Efficient 3-D Tracking and Detection of Multirotor UAVs Using mmWave Radar With Semi-Supervised Learning

Abstract

Small uncrewed aerial vehicles (UAVs) pose security risks to sensitive areas and individuals due to their rapid movement and wide coverage capabilities. Effective monitoring necessitates the deployment of lightweight and energy-efficient surveillance systems. This research introduces an efficient 3-D tracking and detection approach for small UAVs, using millimeter-wave (mmWave) radars and spiking neural networks (SNNs). By capturing micro-Doppler characteristics of UAV movements, it effectively processes low signal-to-noise ratios (SNRs) and uncertain signals. An improved angle estimation algorithm, combining dynamic programming and particle filters, enables real-time 3-D UAV tracking with reduced computational complexity. Then, a simple UAV detection model based on SNN architecture is developed by leveraging UAVs’ position and corresponding Doppler information. Furthermore, a bioinspired semi-supervised method is proposed to facilitate the training of SNNs using a limited number of annotated samples. The effectiveness of the proposed methodology is evaluated under various environmental conditions. Results indicate a significant improvement in tracking computation time efficiency, with the recognition model size reduced to one-tenth of its original size, yet it maintains near-original system performance.