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An Optimized Adaptive Kalman Filtering Algorithm for Agricultural GPS Data Processing

Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan

Citation:  2018 ASABE Annual International Meeting  1800081.(doi:10.13031/aim.201800081)
Authors:   Yang Wang, Andrew D Balmos, James V Krogmeier, Dennis R Buckmaster
Keywords:   Extended Kalman filter, GPS, Interacting Multiple Model, Kalman filter, kinematic model, machinery.

Abstract. For the past decade, estimating and predicting the geographical position of agricultural machines have become one of the primary research interests in autonomous agricultural vehicles, mapping and precision agriculture. The GPS data usually contain noisy and erroneous position estimates of the machine. Several experiments have devised kinematic models for agricultural machines and applied Kalman filtering technique for correcting the error and improving the accuracy of GPS data (Gomez-Gil, Alonso-Garcia, Gómez-Gil, & Stombaugh, 2011; Gomez-Gil, Ruiz-Gonzalez, Alonso-Garcia, & Gomez-Gil, 2013; Shufeng Han, Qin Zhang, & Hyun Kwon Noh, 2001). However, the existing Kalman filters are not sufficient in distinguishing different machine movements (moving in constant speed, turning or stationary) from the data. As a result, these Kalman filters unintentionally introduce some noisier estimates of machine location. In addition, the applications of these filters offer few insights in understanding and determining machine‘s motion. In this work, the Interaction Multiple Model with Extended Kalman filter (IMM-EKF) algorithm was introduced to compare against the single-model based Kalman filer algorithm with the simulated and collected GPS data set for a combine in a harvesting scenario. The kinematic models for modeling combine motions in harvest as well as the basics of the Kalman filter and IMM algorithm were presented. After the applications of the filters, results from both simulation and collected data suggested that the IMM-EKF algorithm not only offered improvement in estimates over the Kalman filter algorithm but also demonstrated an intuitive understanding of combine motions.

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