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UAV Trajectory Tracking via RNN-enhanced IMM-KF with ADS-B Data
Therefore, we employ the ADS-B for UAV trajectory tracking in this work.
UAV Swarm Deployment and Trajectory for 3D Area Coverage via Reinforcement Learning
Unmanned aerial vehicles (UAVs) are recognized as promising technologies for area coverage due to the flexibility and adaptability.
Routing Recovery for UAV Networks with Deliberate Attacks: A Reinforcement Learning based Approach
The unmanned aerial vehicle (UAV) network is popular these years due to its various applications.
Impact of UAVs Equipped with ADS-B on the Civil Aviation Monitoring System
However, due to the limited frequency of ADS-B technique, UAVs equipped with ADS-B devices result in the loss of packets to both UAVs and civil aviation.
Computation Offloading for Uncertain Marine Tasks by Cooperation of UAVs and Vessels
Then, we formulate the studied problem into a Markov decision process, aiming to minimize the total execution time and energy cost.
Recurrent LSTM-based UAV Trajectory Prediction with ADS-B Information
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.
