Teaching
016832 Navigation and Inertial Systems
016832 Navigation and Inertial Systems
Department of Geo-Information, Technion - Israel Institute of Technology
Department of Geo-Information, Technion - Israel Institute of Technology
The course focuses on inertial navigation systems theory and applications. It is divided into three parts:
The course focuses on inertial navigation systems theory and applications. It is divided into three parts:
1) Inertial sensors: accelerometers and gyroscopes are addressed, including their principles of operation, different technology implementations, and sensor calibration.
1) Inertial sensors: accelerometers and gyroscopes are addressed, including their principles of operation, different technology implementations, and sensor calibration.
2) Navigation mathematics: mathematical foundations, such as reference and coordinate frames, transformation matrixes and their properties, quaternions, and more.
2) Navigation mathematics: mathematical foundations, such as reference and coordinate frames, transformation matrixes and their properties, quaternions, and more.
3) Navigation systems: strapdown navigation kinematics, coarse alignment, and pedestrian dead reckoning.
3) Navigation systems: strapdown navigation kinematics, coarse alignment, and pedestrian dead reckoning.
018827 Advanced Applications of Navigation Systems
018827 Advanced Applications of Navigation Systems
Department of Geo-Information, Technion - Israel Institute of Technology
Department of Geo-Information, Technion - Israel Institute of Technology
The course covers the fundamentals of linear and nonlinear estimation theory, as applied to INS fusion with external sensors and information. The course is divided into three parts:
The course covers the fundamentals of linear and nonlinear estimation theory, as applied to INS fusion with external sensors and information. The course is divided into three parts:
1) Inertial navigation systems: a brief review of basic navigation mathematics fundamentals and INS kinematic equations.
1) Inertial navigation systems: a brief review of basic navigation mathematics fundamentals and INS kinematic equations.
2) Estimation theory: a presentation is given of optimal state estimation in a Kalman framework: linear Kalman filter, extended Kalman filter (EKF), and unscented Kalman filter (UKF). The two implementations derived are a total state and an error state.
2) Estimation theory: a presentation is given of optimal state estimation in a Kalman framework: linear Kalman filter, extended Kalman filter (EKF), and unscented Kalman filter (UKF). The two implementations derived are a total state and an error state.
3) INS fusion with external sensors and information: focusing on fusion between INS and global navigation satellite systems (GNSS), we derive two integration approaches —loosely coupled or tightly coupled architecture — and examine their usage in vehicle navigation and geodetic applications. We also address topics within the INS fine alignment process, indoor navigation fusion approaches, autonomous road vehicle navigation, and more.
3) INS fusion with external sensors and information: focusing on fusion between INS and global navigation satellite systems (GNSS), we derive two integration approaches —loosely coupled or tightly coupled architecture — and examine their usage in vehicle navigation and geodetic applications. We also address topics within the INS fine alignment process, indoor navigation fusion approaches, autonomous road vehicle navigation, and more.
Autonomous Underwater Vehicle Navigation
Autonomous Underwater Vehicle Navigation
The Hatter Department of Marine Technologies, University of Haifa
The Hatter Department of Marine Technologies, University of Haifa
The course focuses on autonomous underwater vehicle navigation. After a brief review of inertial navigation theory and optimal state estimation in a Kalman framework, external sensors that are commonly used to aid the INS in AUV navigation are presented. We derive relevant theory, principles of operations, error sources and more, for Doppler velocity log (DVL), magnetometer, terrain-referenced navigation, and model-aided INS. Finally, we address several fusion approaches between the sensors and the INS.
The course focuses on autonomous underwater vehicle navigation. After a brief review of inertial navigation theory and optimal state estimation in a Kalman framework, external sensors that are commonly used to aid the INS in AUV navigation are presented. We derive relevant theory, principles of operations, error sources and more, for Doppler velocity log (DVL), magnetometer, terrain-referenced navigation, and model-aided INS. Finally, we address several fusion approaches between the sensors and the INS.