  The detection of air-shower events via radio signals requires to develop a
trigger algorithm for a clean discrimination between signal and background
events in order to reduce the data stream coming from false triggers. In this
contribution we will describe an approach to trigger air-shower events on a
single-antenna level as well as performing an online reconstruction of the
shower parameters using neural networks.
