The Classification Algorithm of Nano Targets Based on Millimeter Wave Radar

Abstract

Nanodrones are insect-sized drones that could fly in complex environments and confined spaces, and act as an emerging tool for covert surveillance and intelligence attacks, which would become a potential threat to national security. Radar has the advantage of wide range, all-day, and all-weather detection ability, making it a means of detecting such threat. First, this paper introduces a pertinent multiple-input multiple-output (MIMO) millimeter-wave (MMW) radar system, with the advantages of low cost and high accuracy. It is utilized to detect three targets: nanodrone, small helicopter, and mechanical bird, through which more detailed features can be obtained. Then, the echo data of the three targets are processed and analyzed, and their distinct micro-Doppler characteristics were obtained. Finally, the Radar Transformer target classification network is used to classify and identify the targets. It has been confirmed that desired results could be achieved through the above process.