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Apr 19, 2024
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EE 6063 - Integrated Navigation Systems
Theoretical development of positioning and navigation with multiple sensors; basics of estimation theory; classical versus Bayesian estimators; complementary filters, least squares estimators, Kalman filters and particle filters used for navigation purposes; application examples including GPS/INS integration and integration of INS with electro-optical sensors; fault detection and isolation.
Requisites:
Credit Hours: 3
Repeat/Retake Information: May not be retaken.
Lecture/Lab Hours: 3.0 lecture
Grades: Eligible Grades: A-F,WP,WF,WN,FN,AU,I
Learning Outcomes:
- An ability to apply complementary filters, least squares estimators, Kalman filters and particle filters to navigation systems.
- An ability to implement integration methods for GPS/INS and INS with electro-optical sensors.
- An ability to understand classical and Bayesian estimators.
- An ability to understand complementary filters, least squares estimators, Kalman filters and particle filters.
- An ability to understand fault detection and isolation in integrated navigation systems.
- An ability to understand integration methods for GPS/INS and INS with electro-optical sensors.
- An ability to understand the basics of estimation theory.
- An ability to understand the importance of multiple sensor integration for positioning and navigation applications.
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