{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,2]],"date-time":"2026-10-02T10:56:15Z","timestamp":1790938575473,"version":"4.1.0"},"reference-count":25,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2026,10,2]],"date-time":"2026-10-02T00:00:00Z","timestamp":1790899200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100006141","name":"Military University of Technology","doi-asserted-by":"publisher","award":["UGB 531-000122-W300-22"],"award-info":[{"award-number":["UGB 531-000122-W300-22"]}],"id":[{"id":"10.13039\/501100006141","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Efficient passive radio monitoring in dense urban areas is obstructed by multipath propagation and NLOS conditions, causing conventional single-site localization methods to suffer from compounding geometric errors and sparse data availability. To address these limitations, this paper introduces a novel hybrid framework that fuses physics-based wave propagation geometry with data-driven machine learning. The proposed methodology utilizes an advanced, Circulant Modified MUSIC (CM-MUSIC) algorithm to reliably resolve highly correlated wave components on a 5-element Uniform Circular Arrays (UCA) antenna. The extracted directional inputs are processed via a backward raytracing engine to generate a continuous spatial likelihood map, which is then filtered by a Convolutional Neural Network (CNN) trained as a sector semantic mask. Empirical field validation using a low-cost Software Defined Radio (SDR) platform capturing analog FM transmissions demonstrated high tracking precision and robust noise immunity under severe spatial constraints, while an ideal sector-classification scenario analysis confirmed that the proposed framework closely converges to the theoretical accuracy limits of the geometric model. This framework provides a scalable, highly cost-effective, and hardware-preserving single-site solution for modern civilian radio spectrum regulatory enforcement.<\/jats:p>","DOI":"10.3390\/s26196261","type":"journal-article","created":{"date-parts":[[2026,10,2]],"date-time":"2026-10-02T10:14:56Z","timestamp":1790936096000},"page":"6261","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Hybrid Raytracing and Deep Learning-Based Approach for Single-Site Localization of Radio Emitters in Urban Environment"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9457-8492","authenticated-orcid":false,"given":"Jaromir","family":"Gajewski","sequence":"first","affiliation":[{"name":"Institute of Communications Systems, Faculty of Electronics, Military University of Technology, gen. Sylwester Kaliski Str. 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