For RF Signal-Based UAV States Recognition, Is Pre-processing Still
Important At The Era Of Deep Learning?
Abstract
Unmanned Aerial Vehicles (UAVs, also called drones) have been widely
deployed in our living environments for a range of applications such as
healthcare, agriculture, and logistics. Despite their unprecedented
advantages, the increased number of UAVs and their growing threats
demand high-performance management and emergency control strategies. To
accurately detect a UAV’s working state including hovering and flying,
data collection from Radio Frequency (RF) signals is a key step of these
strategies and has thus attracted significant research interest. Deep
neural networks (DNNs) have been applied for UAV state detection and
shown promising potentials. While existing work mostly focuses on
improving the DNN structures, we discover that RF signals’
pre-processing before sending them to the classification model is as
important as improving the DNN structures. Experiments on a dataset show
that, after applying proposed pre-processing methods, the 10-time
average accuracy is improved from 46.8% to 91.9%, achieving nearly
50% gain comparing with the benchmark work using the same DNN
structure. This work also outperforms the state-of-the-art CNN models,
confirming the great potentials of data pre-processing for RF-based UAV
state detection.