An Extended Kalman Filter for Low-Cost Positioning System in Agricultural Vehicles

Accurate positioning is needed for agricultural vehicles now and in the future. Position is currently needed for mapping, precision farming, auto steering vehicles and for agro robotic solutions. Thus two kinds of errors is defined for GPS receivers such as relative accuracy and the absolute accuracy, which is measured positions to their real position. In this paper to overcome the above said problems such as errors we have proposed Extended Kalman filter by adopting artificial bee colony (ABC) algorithm for dynamic tuning. Hence consider a kinematic tractor model where the system inputs are vehicle speed, front wheel steering angle and the midpoint of the rear wheel axle. Thus Extended Kalman filter an efficient and mathematical algorithm that processes imprecise observation of input data and creates an optimal estimate by providing a prediction model and an observation model is proposed. The first phase is prediction stage and the second phase is the update stage in the system to produce an a posteriori state estimate, by adjusting the previous a priori estimate. Moreover ABC optimization algorithm is used to generate an optimal precise output. Hence the developed model is implemented in the working platform of MATLAB and output is compared with the existing technique to evaluate the performance.