Course Catalog 2009-2010 Postgraduate Basic Pori International Postgraduate Open University |Degrees|     |Study blocks|     |Courses|

Person responsible

Pavel Davidson, Jussi Collin

Implementations

	Lecture times and places	Target group recommended to
Implementation 1	Per 5 : Tuesday 14 - 16, TB224

Requirements

Exam

Principles and baselines related to teaching and learning

-

Learning outcomes

This course is designed to give students an understanding of the basic principles of inertial navigation, inertial sensors and implementation of Kalman filtering to fusion of INS and aiding sensors.

Content

Content	Core content	Complementary knowledge	Specialist knowledge
1.	Inertial navigation principles, frames, errors. Level plane 2-D dead reckoning navigator. Error propagation, block diagrams. Gimballed vs strapdown. Spherical Plane 3-D Inertial Navigator. Rotation/corrections. Surface curvature/corrections. Centripetal/Coriolis corrections. Rotating Frames. North-Up navigator mechanization/error models.	Coriolis Law. Mechanization and block diagrams. Error sources.	Sensor selection.
2.	Earth shape. Coordinate frames. Acceleration sensing. Navigation mechanization. Error models. Augmentation. Pendulous reference. Schuler pendulum. Schuler oscillations. Altitude instability.
3.	Gimballed/Strapdown Error Formulation. Psi equation. Gimballed/Strapdown Error Propagation. Position, velocity and attitude error diff eqns. IMU error budgets: MEMS,FOG,RLG etc.	Geometrical, physical, mathematical definition of angular position error and attitude error.
4.	Micromachined (MEMS) Accelerometers & Gyroscopes Process technology. Errors/resolution/noise. Accelerometer and gyro error calibration Instrument errors: bias, thermal bias, scale factor, misalignments, etc. Accel and gyro residual errors. Stochastic models.	MEMS Accl and gyro principle, design, fabrication. Open vs closed loop. Multi-position rotation calibration for accel and gyro. Instrument compensation.	Manufacturers/designs/specs.
5.	Leveling and Gyrocompassing Physical/analytical self leveling. Coarse alignment. Gravity/Earth rate errors. Fine leveling. Gyrocompassing. Gyro Bias. Fundamental limits.
6.	Simple Multisensor Kalman Integration. Aiding sensors. Classical error compensation. Classical vs Kalman. Examples of Kalman filter implementation to Radar/Inertial simple example. Error models.	Optimal mechanization. Close vs Open. Mixing GPS/INS. Benefits. Pitfalls. Cascaded vs Integrated. Open vs closed loop. Coupling type: loose, tight, full, deep.	Inertial sensor error augmentation. Observable difference.

Evaluation criteria for the course

Final exam

Assessment scale:

Numerical evaluation scale (1-5) will be used on the course

Study material

Type	Name	Author	ISBN	URL	Edition, availability, ...	Examination material	Language
Book	Strapdown Inertial Navigation Technology	D. H. Tittertton	1 56347 693 2		Second Edition		English
Lecture slides							English

Prerequisites

Correspondence of content

There is no equivalence with any other courses

More precise information per implementation

	Description	Methods of instruction	Implementation
Implementation 1		Lectures Excercises	Contact teaching: 0 % Distance learning: 0 % Self-directed learning: 0 %

Last modified	11.01.2010
Modifier	Pavel Davidson