The accurate positioning of mobile agricultural equipment in the facilities is the key to the development of intelligent and unmanned agriculture. In view of the positioning problem of agricultural machinery in the facility in a GPS-denied environment, the ultra-wide band (UWB) system is proposed to establish the navigation and positioning system of agricultural machinery in the facility. To improve the positioning accuracy of agricultural machinery equipment, the variational Bayesian Kalman filtering (VBKF) is utilized to smooth the four measured distances of the UWB system to enhance the estimation accuracy of the measured distance, and the centroid positioning algorithm (CPA) is used to calculate the position coordinates of the target node ( TN) . To further improve the positioning accuracy, an improved Taylor series algorithm (TSA) is implemented to optimize the localization results of the VBKF-CPA method. With the mobile robot as the experimental platform, the dynamic and static simulation experiments are conducted indoors by using the UWB positioning system to evaluate the effectiveness of the proposed method. The experimental results show that the VBKFCPA-TSA algorithm can improve the positioning accuracy of the TN and obtain more stable localization results. The mean error on the x-, y-, and z - axis are reduced from 0. 085, 0. 071, and 0. 064 m to 0. 034, 0. 032, and 0. 028 m, and the average estimation accuracies are increased by 60% , 54. 9% , and 56. 3% , respectively. The dynamic localization trajectory of the VBKF-CPA-TSA algorithm is closer to the real movement trajectory, which verifies that the proposed positioning algorithm is able to ameliorate the positioning accuracy of the UWB system in the agricultural facilities and provide a novel method for agricultural mechanical positioning in GPS-denied environment