By Topic

Sensor Calibration Using the Neural Extended Kalman Filter in a Control Loop

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Kathleen A. Kramer ; Department of Engineering, University of San Diego, 5998 Alcalá Park, San Diego, CA, USA, Phone: 619-260-4627, Fax: 619-260-2303, Email: ; Stephen C. Stubberud ; J. Antonio Geremia

Sensor errors can adversely affect the behavior of a control system. When multiple sensors are used, a broken sensor can have its effects minimized by artificially inflating its error covariance. In this paper, a different approach to compensating for sensor errors in a multiple-sensor control system is introduced. The technique, referred to as a neural extended Kalman filter (NEKF), is developed for closed-loop control systems. The NEKF learns on-line from the same residual information used in the state estimator. The improvement in the sensor report is made by the neural network being added to the measurement model. In this work, the NEKF is applied to vehicle trajectory control problem with a position sensor and a velocity sensor.

Published in:

2007 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications

Date of Conference:

27-29 June 2007