{"ID":6537350,"CreatedAt":"2026-07-14T02:54:43.516908796Z","UpdatedAt":"2026-07-15T03:28:55.185153975Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.11868","arxiv_id":"2607.11868","title":"Detection of sUAS in Urban Environments using Multi-Antenna Micro-Doppler Radar","abstract":"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.","short_abstract":"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 pr...","url_abs":"https://arxiv.org/abs/2607.11868","url_pdf":"https://arxiv.org/pdf/2607.11868v1","authors":"[\"Chamindu Liyanage\",\"Chirantha Kurukulasuriya\",\"Chathuni Wijegunawardana\",\"Wikum Kumara\",\"Chamira U. S. Edussooriya\",\"Arjuna Madanayake\"]","published":"2026-07-13T17:53:58Z","proceeding":"eess.SP","tasks":"[\"eess.SP\"]","methods":"[]","has_code":false}