Combined Information Processing from GPS and IMU using Kalman Filtering Algorithm
Inertial Navigation System (INS) and Global Positioning System (GPS) technologies have been widely used in a variety of positioning and navigation applications. Both systems have their unique features and shortcomings. Therefore, the integration of GPS with INS is now critical to overcome each of their drawbacks and to maximize each of their benefits. We present low-cost MEMS inertial and GPS sensor data fusion using a Kalman filter for vehicle distance and velocity estimation. Important parts of the developed Kalman filter that is used to optimally combine data from navigation sensors are described. Then, algorithm performance is illustrated based on an experiment during which the GPS/MEMS-IMU system was installed inside a car. Ill. 16, bibl. 3 (in English; summaries in English, Russian and Lithuanian).
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