AN INTEGRATED NAVIGATION METHOD BASED ON AN ADAPTIVE FEDERAL KALMAN FILTER FOR A RICE TRANSPLANTER

In this article, an integrated global positioning system (GPS), inertial navigation system (INS), and visual navigation system (VNS) navigation method based on an adaptive federal Kalman filter (KF) is presented to improve positioning accuracy for a rice transplanter operating in a paddy field. The proposed method used GPS/VNS to aid the INS and reduce the influence of the accumulated error of the INS on navigation accuracy. An adaptive federal KF algorithm was designed to fuse navigation information from different sensors. The information distribution factor of each local filter was obtained adaptively on the basis of its own error covariance matrix. Computer simulation and transplanter tests were conducted to verify the proposed method. Results showed that the proposed method provided accurate and reliable navigation information outputs and achieved better navigation performance compared with single GPS navigation and an integrated method based on a conventional federal KF.

Automated summary

This article presents an integrated navigation method using a federal Kalman filter to improve positioning accuracy for a rice transplanter in a paddy field. The method utilizes GPS, INS, and VNS technologies to reduce accumulated errors and achieve better navigation performance. Results show that the proposed method provides accurate and reliable navigation information outputs compared to conventional methods.