The realtime accurate position update technology of agricultural machinery was one of the most important studies in agricultural machinery automatic navigation system, and it has a very important significance to improve the efficiency of intelligent agricultural production. However, in the field operation of agricultural machinery automatic navigation system, the situations that the number of satellite was unstable, the GPS signal was blocked, and data transmission was wrong would cause low location precision and poor stability. In order to solve the above problems, a two dimensional kinematic model of agricultural machinery was built, and an adaptive extended Kalman filtering algorithm which adjusted the system’s state covariance was carried out based on the integrated navigation system of GNSS/MIMU/DR. The algorithm was used to compute the difference of present estimate and predictive value. When the difference became greater, it showed that the system state had changed greatly, so as to make appropriate adjustments to the system state covariance matrix for better filtering. The algorithm was verified by static test and linear guide rail test to accurately evaluate the accuracy of the integrated navigation and positioning system. The tests indicated that: in static state, the average value deviation of heading was 0.0014°, the maximum deviation was 0.0998°, the standard deviation was 0.0474°, and the position average deviation was 0.0037m, the maximum deviation was 0.0081m, the standard deviation was 0.0010m; in straight rail state, the average value deviation of heading was 0.0245°, the maximum deviation was 0.4324°, the standard deviation was 0.2511°, and the position average deviation was 0.0076m, the maximum deviation was 0.0186m, the standard deviation was 0.0044m. All the different evaluations proved that the adjusted filtering was superior to the traditional filtering, which indicated the necessity and superiority of the proposed algorithm. At the same time, it is proved that the method can satisfy the accuracy and stability requirements of the agricultural machinery navigation and positioning system.