Inertial Measurement Unit (IMU) and Global Positioning System (GPS) receivers are
sensors that are widely used for land vehicle navigation. GPS receivers provide
position and/or velocity data to any user on the Earth&rsquo / s surface independent of his
position. Yet, there are some conditions that the receiver encounters difficulties, such
as weather conditions and some blockage problems due to buildings, trees etc. Due to
these difficulties, GPS receivers&rsquo / errors increase. On the other hand, IMU works with
respect to Newton&rsquo / s laws. Thus, in stark contrast with other navigation sensors (i.e.
radar, ultrasonic sensors etc.), it is not corrupted by external signals. Owing to this
feature, IMU is used in almost all navigation applications. However, it has some
disadvantages such as possible alignment errors, computational errors and
instrumentation errors (e.g., bias, scale factor, random noise, nonlinearity etc.).
Therefore, a fusion or integration of GPS and IMU provides a more accurate
navigation data compared to only GPS or only IMU navigation data.
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In this thesis, loosely coupled GPS/IMU integration systems are implemented using
feed forward and feedback configurations. The mechanization equations, which
convert the IMU navigation data (i.e. acceleration and angular velocity components)
with respect to an inertial reference frame to position, velocity and orientation data
with respect to any desired frame, are derived for the geographical frame. In other
words, the mechanization equations convert the IMU data to the Inertial Navigation
System (INS) data. Concerning this conversion, error model of INS is developed
using the perturbation of the mechanization equations and adding the IMU&rsquo / s sensor&rsquo / s
error model to the perturbed mechanization equation. Based on this error model, a
Kalman filter is constructed. Finally, current navigation data is calculated using IMU
data with the help of the mechanization equations. GPS receiver supplies external
measurement data to Kalman filter. Kalman filter estimates the error of INS using the
error mathematical model and current navigation data is updated using Kalman filter
error estimates.
Within the scope of this study, some real experimental tests are carried out using the
software developed as a part of this study. The test results verify that feedback
GPS/INS integration is more accurate and reliable than feed forward GPS/INS. In
addition, some tests are carried out to observe the results when the GPS receiver&rsquo / s
data lost. In these tests also, the feedback GPS/INS integration is observed to have
better performance than the feed forward GPS/INS integration.
| Identifer | oai:union.ndltd.org:METU/oai:etd.lib.metu.edu.tr:http://etd.lib.metu.edu.tr/upload/2/12610327/index.pdf |
| Date | 01 December 2008 |
| Creators | Akcay, Emre Mustafa |
| Contributors | Konukseven, Ilhan Erhan |
| Publisher | METU |
| Source Sets | Middle East Technical Univ. |
| Language | English |
| Detected Language | English |
| Type | M.S. Thesis |
| Format | text/pdf |
| Rights | To liberate the content for METU campus |
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