An adaptive navigation method for a small unmanned aerial rotorcraft under complex environment

• AKF and RBFNN prediction method improve navigation performance during GPS outage.
• AKF based on covariance scaling deals with the process noise in real time.
• A RBFNN is trained on line to construct the projection among input and output.
• Land vehicle test and flight test have confirmed the effectiveness of the method.

Focusing on low navigation performance of small unmanned aerial rotorcraft under complex environment, a composite navigation method combined with adaptive Kalman filtering and radial basis function neural network prediction method is proposed to improve navigation performance during GPS outages. When the GPS signal is available, an adaptive Kalman filter based on covariance scaling is introduced to deal with the process noise in real time. Meanwhile, a radial basis function neural network is trained on line to construct the projection among input (output of the inertial measurement unit, attitude and GPS losing time) and output (position error and velocity error). During GPS outages, the radial basis function neural network can provide high performance error estimation for position and velocity to improve state information. Finally, a land vehicle test and a flight test have confirmed that the proposed method can improve the navigation performance largely under complex environment.