Absolute Position Estimation with GPS/INS Sensor Fusion
using Kalman Filtering
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Absolute position estimation is nowadays globally and always available due to Global Navigation Satellite Systems, like the Global Position System (GPS). Because of several error sources and other influences, the accuracy is limited or the signal may be blocked completely. Sensor fusion techniques are used to achieve better accuracy and to increase reliability. The most common integration method is an inertial navigation system (INS) aided with GPS measurements, processed by a Kalman filter. In this work two different GPS/INS integration methods are implemented, utilizing inertial sensors, odo...
Absolute position estimation is nowadays globally and always available due to Global Navigation Satellite Systems, like the Global Position System (GPS). Because of several error sources and other influences, the accuracy is limited or the signal may be blocked completely. Sensor fusion techniques are used to achieve better accuracy and to increase reliability. The most common integration method is an inertial navigation system (INS) aided with GPS measurements, processed by a Kalman filter. In this work two different GPS/INS integration methods are implemented, utilizing inertial sensors, odometer data and GPS measurements. The algorithms are designed to keep the computational complexity low and to be suitable for real time applications. The multi-rate property of the sensors is considered as well. Also an inertial sensor error analysis is performed. The results are presented by a MATLAB simulation, based on prior taken measurement data sets.
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