Appendices
Copyright © 2015 Coda Octopus Products Ltd
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F180R MOTION Sensor User and Reference Guide
6.2.2 Inertial Navigation System (INS)
An inertial navigation system determines the position, attitude and dynamics of a vessel by
measuring and relating the forces acting upon the object in three dimensions using an inertial
measurement unit (IMU). In order to produce the position and velocity data, an inertial
navigation system must perform the following operations:
1. Measure the angular motion of the vessel using an orthogonal set of gyroscopic sensors.
2. Measure the forces acting on the vessel using an orthogonal set of accelerometers.
3. Relate the forces to a given local reference frame using the knowledge of the vessel
attitude given by the gyroscopes.
4. Account for any external forces acting upon the vessel such as the Earth gravitational
force and the Coriolis force.
5. Integrate the force measurement presented in the local reference frame to obtain
estimates of the velocity and the relative position of the object.
The pros and cons of an inertial navigation system can be broken into the following
categories:
High short term accuracy
Low noise level
High data rate
Autonomous navigation solution not
depending on any external information
Relative position updates
Low long term accuracy (high drift)
6.2.3 GPS-INS Integration
By integrating a GPS system with an INS system, one takes advantage of the complimentary
attributes of the two navigation systems in order to yield a position solution more stable than
the one produced by either system in isolation. As discussed above, the different strengths
and weaknesses of the two systems make them ideal partners in an integrated system.
Kalman Filter
A Kalman filter is the numeric tool making the fusion of GPS and IMU information possible. It
is a mathematical model relating the noisy and possibly incomplete IMU and GPS
measurement variables in order to present estimates for the position, heading, attitude as
well as their associated errors.
The F180R System uses a Kalman filter, which in addition to the position, attitude and velocity
solves for the following variables: position error (north, east, down), velocity error (north,
east, down), heading error, pitch error, roll error, gyro bias (X, Y, Z), gyro scale factor (X, Y, Z),
accelerometer bias (X, Y, Z), GPS antenna mount offset (X, Y, Z) and GPS antenna mount
orientation (heading, pitch).
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