Radial basis function Kalman filter for attitude estimation in GPS‐denied environment

Abstract

This study presents a radial basis function (RBF) aided extended Kalman filter (EKF) (namely, novel RBFEKF: NRBFEKF) to improve attitude estimation solutions in GPS-Denied environments. The NRBFEKF has been developed and applied for attitude estimation using only the outputs of strap-down IMU (gyroscopes and accelerometers) and strap-down magnetometer. In general, neural networks have the capability to map input-output relationships of a system without a-priori knowledge about them. A properly designed RBF neural network is able to learn and extract complex relationships given enough training. Furthermore, if there is a platform with inputs, outputs and many sensors, the RBF is able to adapt all the changes of sensors output. The RBFEKF, which is based on EKF aided by RBF network is validated in Matlab environment using simulated trip data and real data acquired during an UAV's trip. The RBFEKF has increased the accuracy of attitude estimation compared to typical EKF. In addition, the RBF is trained to map the vehicle manoeuvre for tuning measurement noise covariance matrix. Simulation results show that estimated measurement noise covariance matrix is closed to the nominal values in cruise flight (stationary phase), while in non-stationary phase the trained RBF neglects measurements from accelerometers, where accelerometer measurement model is not valid during this phase.