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

TL;DR

This paper proposes a recurrent LSTM-based system leveraging ADS-B data for accurate UAV trajectory prediction to enhance air traffic control and collision avoidance.

Contribution

It introduces a novel RLSTM algorithm that effectively utilizes ADS-B information for UAV trajectory prediction, addressing the challenge of dynamic flight paths.

Findings

The RLSTM algorithm achieves low prediction error in simulations.

ADS-B data significantly improves UAV trajectory accuracy.

The system demonstrates robustness in diverse flight scenarios.

Abstract

Recently, unmanned aerial vehicles (UAVs) are gathering increasing attentions from both the academia and industry. The ever-growing number of UAV brings challenges for air traffic control (ATC), and thus trajectory prediction plays a 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 for UAV trajectory prediction depends on acquiring effective location information. Fortunately, the automatic dependent surveillance-broadcast (ADS-B) is an effective technique to help obtain positioning information. It is widely used in the civil aviation aircraft, due to its high data update frequency and low cost of corresponding ground stations construction. Hence, in this work, we consider leveraging ADS-B to help UAV trajectory prediction. However, with the ADS-B information for a UAV, it still lacks efficient mechanism to predict the UAV trajectory. It is noted that the recurrent neural network (RNN) is available for the UAV trajectory prediction, in which the long short-term memory (LSTM) is specialized in dealing with the time-series data. As above, in this work, we design a system of UAV trajectory prediction with the ADS-B information, and propose the recurrent LSTM (RLSTM) based algorithm to achieve the accurate prediction. Finally, extensive simulations are conducted by Python to evaluate the proposed algorithms, and the results show that the average trajectory prediction error is satisfied, which is in line with expectations.