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Radioengineering

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Proceedings of Czech and Slovak Technical Universities

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December 2026, Volume 35, Number 4 [DOI: 10.13164/re.2026-4]

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A. G. Pakfiliz [references] [full-text] [DOI: 10.13164/re.2026.0425] [Download Citations]
An S-Band Radar Method for Early Detection of Stealth Fighters in Air-to-Air Combat

Stealth aircrafts reduce the detection ranges of traditional radar systems primarily by lowering Radar-Cross-Section (RCS) optimized for high-frequency X-band threat radars. To evaluate multi-band detection potential under airframe constraints, this study analyzes the integration of a wing-leading-edge S-band radar array to complement conventional nose-mounted X-band systems. Using link-budget modeling and simulation, detection performance across L-, S-, and X-bands is compared for low-observable target profiles under clear and jamming conditions. The analytical findings indicate that while nose-mounted X-band radars maintain high angular resolution, S- and L-band arrays provide extended burn-through and crossover detection ranges against stealth targets. Specifically, a fixed-aperture S-band wing-mounted configuration offers a balanced trade-off between element density and aperture constraints, providing actionable theoretical insights for future multi-band radar architectures in air-to-air scenarios.

  1. LORELL, M. A., LEVAUX, H. P. The 1970s to the 1990s: The stealth revolution. In The Cutting Edge: A Half Century of U.S. Fighter Aircraft R&D. 1st ed. RAND Corporation. 1998. ISBN: 08330-2607-0
  2. ZIKIDIS, K., SKONDRAS, A., TOKAS, C. Low observable principles, stealth aircraft and anti-stealth technologies. Journal of Computations & Modelling, 2014, vol. 4, no. 1, p. 129–165. ISSN: 1792-7625 (print), 1792-8850 (online)
  3. LYNCH, D. Introduction to RF Stealth. Raleigh (NC): Scitech Publishing Inc., 2004. DOI: 10.1049/SBRA120E
  4. ZOHURI, B. Radar Energy Warfare and the Challenges of Stealth Technology. Berlin: Springer, 2020. DOI: 10.1007/978-3-03040619-6
  5. KNOTT, E. F., SCHAEFFER, J. F., TULLEY, M. T. Radar Cross Section. 2nd ed. SciTech Publishing, 2004. ISBN-10: 1891121251
  6. STIMSON, G. W. Introduction to Airborne Radar. 2nd ed. SciTech Publishing, 1998. ISBN-10: 1891121014
  7. XU, P., WANG, Y., XU, X., et al. Structural‐electromagnetic‐thermal coupling technology for active phased array antenna. International Journal of Antennas and Propagation, 2023, no. 1, p. 1–36. DOI: 10.1155/2023/2843443
  8. ZHANG, J. A novel intelligent radar detection network. Journal of Physics: Conference Series. IOP Publishing, 2022, p. 1–5. DOI: 10.1088/1742-6596/2181/1/012056
  9. CHEN, W., DUAN, Y., MA, D., et al. Neural network–enabled accelerated discovery of multifunctional metamaterials for adaptive multispectral stealth applications. Materials Today Physics, 2025, 52, p. 1–11. DOI: 10.1016/j.mtphys.2025.101696
  10. YAN, L., GAO, Y., COLONE, F., et al. RIS-aided radar detection architectures with application to low-RCS targets. arXiv preprint arXiv:2601.10846, 2026, p. 1–17. DOI: 10.48550/arXiv.2601.10846
  11. BOHREN, C. F., HUFFMAN, D. R. Absorption and Scattering of Light by Small Particles. John Wiley & Sons, 2008. ISBN: 9783527618163
  12. RICHARDSON, D. Stealth Warplanes. Zenith Imprint, 2001. ISBN-10: 0760310513
  13. PELOSI, M. J., KOPP, C. A. Preliminary assessment of specular radar cross section performance in the Sukhoi T-50 prototype. Air Power Australia Analyses, 2012. [Online] Cited 2026-03-22. Available at: https://www.ausairpower.net/APA-2012-03.html
  14. BOLKCOM, C. F-35 Lightning II Joint Strike Fighter (JSF) Program: Background, status, and issues. 2009. [Online] Cited 2026-03-22. Available at: https://apps.dtic.mil/sti/tr/pdf/ADA494859.pdf
  15. DAVIDENKO, A. N., STRELEC, M. J., GAVRIKOV, A. J., et al. Multifunctional aircraft of decreased radar signature (in Russian). Russia patent RU2502643C2. 2013-12-27. [Online] Cited 2026-03-22. Available at: https://patents.google.com/patent/RU2502643C2/en
  16. PAKFILIZ, A. G. Increasing self-protection jammer efficiency using radar cross section adaptation. Computers & Electrical Engineering, 2022, vol. 98, p. 1–19. DOI: 10.1016/j.compeleceng.2021.107635
  17. ZIKIDIS, K. C. Early warning against stealth aircraft, missiles and unmanned aerial vehicles. In Karampelas, P., Bourlai, T. (eds.). Surveillance in Action: Technologies for Civilian, Military and Cyber Surveillance. Cham: Springer International Publishing, 2017, p. 195–216. DOI: 10.1007/978-3-319-68533-5_10
  18. MAST TECHNOLOGIES, San Diego, California. MR11-0010-00 Tuned Frequency Absorber, Silicone. Datasheet. 2018. [Online] Cited 2026-03-22. Available at: https://www.masttechnologies.com/wp-content/uploads/2016/04/MR11-0010-00-Tech-Data-Sheet.pdf
  19. MAST TECHNOLOGIES, San Diego, California. MR31-0013-00 Surface Wave Absorber, Silicone. Datasheet. 2018. [Online] Cited 2026-03-22. Available at: https://www.masttechnologies.com/wp-content/uploads/2016/04/MR31-0003-00-Tech-Data-Sheet.pdf
  20. MWT MATERIALS, Inc., New Jersey. MF-500-Urethane. Datasheet. 2020. [Online] Cited 2026-03-22. Available at: https://www.mwtmaterials.com/wp-content/uploads/2020/04/MWT-TD-MF-500-Urethane-Ver-4.2.pdf
  21. ABRAMS, A. J-20 Mighty Dragon: Asia’s First Stealth Fighter in the Era of China’s Military Rise. Helion and Company, 2024. ISBN-10: 1804515604
  22. VUVUZELA. F-35 vs J-20 vs Su-57 RCS. Aircraft 101, Jan. 20, 2020. [Online] Cited 2026-03-22. Available at: basicsaboutaerodynamicsandavionics.wordpress.com.
  23. GAITANAKIS, G. K., LIMNAIOS, G., ZIKIDIS, K. C. On the use of AESA (active electronically scanned array) radar and IRST (infrared search & track) system to detect and track low observable threats. In MATEC Web of Conferences, 2019, vol. 304, p. 1–8. DOI: 10.1051/matecconf/201930404001
  24. MAHAFZA, B. R. Radar Signal Analysis and Processing using MATLAB. Chapman and Hall/CRC, 2016. ISBN: 9780429149337
  25. LOCKHEED MARTIN CORPORATION. T.O. GR1F-16CJ-1. Flight Manual HAF Series Aircraft F-16C/D. 2002 [Online] Cited 2026-03-22. Available at: https://info.publicintelligence.net/HAF-F16.pdf
  26. MACOM. CGHV14800; 800W; 960-1400 MHz GaN HEMT for L-Band Radar Systems. Datasheet, 2022. [Online] Cited 2026-03-22. Available at: https://cdn.macom.com/datasheets/CGHV14800.pdf
  27. RFHIC. RRP27311K0-22; 1kW S-band Pulsed Pallet Amplifier. Datasheet, 2026. [Online] Cited 2026-03-22. Available at: https://rfhic.com/wp-content/uploads/2026/03/RRP27311K0-22_V0.2-1kW-2.7-_-3.1GHz-GaN-Solid-State-Power-Amplifier-SSPA.datasheet-download.pdf
  28. RICHARDS, M. A. Fundamentals of Radar Signal Processing. 1st ed. New York: McGraw-Hill, 2005. ISBN: 9780071444743
  29. KHALILPOUR, J., RANJBAR, J., KARAMI, P. A novel algorithm in a linear phased array system for side lobe and grating lobe level reduction with large element spacing. Analog Integrated Circuits and Signal Processing, 2020, vol. 104, no. 3, p. 265–275. DOI: 10.1007/s10470-020-01612-1
  30. SKOLNIK, M. I. Radar Handbook. 3rd ed. McGraw-Hill, 2008. ISBN: 9780071485470
  31. BALANIS, C. A. Antenna Theory: Analysis and Design. 4th ed. John Wiley & Sons, 2016. ISBN-10: 1118642066
  32. ALBERHSEIM, W. J. A closed-form approximation to Robertson's detection characteristics. Proceedings of the IEEE, 1981, vol. 69, no. 7, p. 839–839. DOI: 10.1109/PROC.1981.12082
  33. MIT LINCOLN LABORATORY. Introduction to Radar Systems. 2020 [Online] Cited 2026-03-22. Available at: https://www.ll.mit.edu/sites/default/files/outreach/doc/2018-07/lecture%203.pdf
  34. PAKFILIZ, A. G. Self-Protection Jammer Systems. Artech House, 2024. ISBN-10: 1685690114
  35. LU, X., HUANG, J., WU, Y., et al. Influence of stealth aircraft dynamic RCS peak on radar detection probability. Chinese Journal of Aeronautics, 2023, vol. 36, no. 3, p. 137–145. DOI: 10.1016/j.cja.2022.04.009
  36. LU, S., MENG, Z., HUANG, J., et al. Study on quantum radar detection probability based on flying-wing stealth aircraft. Sensors, 2022, vol. 22, p. 1–34. DOI: 10.3390/s22165944
  37. ZHANG, B., GAO, Y., WANG, J., et al. Design and analysis of a multispectral compatible stealth metamaterial for enhanced stealth across the laser, infrared, and radar spectral bands. Optics & Laser Technology, 2025, vol. 187, p. 1-9, 10.1016/j.optlastec.2025.112902

Keywords: Stealth fighters, air-to-air radar, radar absorbent material, S-band radar

Q. Guo, H. D. Zhao, W. J. Wang, W. K. Zhang [references] [full-text] [DOI: 10.13164/re.2026.0442] [Download Citations]
SAGA-YOLO: A High-Accuracy Detector for SAR Aircraft in Complex Environments

Synthetic Aperture Radar (SAR), utilizing its ability to actively transmit microwave signals, is widely used for military aircraft target reconnaissance and aviation safety monitoring. However, the complex background of ground-based aircraft targets, combined with the coherent imaging characteristics of SAR, results in speckle noise, which increases the difficulty of detection. To address this, we propose a high-accuracy SAR aircraft detection model, SAR-Adaptive Gated-Attention You Only Look Once (SAGA-YOLO). It includes the following three improvements: Firstly, the model constructs a robust gated convolutional backbone network that adaptively filters out noise and irrelevant clutter by dynamically adjusting feature channels, thereby extracting more robust semantic features of aircraft. Secondly, an attention module, C2-CAS, is introduced at the end of the backbone network to refocus features on the most distinctive scattering regions of the aircraft, enhancing its key structural features. Finally, a feature fusion network, Slim-neck, is introduced at the neck to improve the model’s ability to extract and fuse features of targets at different scales. Experimental results on the SAR-AIRcraft-1.0 and SADD aircraft datasets demonstrate that SAGA-YOLO achieves superior performance; compared to the baseline YOLO11, SAGA-YOLO improves mAP50 and mAP50-95 by 3.0% and 5.8% respectively on SAR-AIRcraft-1.0, and by 0.6% and 18.3% on SADD.

  1. YAO, W. Q., CAO, L., TIAN, S., et al. Dynamic frequency-aware feature enhancement network for synthetic aperture radar target detection (in Chinese). Laser and Optoelectronics Progress, 2026, vol. 63, no. 6, p. 1–15. DOI: 10.3788/LOP252076
  2. WANG, C., DING, H., ZHAO, M. Aircraft target detection algorithm for SAR images based on improved YOLOv8. In 2024 30th International Conference on Mechatronics and Machine Vision in Practice (M2VIP). Leeds (UK), 2024, p. 1–6. DOI: 10.1109/M2VIP62491.2024.10746028
  3. KE, Q., WU, Y., ZHAO, W., et al. SFG-Net: A scattering feature guidance network for oriented aircraft detection in SAR images. Remote Sensing, 2025, vol. 17, no. 7, p. 1–25. DOI: 10.3390/rs17071193
  4. LIU, W., WANG, H., DUAN, J., et al. Complex-scene SAR aircraft recognition combining attention mechanism and inner convolution operator. Sensors, 2025, vol. 25, no. 15, p. 1–15. DOI: 10.3390/s25154749
  5. WANG, X., XU, W., HUANG, P., et al. MSADNet: Multistage SAR aircraft target detection network. IEEE Geoscience and Remote Sensing Letters, 2025, vol. 22, p. 1–5. DOI: 10.1109/LGRS.2024.3521650
  6. WANG, C. S., LI, Y. Z., SHANG, Y. Z., et al. An anchor-free method for aircraft detection in SAR images based on density map. In 2024 IEEE International Conference on Signal, Information and Data Processing (ICSIDP). Zhuhai (China), 2024, p. 1–6. DOI: 10.1109/ICSIDP62679.2024.10868269
  7. FINN, H. M., JOHNSON, R. S. Adaptive detection mode with threshold control as a function of spatially sampled clutter-level estimates. RCA Review, 1968, vol. 29, no. 3, p. 414–465. Available at: https://www.worldradiohistory.com/ARCHIVE-RCA/RCA-Review/RCA-Review-1968-09.pdf
  8. ROHLING, H. Radar CFAR thresholding in clutter and multiple target situations. IEEE Transactions on Aerospace and Electronic Systems, 1983, vol. AES-19, no. 4, p. 608–621. DOI: 10.1109/TAES.1983.309350
  9. NOVAK, L. M., OWIRKA, G. J., NETISHEN, C. M. Performance of a high-resolution polarimetric SAR automatic target recognition system. The Lincoln Laboratory Journal, 1993, vol. 6, no. 1, p. 11–24. Available at: https://archive.ll.mit.edu/publications/journal/pdf/vol06_no1/6.1.2.polarimetricsar.pdf
  10. APATEAN, A., ROGOZAN, A., BENSRHAIR, A. SVM-based obstacle classification in visible and infrared images. In 2009 17th European Signal Processing Conference. Glasgow (Scotland), 2009, p. 293–297. Available at: https://www.eurasip.org/Proceedings/Eusipco/Eusipco2009/contents/papers/1569192405.pdf
  11. FREUND, Y., SCHAPIRE, R. E. A desicion-theoretic generalization of on-line learning and an application to boosting. In Computational Learning Theory. Berlin (Germany), 1995, p. 23 to 37. DOI: 10.1007/3-540-59119-2_166
  12. REN, S., HE, K., GIRSHICK, R., et al. Faster R-CNN: towards real time object detection with region proposal networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, vol. 39, no. 6, p. 1137–1149. DOI: 10.1109/TPAMI.2016.2577031
  13. CAI, Z., VASCONCELOS, N. Cascade R-CNN: high quality object detection and instance segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021, vol. 43, no. 5, p. 1483 to 1498. DOI: 10.1109/TPAMI.2019.2956516
  14. REDMON, J., DIVVALA, S., GIRSHICK, R., et al. You only look once: Unified, real-time object detection. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas (USA), 2016, p. 779–788. DOI: 10.1109/CVPR.2016.91
  15. LIU, W., ANGUELOV, D., ERHAN, D., et al. SSD: Single shot multiBox detector. In Computer Vision – ECCV 2016. Amsterdam (Netherlands), 2016, p. 21–37. DOI: 10.1007/978-3-319-46448-0_2
  16. YANG, Y. J., SINGHA, S., MAYERLE, R. A deep learning based oil spill detector using Sentinel-1 SAR imagery. International Journal of Remote Sensing, 2022, vol. 43, no. 11, p. 4287–4314. DOI: 10.1080/01431161.2022.2109445
  17. SHEN, Y. F., GAO, Q. DS-YOLO: A SAR ship detection model for dense small targets. Radioengineering, 2025, vol. 34, no. 3, p. 407 to 421. DOI: 10.13164/re.2025.0407
  18. MO, H., WU, J., XIA, H., et al. A lightweight, efficient, adaptive design of YOLOv5 for enhanced SAR ship detection. Remote Sensing Letters, 2025, vol. 16, no. 5, p. 549–559. DOI: 10.1080/2150704X.2025.2480761
  19. ZHU, L. J., CHEN, J. L., CHEN, J. Y., et al. DGSP-YOLO: A novel high-precision synthetic aperture radar (SAR) ship detection model. IEEE Access, 2024, vol. 12, p. 167919–167933. DOI: 10.1109/ACCESS.2024.3497314
  20. ROCHA, R. D. L., FIGUEIREDO, F. A. P. D. Enhancing YOLO based SAR ship detection with attention mechanisms. Remote Sensing, 2025, vol. 17, no. 18, p. 1–32. DOI: 10.3390/rs17183170
  21. WANG, L. G., NING, Y. X., WANG, G. Q., et al. AMDC-YOLO: An adaptive multi-dimensional dynamic convolution approach for aircraft target detection. In 2025 10th International Conference on Computer and Communication System (ICCCS). Chengdu (China), 2025, p. 278–283. DOI: 10.1109/ICCCS65393.2025.11069621
  22. BAYRAKTAR, I., BAKIRCI, M. Attention-augmented YOLO11 for high-precision aircraft detection in synthetic aperture radar imagery. In 2025 27th International Conference on Digital Signal Processing and Its Applications (DSPA). Moscow (Russia), 2025, p. 1–6. DOI: 10.1109/DSPA64310.2025.10977903
  23. YU, W. H., WANG, X. C. MambaOut: Do we really need mamba for vision? In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Nashville (USA), 2025, p. 4484–4496. DOI: 10.1109/CVPR52734.2025.00423
  24. ZHANG, T. F., LI, L., ZHOU, Y., et al. CAS-ViT: Convolutional additive self-attention vision transformers for efficient mobile applications. arXiv preprint, 2024. DOI: 10.48550/arXiv.2408.03703
  25. SALAZAR, A., SAFONT, G., VERGARA, L., et al. Graph regularization methods in soft detector fusion. IEEE Access, 2023, vol. 11, p. 144747–144759. DOI: 10.1109/ACCESS.2023.3344776
  26. LI, H., LI, J., WEI, H., et al. Slim-neck by GSConv: A better design paradigm of detector architectures for autonomous vehicles. arXiv preprint, 2022. DOI: 10.48550/arXiv.2206.02424
  27. WANG, Z. R., KANG, Y. Z., ZENG, X., et al. SAR-AIRcraft-1.0: High-resolution SAR aircraft detection and recognition dataset. Journal of Radars, 2023, vol. 12, no. 4, p. 906–922. DOI: 10.12000/JR23043
  28. ZHANG, P., XU, H., TIAN, T., et al. SEFEPNet: Scale expansion and feature enhancement pyramid network for SAR aircraft detection with small sample dataset. IEEE Journal of Selected Topics in Applied Earth Observation and Remote Sensing, 2022, vol. 15, p. 3365–3375. DOI: 10.1109/JSTARS.2022.3169339
  29. OUYANG, D. L., HE, S., ZHANG, G. Z., et al. Efficient multi-scale attention module with cross-spatial learning. In 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Rhodes Island (Greece), 2023, p. 1–5. DOI: 10.1109/ICASSP49357.2023.10096516
  30. JIANG, M., ZENG, P., WANG, K., et al. FECAM: Frequency enhanced channel attention mechanism for time series forecasting. 2022, arXiv: arXiv:2212.01209, p. 1–11. DOI: 10.48550/arXiv.2212.01209
  31. XIA, Z., PAN, X., SONG, S., et al. Vision transformer with deformable attention. arXiv, 2022, arXiv:2201.00520, p. 1–12. DOI: 10.48550/arXiv.2201.00520
  32. WANG, Y., LI, Y., WANG, G., et al. Multi-scale attention network for single image super-resolution. arXiv, 2024, arXiv:2209.14145, p. 1–11. DOI: 10.48550/arXiv.2209.14145
  33. YU, W. H., LUO, M., ZHOU, P., et al. MetaFormer is actually what you need for vision. arXiv: 2022, arXiv:2111.11418, p. 1–17. DOI: 10.48550/arXiv.2111.11418
  34. PENG, Y. P., CHEN, D. Z., SONKA, M. U-Net V2: Rethinking the skip connections of U-Net for medical image segmentation. In 2025 IEEE 22nd International Symposium on Biomedical Imaging (ISBI). Houston (USA), 2025, p. 1–5. DOI: 10.1109/ISBI60581.2025.10980742
  35. DING, X. H., ZHANG, Y. Y., GE, Y. X., et al. UniRepLKNet: A universal perception large-kernel ConvNet for audio, video, point cloud, time-series and image recognition. arXiv: 2024, arXiv:2311.15599, p. 1–16. DOI: 10.48550/arXiv.2311.15599
  36. LIU, Z., LIN, Y. T., CAO, Y. et al. Swin transformer: Hierarchical vision transformer using shifted windows. In Proceedings of the IEEE/CVF International Conference on Computer Vision. Montreal (Canada), 2021, p. 10012–10022. DOI: 10.1109/ICCV48922.2021.00986
  37. ZHAO, Y., LV, W. Y, XU, S. L., et al. DETRs beat YOLOs on real time object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle (USA), 2024, p. 16965–16974. DOI: 10.1109/CVPR52733.2024.01605
  38. SABAGHIAN, M., KEYVANRAD, M. A., MOGHADAMI, S. M. A novel compression framework for YOLOv8: Achieving real-time aerial object detection on edge devices via structured pruning and channel-wise distillation. arXiv: 2025, arXiv:2509.12918, p. 1–28. DOI: 10.48550/arXiv.2509.12918

Keywords: SAR image, YOLO11, aircraft detection, gated backbone

L. Ju, J.–J. Ren, Z.-Q. Zhang, C.-L. Dai, Y. Mao, K. Huo, K. Wei, W. Hu, W. Jiang [references] [full-text] [DOI: 10.13164/re.2026.0455] [Download Citations]
A Low-Scattering Tightly Coupled Dipole Array in a Triangular Lattice

A low-scattering tightly coupled phased array antenna based on a triangular lattice with resistive metallic strips is presented in this paper. Resistive metallic strips are loaded orthogonal to the dipole at the wide-angle impedance matching (WAIM) layer to achieve an in-band low-scattering array design. Compared to a rectangular grid, a triangular grid phased array reduces the total number of elements by approximately 13.4%. The proposed array achieves ±45° beam scanning in the E- and H-planes over the frequency band of 5.6-16 GHz, while exhibiting outstanding low-scattering characteristics for cross-polarized waves throughout the operational bandwidth. The monostatic RCS reduction exceeds 10 dB within the range of 6.6-16 GHz, and it also demonstrates excellent bistatic RCS reduction under oblique incidences. A 10×10 array prototype is fabricated and measured. Both simulated and measured results are in good agreement, validating the feasibility of the design.

  1. HOSSAIN, M. M., KOULOURIDIS, S., VENKATAKRISHNAN, S. B., et al. Simple wideband circularly polarized tightly coupled dipole array. IEEE Antennas and Wireless Propagation Letters, 2024, vol. 23, no. 6, p. 1929–1933. DOI: 10.1109/LAWP.2024.3374578
  2. WU, J., LI, Z., SUN, H. Design and performance of a fully-polarized tightly-coupled patch antenna for advanced phased array radar systems. Progress In Electromagnetics Research Letters, 2024, vol. 121, p. 41–49. DOI: 10.2528/PIERL24051302
  3. WANG, B., YANG, S., ZHANG, Z., et al. Ferrite-loaded ultralow profile ultrawideband tightly coupled dipole array. IEEE Transactions on Antennas and Propagation, 2022, vol. 70, no. 3, p. 1965–1975. DOI: 10.1109/TAP.2021.3118798
  4. GAO, G., DAI, J., LIU, S., et al. Low in-band-RCS tightly coupled dipole array based on metamaterial absorber. In 2024 International Applied Computational Electromagnetics Society Symposium (ACES-China). Xi’an (China), 2024, p. 1–3. DOI: 10.1109/ACES-China62474.2024.10699542
  5. SUN, J. X., CHENG, Y. J., WU, Y. F., et al. Ultrawideband, low-profile, and low-RCS conformal phased array with capacitance-integrated balun and multifunctional meta-surface. IEEE Transactions on Antennas and Propagation, 2022, vol. 70, no. 9, p. 7448–7457. DOI: 10.1109/TAP.2022.3183301
  6. ZHANG, Z., WANG, B., YANG, F., et al. Conical conformal tightly coupled dipole arrays co-designed with low-scattering characteristics. IEEE Transactions on Antennas and Propagation, 2022, vol. 70, no. 12, p. 12352–12357. DOI: 10.1109/TAP.2022.3209732
  7. FANG, S.-G., QU, S.-W. Broadband wide-scanning large-curvature cylindrical conformal dipole array antenna with low-scattering characteristics. IEEE Transactions on Antennas and Propagation, 2024, vol. 72, no. 8, p. 6437–6447. DOI: 10.1109/TAP.2024.3417293
  8. MAILLOUX, R. Phased Array Antenna Handbook. 3rd ed., Norwood (USA): Artech House, 2017. ISBN: 9781630815080
  9. MANIVANNAN, P., VOUVAKIS, M. N. Differentially-fed coincident phase-centered wideband dipole array on a triangular grid. In 2024 IEEE International Symposium on Antennas and Propagation and INC/USNC‐URSI Radio Science Meeting (AP-S/INC-USNC-URSI). Firenze (Italy), 2024, p. 855–856. DOI: 10.1109/AP-S/INC-USNC-URSI52054.2024.10687077
  10. KINDT, R. W., BINDER, B. T. Wideband planar printed aperture on a triangular lattice for millimeter wave applications. In 2023 17th European Conference on Antennas and Propagation (EuCAP). Florence (Italy), 2023, p. 1–3. DOI: 10.23919/EuCAP57121.2023.10133722
  11. LI, X., SONG, X., YIN, J., et al. A triangular grid millimeter wave planar array with wide bandwidth and angular scanning. In 2024 Cross Strait Radio Science and Wireless Technology Conference (CSRSWTC).Macao (China), 2024, p. 1–3. DOI: 10.1109/CSRSWTC64338.2024.10811645
  12. VISHWAKARMA, M., NAGARAJA RAO, P. Performance investigation of triangular lattice arrangement based UWB phased array for electronic warfare applications. In 2024 IEEE Microwaves, Antennas, and Propagation Conference (MAPCON). Hyderabad (India), 2024, p. 1–6. DOI: 10.1109/MAPCON61407.2024.10923352
  13. KINDT, R. W., BINDER, B. T. Wideband, low-profile, dual-polarized machined-metal array on a triangular lattice. IEEE Transactions on Antennas and Propagation, Feb. 2022, vol. 70, no. 2, p. 1097–1106. DOI: 10.1109/TAP.2021.3111317
  14. TAO, J., LI, X., TENG, F., et al. ±45° dual-slant polarized all-metal Vivaldi array with isosceles triangular lattice. Electromagnetics, 2026 vol. 46, no. 4, p. 507–523. DOI: 10.1080/02726343.2025.2566765
  15. ZHANG, T., CHEN, Y., LIU, Z., et al. A low-scattering Vivaldi antenna array with slits on nonradiating edges. IEEE Transactions on Antennas and Propagation, 2023, vol. 71, no. 2, p. 1999–2004. DOI: 10.1109/TAP.2022.3232690
  16. SHARP, E. A triangular arrangement of planar-array elements that reduces the number needed. IEEE Transactions on Antennas and Propagation, 1961, vol. 9, no. 2, p. 126–129. DOI: 10.1109/TAP.1961.1144967
  17. WANG, B., YANG, S., CHEN, Y., et al. Low cross-polarization ultrawideband tightly coupled balanced antipodal dipole array. IEEE Transactions on Antennas and Propagation, 2020, vol. 68, no. 6, p. 4479–4488. DOI: 10.1109/TAP.2020.2970087
  18. POZAR, D. M. The active element pattern. IEEE Transactions on Antennas and Propagation, 1994, vol. 42, no. 8, p. 1176–1178. DOI: 10.1109/8.310010
  19. PAN, Y., HUANG, Z., LIAO, S. Large phase-shifting channel spacing phased array antenna with triangular grid. In 2025 18th IEEE United Conference on Millimeter Waves and Terahertz Technologies (UCMMT). Nanjing (China), 2025, p. 1–3. DOI: 10.1109/UCMMT67044.2025.11286411
  20. GOU, Y., CHEN, Y., YANG, S. A tightly coupled dipole array with diverse element reflection phases for RCS reduction. Chinese Journal of Electronics, 2024, vol. 33, no. 2, p. 449–455. DOI: 10.23919/cje.2022.00.121

Keywords: Antenna array, triangular lattice, radar cross section (RCS)