{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T16:17:13Z","timestamp":1785428233019,"version":"3.56.0"},"reference-count":23,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2026,7,18]],"date-time":"2026-07-18T00:00:00Z","timestamp":1784332800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62171141"],"award-info":[{"award-number":["62171141"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"award":["62171141"],"award-info":[{"award-number":["62171141"]}],"id":[{"id":"https:\/\/ror.org\/01h0zpd94","id-type":"ROR","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010226","name":"Guangdong Provincial Department of Education","doi-asserted-by":"crossref","award":["2025ZDJS097"],"award-info":[{"award-number":["2025ZDJS097"]}],"id":[{"id":"10.13039\/501100010226","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Accurate recognition of overlapping low-probability-of-intercept (LPI) radar signals under low signal-to-noise ratio (SNR) conditions remains a challenging problem in electronic reconnaissance. Existing methods often struggle to separate and identify multiple overlapping components in time\u2013frequency representations. To address this problem, we present an enhanced YOLOv8-based model, named LPI-YOLO, for processing short-time Fourier transform (STFT) spectrograms. The proposed method integrates a Coordinate Attention (CA) mechanism into the C2f backbone to improve directional feature representation along time and frequency axes. A Transformer encoder is introduced in deeper layers to model global contextual relationships among overlapping signal components. In addition, lightweight GSConv-based bottlenecks and a simplified SimSPPF module are adopted to reduce computational complexity in terms of parameters and FLOPs. Experiments on a simulated dataset containing six modulation types show that the proposed method achieves competitive performance under low SNR conditions (down to \u221212 dB). Compared with YOLOv8n, the proposed model shows improvements on several challenging modulation classes while maintaining lower computational complexity.<\/jats:p>","DOI":"10.3390\/s26144567","type":"journal-article","created":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T07:54:13Z","timestamp":1784534053000},"page":"4567","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Time\u2013Frequency Image-Based Overlapping LPI Radar Signal Detection and Recognition with LPI-YOLO"],"prefix":"10.3390","volume":"26","author":[{"given":"Hongbin","family":"Pan","sequence":"first","affiliation":[{"name":"School of Automation, Guangdong University of Technology, Guangzhou 510006, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Honglin","family":"Zhuo","sequence":"additional","affiliation":[{"name":"School of Intelligent Manufacturing and Electrical Engineering, Guangzhou Institute of Science and Technology, Guangzhou 510540, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weixuan","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Intelligent Manufacturing and Electrical Engineering, Guangzhou Institute of Science and Technology, Guangzhou 510540, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weiru","family":"Lai","sequence":"additional","affiliation":[{"name":"School of Intelligent Manufacturing and Electrical Engineering, Guangzhou Institute of Science and Technology, Guangzhou 510540, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanzhou","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Automation, Guangdong University of Technology, Guangzhou 510006, China"},{"name":"School of Intelligent Manufacturing and Electrical Engineering, Guangzhou Institute of Science and Technology, Guangzhou 510540, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,7,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1109\/JSTSP.2007.897055","article-title":"Automatic radar waveform recognition","volume":"1","author":"Lunden","year":"2007","journal-title":"IEEE J. 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