Distributed error correction of EKF algorithm in multi-sensor fusion localization model

In order to solve the problem that the standard extended Kalman filter (EKF) algorithm has large errors in Unmanned Aerial Vehicle (UAV) multi-sensor fusion localization, this paper proposes a multi-sensor fusion localization method based on adaptive error correction EKF algorithm. Firstly, a multi-sensor navigation localization system is constructed by using gyroscopes, acceleration sensors, magnetic sensors and mileage sensors. Then the information detected by the sensor is compared and adjusted, to reduce the influence of error on the estimated value. The nonlinear observation equation is linearized by Taylor, and the normal distribution hypothesis is carried out in two steps of prediction and correction respectively. Finally, the parameters of system noise and measurement noise covariance in EKF are optimized by using the evolutionary iteration mechanism of genetic algorithm. The adaptive degree is obtained according to the absolute value of the difference between the estimated value and the real value of EKF. The individual evaluation results of EKF algorithm parameters are used as the measurement standard for iteration to obtain the optimal value of EKF algorithm parameters. Experimental simulation results show that the improved algorithm proposed has higher real-time localization accuracy and higher robustness than those of the standard EKF algorithm