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 andapplied for attitude estimation using only the outputs of strap-down IMU (gyroscopes and accelerometers) and strap-downmagnetometer. In general, neural networks have the capability to map input-output relationships of a system without a-prioriknowledge about them. A properly designed RBF neural network is able to learn and extract complex relationships givenenough training. Furthermore, if there is a platform with inputs, outputs and many sensors, the RBF is able to adapt all thechanges of sensors output. The RBFEKF, which is based on EKF aided by RBF network is validated in Matlab environmentusing simulated trip data and real data acquired during an UAV's trip. The RBFEKF has increased the accuracy of attitudeestimation compared to typical EKF. In addition, the RBF is trained to map the vehicle manoeuvre for tuning measurement noisecovariance matrix. Simulation results show that estimated measurement noise covariance matrix is closed to the nominal valuesin 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.