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Recently, unmanned aerial vehicles (UAVs) are gathering increasing attentionsfrom both the academia and industry. The ever-growing number of UAV bringschallenges for air traffic control (ATC), and thus trajectory prediction playsa vital role in ATC, especially for avoiding collisions among UAVs. However,the dynamic flight of UAV aggravates the complexity of trajectory prediction.Different with civil aviation aircrafts, the most intractable difficulty forUAV trajectory prediction depends on acquiring effective location information.Fortunately, the automatic dependent surveillance-broadcast (ADS-B) is aneffective technique to help obtain positioning information. It is widely usedin the civil aviation aircraft, due to its high data update frequency and lowcost of corresponding ground stations construction. Hence, in this work, weconsider leveraging ADS-B to help UAV trajectory prediction. However, with theADS-B information for a UAV, it still lacks efficient mechanism to predict theUAV trajectory. It is noted that the recurrent neural network (RNN) isavailable for the UAV trajectory prediction, in which the long short-termmemory (LSTM) is specialized in dealing with the time-series data. As above, inthis work, we design a system of UAV trajectory prediction with the ADS-Binformation, and propose the recurrent LSTM (RLSTM) based algorithm to achievethe accurate prediction. Finally, extensive simulations are conducted by Pythonto evaluate the proposed algorithms, and the results show that the averagetrajectory prediction error is satisfied, which is in line with expectations.

Recurrent LSTM-based UAV Trajectory Prediction with ADS-B Information