Application of Adaptive Kalman Filtering Algorithms for Smoothing Attitude Solutions with GPS Source Variability
نویسندگان
چکیده
GNSS/INS (Global Navigation Satellite System/Inertial Navigation System) systems have found widespread usage in industry, especially in automated agriculture. Automated agriculture requires high frequency, precise, steady and smooth attitude solutions. This necessitates smooth error patterns in navigation solutions. Given that navigation solutions can be derived from different information sources, combining several information sources can result in smoother attitude solutions relative to when a single information source is utilized. A Multiple Model Kalman Filter (MMKF) is used for this purpose. The results show that the fine attributes of both information sources are adequately captured.
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