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arxiveess.SP2026-07-13

Detection of sUAS in Urban Environments using Multi-Antenna Micro-Doppler Radar

Chamindu Liyanage, Chirantha Kurukulasuriya, Chathuni Wijegunawardana, Wikum Kumara, Chamira U. S. Edussooriya, Arjuna Madanayake

Sensing and early detection of small unmanned aerial systems (sUAS) are critically important in modern-day defense. In dense urban and indoor environments, detection becomes extremely challenging due to dense multipath, fading, low-altitude flight, and non-line-of-sight (NLOS) radio-frequency propagation. This paper presents a continuous-wave multiple-input multiple-output radar and a deep learning model for sUAS detection using NLOS signals. The radar operates at 2.47 GHz, and spectral correlation densities derived from rotational micro-Doppler signatures from the rotor blades are used as inputs to the deep learning model. Experimental results demonstrate an overall detection accuracy of $86.11\%$ across a dataset of five drone types, confirming the feasibility of sUAS detection in dense urban environments without direct line-of-sight conditions.

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arxiveess.SP2026-06-26

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arxivcs.NIcs.AIeess.SP2026-07-18

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arxiveess.SPeess.SY2026-07-12

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arxiveess.SP2026-07-09

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