Measurement and analysis of a field area based on an adaptive Kalman filter

Abstract

This paper proposed the use of an adaptive Kalman filter (AFK) to improve Global Positioning System (GPS) positioning accuracy to measure a tractor operational area. First, we used MATLAB to identify the operation trajectory. Then, we used different colors to show the area of operation. Finally, we used an image-processing method to calculate the effective operational area, actual operational area, and repeat and omission rates. We used these rates to evaluate the tractor efficiency. The experiment indicated that the Kalman filter improved the accuracy of GPS single-point positioning. To test the GPS area-measurement precision, field area measurements were taken. We used GPS to measure standard figures and some irregular figures. The results indicate that the area measurement relative error was 2.09%. The measurement accuracy increased with the increasing measurement area. The field test results indicated that the most efficient farming method was alternative tillage and the second most efficient was spindle tillage. The omission rate under back tillage was highest and its operational efficiency was lowest.