  With the impact of artificial intelligence on the traditional UAV industry,
autonomous UAV flight has become a current hot research field. Based on the
demand for research on critical technologies for autonomous flying UAVs, this
paper addresses the field of flight state recognition and trajectory prediction
of UAVs. This paper proposes a method to improve the accuracy of UAV trajectory
prediction based on UAV flight state recognition and verifies it using two
prediction models. Firstly, UAV flight data acquisition and data preprocessing
are carried out; secondly, UAV flight trajectory features are extracted based
on data fusion and a UAV flight state recognition model based on PCA-DAGSVM
model is established; finally, two UAV flight trajectory prediction models are
established and the trajectory prediction errors of the two prediction models
are compared and analyzed after flight state recognition. The results show
that: 1) the UAV flight state recognition model based on PCA-DAGSVM has good
recognition effect. 2) compared with the traditional UAV trajectory prediction
model, the prediction model based on flight state recognition can effectively
reduce the prediction error.
