Perkembangan Radar Modern: Integrasi Antena Adaptif, Pemrosesan Sinyal Cerdas, dan Aplikasi SAR serta RCS

Salsabila Husniah Putri, Sovian Aritonang

Abstract


This research aims to structuredly examine the latest developments in radar technology, combining adaptive antenna design, intelligent signal processing, and the use of Synthetic Aperture Radar (SAR) and Radar Cross Section (RCS). This study aims to address the research gap on how radar hardware innovations can be fully integrated with artificial intelligence-based algorithms, a topic that remains largely unexplored in the current literature. The method used is a Systematic Literature Review (SLR), with data from leading Q1 international journals indexed in Scopus, such as IEEE Transactions on Antennas and Propagation, IEEE Transactions on Signal Processing, IET Radar, Sonar & Navigation, and Remote Sensing of Environment. The analysis process includes article searches, application of entry-exit rules, retrieval of key information, topic clustering, and comparison of methods and results to identify key trends and remaining research gaps. The results of the study indicate that the main direction of modern radar research emphasizes the integration of adaptive antennas using metamaterials and phased-array systems, plus the application of machine learning and deep learning to detect and classify targets. However, significant challenges remain in optimizing the capabilities of learning models for general use across diverse environmental conditions, as well as effectively integrating hardware and intelligent algorithms. The key contribution of this research is the creation of a conceptual framework for modern radar that connects adaptive antenna components, artificial intelligence-based signal processing, and cross-disciplinary applications such as defense, autonomous vehicles, and earth monitoring. This study is expected to serve as a foundation for further research on the development of adaptive radar systems utilizing artificial intelligence and advanced materials, while also supporting strategic policy directions in the fields of defense and environmental surveillance.


Keywords


Modern radar; adaptive antenna; intelligent signal processing; Synthetic Aperture Radar (SAR); Radar Cross Section (RCS)

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References


Jianwei Li, Zhentao Yu ,Lu Yu,Pu Cheng,Jie Chen andCheng Chi, "A Comprehensive Survey on SAR ATR in Deep-Learning Era," Remote Sens, vol. 5, p. 15, 2023.

X. X. Zhu, S. Montazeri, M. Ali, Y. Hua, Y. Wang, L. Mou, Y. Shi, F. Xu and R. Bamler, "Deep Learning Meets SAR: Concepts, models, pitfalls, and perspectives," IEEE Geoscience and Remote Sensing Magazine, vol. 9, no. 4, pp. 143-172, 2021.

S. N. S. A. A. D. K. Alicia Passah, "Synthetic Aperture Radar image analysis based on deep learning: A review of a decade of research," Engineering Applications of Artificial Intelligence, vol. 123, 2023.

E. J. H. A. P.-N. T. N. N. D. Mahya G.Z. Hashemi, "Review of synthetic aperture radar with deep learning in agricultural applications," ISPRS Journal of Photogrammetry and Remote Sensing, vol. 218, pp. 20-49, 2024.

S. L. Aqeel Hussain Naqvi, "Review of Recent Phased Arrays for Millimeter-Wave Wireless Communication," Sensors, vol. 18, no. 10, 2018.

Zhang, X., Li, Y.,& Chen, Z, "Adaptive beamforming and pattern synthesis for smart radar antennas using RIS technology.," IEEE Transactions on Antennas and Propagation, vol. 71, no. 9, 2023.

Huang, H., Yang, C., & Wu, J. , "Reconfigurable intelligent surface for adaptive beam steering in radar systems," IEEE Transactions on Antennas and Propagation, vol. 70, no. 12, 2022.

Liu, Q., Gao, M., & Zhang, X., "Numerical analysis of metamaterial-based adaptive antennas using hybrid FEM-MoM method," IET Microwaves, Antennas & Propagation, vol. 17, no. 2, p. 130–141., 2023.

Y. L. Q. &. Z. J. Chen, "Optimization of wideband RCS reduction via controlled phase and amplitude in metasurface design," Applied Physics A, vol. 130, no. 5, p. 298, 2024.

Luo, K., Feng, R., & Xu, S, "Multiband radar cross section reduction using coding metasurfaces and hybrid optimization algorithms," Advanced Electronic Materials, vol. 9, no. 7, 2023.

Y. C. L. &. F. Q. Wang, "Wideband RCS reduction using phase-gradient metasurface with polarization control," Advanced Functional Materials, vol. 32, no. 46, (2022).

Li, D., Zhang, T., & Liu, P. , "Deep learning-based automatic target recognition in complex radar environments," IEEE Transactions on Signal Processing, vol. 71, 2023.

Zhou, R., Liu, H., & Li, K, "Intelligent radar systems: Integrating metamaterials and deep learning-based signal processing," Nature Communications Engineering, vol. 3, no. 4, p. 118, 2024.

Sun, L., Wang, J., & Tang, Z, "Unsupervised learning framework for radar target recognition under cluttered environments.," IEEE Access, , vol. 11, 2023.

Yang, P., Chen, X., & Luo, D, "Deep learning-based super-resolution reconstruction for synthetic aperture radar imaging," Remote Sensing, vol. 16, no. 2, p. 303, 2024.

Park, S., Lee, J., & Kim, H, "High-resolution SAR change detection using transformer-based feature fusion networks," IEEE Geoscience and Remote Sensing Letters, vol. 20, 2023.

Jin, X., Zhang, W., & Zhao, L, "SAR-optical data fusion using deep residual networks for high-resolution terrain mapping," Remote Sensing of Environment, p. 280, 2022.

Yousef Azizi, Mohammad Soleimani, Seyed-Hasan Sedighy, Ladislau Matekovits, "Wideband RCS Reduction by Single-Layer Phase Gradient Modulated Surface," Sensors, vol. 22, no. 19, 2022.

Tianwen Zhang, Tianjiao Zeng, Xiaoling Zhang, "Synthetic Aperture Radar (SAR) Meets Deep Learning," Remote Sensing, vol. 15, no. 2, 2023.

Shengyao Chen, Qi Feng, Longyao Ran, Feng Xi, "Reconfigurable Intelligent Surface-Enabled Array Radar for Interference Mitigation," Remote Sensing, 2024.

Zhang, H., et al. , "Multi-sensor fusion radar perception using deep neural embedding," Remote Sensing of Environment, 2022.

Wang, T., et al , "Domain-specific radar signal enhancement through data-driven transfer learning," Sensors, 2021.

S. e. a. Kim, "Attention-driven radar imaging for dynamic object detection," IEEE Transactions on Aerospace and Electronic Systems, 2023.

Y. e. a. Li, "Hybrid deep radar network for adaptive signal interpretation in cluttered environments," IEEE Transactions on Geoscience and Remote Sensing, 2023.

T. e. a. Wang, "Domain-specific radar signal enhancement through data-driven transfer learning," Sensors, 2021.

R. e. a. Chen, "Integrated spatiotemporal modeling for radar situational awareness," ISPRS Journal of Photogrammetry and Remote Sensing, 2024.




DOI: https://doi.org/10.30596/rmme.v9i1.27046

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