Data Selection for Training the Neural Fuser Applied to Autonomous UAV Navigation

ABSTRACT

Over the past few years, there has been a steady increase in the use of aircraft vehicles, in particular unmanned aerial vehicles (UAV). UAV navigation is generally controlled by a human pilot. But the challenge for the scientific community is to carry out autonomous navigation. Some solutions have been proposed for the UAV autonomous navigation. Studies indicate as a solution to use data fusion and/or image processing navigation. Kalman Filter (KF) can be employed as a data fuser, but the KF has disadvantages. An alternative to the KF is based on artificial intelligence. Here, the KF is replaced by a self-configured neural network. This work investigates a way to select data for training the neural fuser, based on cross-validation techniques. The results are compared to the data fusion done by a KF.

Keywords:
self-configured neural network; unmanned aerial vehicle; cross-validation; k-fold