):  
J.L. Farrell
Keyword(s):  
Kalman Filtering ◽  
Attitude Determination
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