2 research outputs found
A Multi-task Learning Framework for Drone State Identification and Trajectory Prediction
The rise of unmanned aerial vehicle (UAV) operations, as well as the
vulnerability of the UAVs' sensors, has led to the need for proper monitoring
systems for detecting any abnormal behavior of the UAV. This work addresses
this problem by proposing an innovative multi-task learning framework (MLF-ST)
for UAV state identification and trajectory prediction, that aims to optimize
the performance of both tasks simultaneously. A deep neural network with shared
layers to extract features from the input data is employed, utilizing drone
sensor measurements and historical trajectory information. Moreover, a novel
loss function is proposed that combines the two objectives, encouraging the
network to jointly learn the features that are most useful for both tasks. The
proposed MLF-ST framework is evaluated on a large dataset of UAV flights,
illustrating that it is able to outperform various state-of-the-art baseline
techniques in terms of both state identification and trajectory prediction. The
evaluation of the proposed framework, using real-world data, demonstrates that
it can enable applications such as UAV-based surveillance and monitoring, while
also improving the safety and efficiency of UAV operations