Please use this identifier to cite or link to this item:
|Title: ||Incorporation of Neural Network State Estimator for GPS Attitude Determination|
|Authors: ||Dah-Jing Jwo|
Recurrent Neural Network
|Issue Date: ||2018-09-14T01:38:41Z
|Publisher: ||The Journal of Navigation|
|Abstract: ||Abstract: 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.
|Relation: ||57 pp.117–134|
|Appears in Collections:||[通訊與導航工程學系] 期刊論文|
Files in This Item:
All items in NTOUR are protected by copyright, with all rights reserved.