A Multi-task Learning Framework for Drone State Identification and Trajectory Prediction

TL;DR

This paper introduces a multi-task deep learning framework that simultaneously identifies UAV states and predicts their trajectories, enhancing UAV monitoring and safety using sensor and trajectory data.

Contribution

The paper presents a novel multi-task learning framework with a shared neural network and a combined loss function for improved UAV state detection and trajectory prediction.

Findings

Outperforms state-of-the-art baseline techniques

Effective on large real-world UAV datasets

Enhances UAV safety and operational efficiency

Abstract

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.