Hybrid-Level Fusion of Radar Imaging Methods

Year 2026, Volume: 11 Issue: 1, 1 - 20, 01.10.2025

Emrah Onat , Yakup Özkazanç

https://doi.org/10.26833/ijeg.1611426

Abstract

In this paper, hybrid-level fusion of radar imaging methods generally used in fighter aircraft such as Real Beam Ground Mapping (RBGM), Doppler Beam Sharpening (DBS) and Unfocused Synthetic Aperture Radar (SAR) is explained. Historically, these methods are improved based upon previously developed methods. These methods that are considered in this paper chronologically are investigated in terms of theoretical aspects in detailed. The primary distinction between the methods lies in their cross-range resolutions. However, it is not feasible to generalize the resolution comparison among the methods, as cross-range resolution is influenced by both fixed parameters; such as real antenna beam-width, and dynamic parameters; range, including the aircraft's speed and the angle of the radar beam. These varying factors contribute to the differences in resolution across methods. Because of their some disadvantages against each other, the new method which is the fusion of them is proposed in order to defeat these deficiencies. With the help of the fusion algorithm, a new image can be generated dynamically by using different radar imaging methods for each pixel of the image depending on the real antenna beam width, momentary range, beam position and aircraft speed. So, an image with better resolution can be produced by fusion of radar imaging methods than the image they would produce alone. The article provides details about the hybrid-level fusion algorithm discussed, including its application to a reference image. Both the individual methods and the fusion algorithm were executed for comparison. Additionally, the improved DBS algorithm was also implemented for benchmarking purposes. The final images generated by each method and the fusion algorithm are presented, and evaluation metrics such as Root Mean Square Error (RMSE), Peak Signal to Noise Ratio (PSNR) and Entropy (EN) were calculated to compare the results.

Keywords

Remote Sensing , Radar Imaging , Real Beam Ground Mapping , Doppler Beam Sharpening , Synthetic Aperture Radar , Hybrid-Level Fusion

References

• Moir, I., & Seabridge, A. (2006). Military Avionics Systems-Aerospace Series. ISBN: 9780470016329
• Onat, E. (2014). An Investigation of Radar Imaging Methods Used in Fighter Aircrafts, Master’s Thesis, Hacettepe University, Department of Electrical & Electronics Engineering.
• Onat, E., & Özkazanç, Y. (2018). An analysis of Doppler beam sharpening technique used in fighter aircraft, 2018 IEEE 26th Signal Processing and Communications Applications Conference (SIU), Izmir, Turkey, doi: 10.1109/SIU.2018.8404521
• Tait, P. (2009). Introduction to Radar Target Recognition. ISBN: 0863415016
• Onart, S., & Arikan, O. (2006). Simulation of Real Beam Ground Mapping Mode of a Pulsed Radar, 2006 IEEE 14th Signal Processing and Communications Applications, Antalya, Turkey, pp. 1-4, doi: 10.1109/SIU.2006.1659887
• Zhang, L., Yu, Y., Xu, T., & Zhang, J. (2013). Simulation of Airborne Radar Real Beam Ground Map Based on Digital Terrain, 2013 International Conference on Computational and Information Sciences, Shiyang, China, pp. 26-29, doi: 10.1109/ICCIS.2013.15
• Wiley, C. A. (1954). Pulsed Doppler Radar Methods and Apparatus, (U. S. patent no. US3196436A), U. S. Patent and Trademark Office. https://patents.google.com/patent/US3196436A/en
• Jianyun, Z., & Deshu, L. (1996). An approach to improve the image quality in Doppler beam sharpening, Proceedings of International Radar Conference, Beijing, China, pp. 452-456, doi: 10.1109/ICR.1996.574493
• Tian, J., Cui, W., Ma, L., & Wu, S. (2012). DBS imaging based on Keystone transform, Journal of Systems Engineering and Electronics, vol. 23, no. 3, pp. 342-348, doi: 10.1109/JSEE.2012.00042
• Long, T., Lu, Z., Ding, Z. G., & Liu, L. (2011). A DBS Doppler centroid estimation algorithm based on entropy minimization, IEEE Trans. on Geoscience and Remote Sensing, 49(10): 3703–3712, doi: 10.1109/TGRS.2011.2142316
• Zhang, F., Yu, A., He, F., & Dong, Z. (2013). An improved DBS algorithm based on Doppler localization equations, 2013 IEEE China Summit and International Conference on Signal and Information Processing, Beijing, China, pp. 607-611, doi: 10.1109/ChinaSIP.2013.6625413
• Wu, Y., Zhang, P., Jia, L., Li, M., Liu, G., & Chen, H. (2015). Resolution enhancement for Doppler beam sharpening imaging, Radar Sonar and Navigation Iet, 9(7):843–851, doi:10.1049/iet-rsn.2014.0384
• Qi, L., Zheng, M., Yu, W., Li, N., & Hou, L. (2016). Super-resolution Doppler beam sharpening imaging based on an iterative adaptive approach, Remote Sensing Letters, 7(3):259–268, doi:10.1080/2150704X.2015.1128129
• Yang, H., Mao, D., Zhang, Y., Zhang, Y., Huang, Y., & Yang, J. (2017). Doppler beam sharpening imaging based on fast iterative adaptive approach, In Radar Conference, pages 1419–1423, doi: 10.1109/RADAR.2017.7944429
• Zhang, Y., Zhang, Q., Mao, D., Zhang, Y., Huang, Y., & Yang, J. (2019). Super-resolution Doppler Beam Sharpening based on Sparse Covariance Fitting, 2019 IEEE Radar Conference (RadarConf), Boston, MA, USA, pp. 1-4, doi: 10.1109/RADAR.2019.8835708
• Wiley, C. A. (1985). Synthetic Aperture Radars, in IEEE Transactions on Aerospace and Electronic Systems, vol. AES-21, no. 3, pp. 440-443, May 1985, doi: 10.1109/TAES.1985.310578
• Nałęcz, M., Śliwa, E., & Kulpa, K. (1992). Algorithms for radar signal processing in DBS systems. In (Ed.), Proc. of the XV-th National Conference Circuit Theory and Electronic Circuits (pp. 418–424).
• Pietrzyk, G., Samczynski, P., Gorzelanczyk, A., & Kulpa, K. (2004). Real-time implementation of doppler beam sharpening technique with simple motion estimation, First European Radar Conference (EURAD), pp. 185-188, Amsterdam, Netherlands.
• Kulpa, K., Purchla, M., & Malanowski, M. (2003). Real-time Unfocused SAR Algorithm with Motion Compensation, International Radar Symposium, Dresden.
• Radecki, K., Samczyński, P., Kulpa, K., & Drozdowicz, J. (2015). A real-time unfocused SAR processor based on a portable CUDA GPU, 2015 European Radar Conference (EuRAD), Paris, France, pp. 173-176, doi: 10.1109/EuRAD.2015.7346265
• Radecki, K., Samczyński, P., Kulpa, K., & Drozdowicz, J. (2015). Implementation of a real-time unfocused SAR algorithm using various computing platforms, 2015 Signal Processing Symposium (SPSympo), Debe, Poland, pp. 1-5, doi: 10.1109/SPS.2015.7168297
• Dawidowicz, B., Gados, A., Gorzelanczyk, A., Jarzebska, A., Kulpa, K. S., Mordzonek, M., Samczyński, P., & Smolarczyk, M. (2004). First Polish SAR trials, IEE Proceedings, Radar, Sonar and Navigation, Volume 153, Issue 2, doi: 10.1049/ip-rsn:20045120
• Malanowski, M., Krawczyk, G., Samczyn]ski, P., Kulpa, K., Borowiec, K., & Gromek, D. (2013). Real-time high-resolution SAR processor using CUDA technology. 2013 14th International Radar Symposium (IRS), 2, 673–678.
• Radecki, K., Samczyński, P., Kulpa, K.S., & Drozdowicz, J. (2016). A real-time focused SAR algorithm on the Jetson TK1 board, Image and Signal Processing for Remote Sensing XXII 10004, pg. 1000412, doi:10.1117/12.2241209
• Maslikowski, L., Samczynski, P., Baczyk, M., Krysik, P., & Kulpa, K. (2014). Passive bistatic SAR imaging- Challenges and limitations, in IEEE Aerospace and Electronic Systems Magazine, vol. 29, no. 7, pp. 23-29, doi: 10.1109/MAES.2014.130141
• Kulpa, J. S., Malanowski, M., Gromek, D., Samczyński, P., Kulpa, K., & Gromek, A. (2013). Experimental Results of High-Resolution ISAR Imaging of Ground-Moving Vehicles with a Stationary FMCW Radar, Intl. Journal of Electornics and Telecommunications, Vol. 59, No. 3, pp. 293–299. DOI: 10.2478/eletel-2013-0035
• Kulpa, J. S., Gromek, D., Samczyski, P., Kulpa, K., Gromek A., & Malanowski, M. (2013). Results of high-resolution ISAR imaging of ground moving targets, 2013 Signal Processing Symposium (SPS), Serock, Poland, pp. 1-4, doi: 10.1109/SPS.2013.6623569
• Purchla, M., & Malanowski, M. (2004). Simple motion compensation algorithm for unfocused synthetic aperture radar, Proc. SPIE 5484, Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments II, doi:10.1117/12.569054
• Malanowski, M. (2006). Multilook processing in unfocused synthetic aperture radar, Proc. SPIE 6159, Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments IV, doi: 10.1117/12.675188
• Buchhaupt, C., Fenoglio-Marc, L., Dinardo, S., Scharroo, R., & Becker, M. (2018). A fast convolution based waveform model for conventional and unfocused SAR altimetry, Advances in Space Research, Volume 62, Issue 6, Pages 1445-1463, doi: 10.1016/j.asr.2017.11.039
• Castelletti, D., Schroeder, D. M., Mantelli, E., & Hilger, A. (2018). Unfocused SAR Processing for Englacial Layer Slope Estimation Using Radar Sounder Data, IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain, pp. 4150-4153, doi: 10.1109/IGARSS.2018.8518928
• Blasband, C., Jorch, W.C., & Sigda, M. (1998). Real-time imaging radar simulation, IEE Colloquium on Radar System Modelling (Ref. No. 1998/459), London, UK, pp. 12/1-12/5, doi: 10.1049/ic:19980771
• Costantini, M., Farina, A., & Zirilli, F. (1997). The fusion of different resolution SAR images, in Proceedings of the IEEE, vol. 85, no. 1, pp. 139-146, doi: 10.1109/5.554214
• Dai, X., & Khorram, S. (1999). Data fusion using artificial neural networks: a case study on multitemporal change analysis, Computers, Environment and Urban Systems, Volume 23, Issue 1, Pages 19-31, doi: 10.1016/S0198-9715(98)00051-9
• Dong, J., Zhuang, D., Huang, Y., & Fu, J. (2009). Advances in Multi-Sensor Data Fusion: Algorithms and Applications. Sensors, 9(10), 7771-7784. doi: 10.3390/s91007771
• Kalamkar, S., & Geetha-Mary, A. (2023). Multimodal image fusion: A systematic review, Decision Analytics Journal, Volume 9, 100327, doi: 10.1016/j.dajour.2023.100327
• Burt, P., & Adelson, E. (1983). The Laplacian Pyramid as a Compact Image Code. IEEE Transactions on Communications, 31(4), 532–540. doi:10.1109/TCOM.1983.1095851
• Burt, P.J. (1992) A gradient pyramid basis for pattern- selective image fusion, Society for Information Displays (SID) International Symposium Digest of Technical Papers, 23, 467-470.
• Mallat, S. G. (1989). A theory for multiresolution signal decomposition: the wavelet representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 11(7), 674–693. doi:10.1109/34.192463
• Sariturk, B., Bayram, B., Duran, Z., & Seker, D. Z. (2020). Feature extraction from satellite images using segnet and fully convolutional networks (FCN). International Journal of Engineering and Geosciences, 5(3), 138-143. doi: 10.26833/ijeg.645426
• Vann, L. D., Cuomo, K. M., Piou, J. E., & Mayhan, J. T. (2000). Multisensor Fusion Processing for Enhanced Radar Imaging, Lincoln Laboratuary, Massachusetts Institute of Technology.
• Ye, F., He, F., & Sun, Z. (2008). Radar Signal Level Fusion Imaging, IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium, Boston, MA, USA, pp. IV - 1288-IV - 1291, doi: 10.1109/IGARSS.2008.4779966
• Xuan, F., & Lin, Z. (2020). Multi-radar Signal Level Fusion Detection Algorithm based on Fast Time Accumulation, 2020 IEEE International Conference on Information Technology,Big Data and Artificial Intelligence (ICIBA), Chongqing, China, pp. 1487-1492, doi: 10.1109/ICIBA50161.2020.9277453
• Ge, J., Wang, H., Liu, G., & Lv, W. (2021). The Design and Implementation of Multi-radar Signal-level Cooperative Detection System, 2021 CIE International Conference on Radar (Radar), Haikou, Hainan, China, pp. 2636-2640, doi: 10.1109/Radar53847.2021.10028395
• Zhang, W., Miao, C., Ma, Y., & Wu, W. (2023). A Signal-Level Fusion Distributed Radar Localization Method Based on Wideband Synthesis Technology, in IEEE Sensors Journal, vol. 23, no. 24, pp. 31017-31026, doi: 10.1109/JSEN.2023.3328353
• Yang, Y., Han, C., & Han, D. (2008). A Markov Random Field Model-based Fusion Approach to Segmentation of SAR and Optical Images, IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium, Boston, MA, USA, pp. IV - 802-IV - 805, doi: 10.1109/IGARSS.2008.4779844
• Altun, M., & Turker, M. (2022). Integration of Sentinel-1 and Landsat-8 images for crop detection: The case study of Manisa, Turkey. Advanced Remote Sensing, 2(1), 23–33. Retrieved from https://publish.mersin.edu.tr/index.php/arsej/article/view/322
• Li, J., Zhang, J., Yang, C., Liu, H., Zhao, Y., & Ye, Y. (2023). Comparative Analysis of Pixel-Level Fusion Algorithms and a New High-Resolution Dataset for SAR and Optical Image Fusion. Remote Sensing, 15(23), 5514. doi: 10.3390/rs15235514
• Duysak, H., & Yiğit, E. (2022). Investigation of the performance of different wavelet-based fusions of SAR and optical images using Sentinel-1 and Sentinel-2 datasets. International Journal of Engineering and Geosciences, 7(1), 81-90. doi: 10.26833/ijeg.882589
• Xu, J., Dong, M., Ding, D., & Chen, R. (2017). Multi-radar data fusion imaging based on iterative adaptive algorithm, 2017 International Applied Computational Electromagnetics Society Symposium (ACES), Suzhou, China, pp. 1-2.
• Rout, M., Nahak, S., Priyadarshinee, S., Mohapatra, P., Sa, K. D., & Dash, D. (2019). A Deep Learning Approach for SAR Image Fusion, 2019 2nd International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT), Kannur, India, pp. 335-339, doi: 10.1109/ICICICT46008.2019.8993376
• Hussain, M., O’Nils, M., Lundgren, J., & Mousavirad, S. J. (2024). A Comprehensive Review on Deep Learning-Based Data Fusion. IEEE Access, 12, 180093–80124.doi:10.1109/ACCESS.2024.3508271
• Yakar, M., & Yılmaz, H. M. (2010). Comparative evaluation of excavation volume by TLS and total topographic station based methods.
• Bigdeli, B., Pahlavani, P., & Amirkolaee, H. A. (2021). An ensemble deep learning method as data fusion system for remote sensing multisensor classification. Applied Soft Computing, 110, 107563. doi:10.1016/j.asoc.2021.107563
• Li, J., Liu, Y., Song, R., Li, Y., Han, K., & Du, Q. (2023). Sal2RN: A Spatial–Spectral Salient Reinforcement Network for Hyperspectral and LiDAR Data Fusion Classification. IEEE Transactions on Geoscience and Remote Sensing, 61, 1–14. doi:10.1109/TGRS.2022.3231930
• Song, D., Shang, S., & Ding, D. (2023). Super-Resolution Technique of Multi-Radar Fusion 2D Imaging Based on ExCoV Algorithm in Low SNR. Remote Sensing, 15(8), 2108. doi: 10.3390/rs15082108
• Tian, R., Zhao, R., & Wang, X. (2019). Multi-Level Fusion Processing Algorithm for Complex Radar Signals Based on Evidence Theory. Journal of Information Processing Systems, 15(5), 1243-1257. doi: 10.3745/JIPS.04.0136
• Stimson, G. W. (1998). Introduction to Airborne Radar. ISBN: 9781891121012
• Richards, M. A. (2022). Fundamentals of Radar Signal Processing. ISBN: 1260468712
• Zhang, Z. (1991). The Principle and Technique of Doppler Beam Sharpening (DBS), CIE 1991 International Conference on Radar, Beijing, China.
• Ulaby, F. T., Moore, R. K., & Fung, A. K. (1986). Microwave Remote Sensing: Active and Passive, Volume II: Radar Remote Sensing and Surface Scattering and Emission Theory. ISBN: 0890061912
• Brooker, G. M. (2009). Introduction to Sensors for Ranging and Imaging. ISBN: 9781891121746
• Song, W., & Hongyuan, W. (2004). High resolution DBS imaging and the moving target trajectory forming with raw SAR/GMTI Data, IGARSS 2004. 2004 IEEE International Geoscience and Remote Sensing Symposium, Anchorage, AK, USA, pp. 4251-4254 vol.6, doi: 10.1109/IGARSS.2004.1370074
• Mao, D., Zhang, Y., Zhang, Y., Huang, Y., & Yang, J. (2019). Doppler Beam Sharpening Using Estimated Doppler Centroid Based on Edge Detection and Fitting, in IEEE Access, vol. 7, pp. 123604-123615, doi: 10.1109/ACCESS.2019.2937992
• Ünel, F. B., Kuşak, L., Yakar, M., & Doğan, H. (2023). Coğrafi bilgi sistemleri ve analitik hiyerarşi prosesi kullanarak Mersin ilinde otomatik meteoroloji gözlem istasyonu yer seçimi. Geomatik, 8(2), 107-123.
• İnce, H., & Erdem, N. (2020). Investigation of the center coordinates of a circle with unknown radius using polar measurements. International Journal of Engineering and Geosciences, 5(1), 26-32. doi: 10.26833/ijeg.580373
• Wehner, D. R. (1987). High Resolution Radar. ISBN: 9780890067277
• Li, M., Wei, H., Sun, J., & Wu, Y. (2009). Keeping sharpening ratio constant for DBS of airborne mechanic scanning radar, 2009 IET International Radar Conference, Guilin, pp. 1-5, doi: 10.1049/cp.2009.0355
• Qiu, T., Du, Z., & Zhang, T. (2005). Technique of Doppler Beam Sharpening for Enhancing Radar Azimuth Resolution, Fire Control Radar Technology, Vo1.34, pp.17-20.
• Liu, Y., & Wu, S. (2005). Combination Algorithm for DBS Sub-Images in Scanning Mode, Radar Science and Technology, Vol. 3, No.2, pp91-95.
• Moreira, A., Prats-Iraola, P., Younis, M., Krieger, G., Hajnsek, I., & Papathanassiou, K. P. (2013). A tutorial on synthetic aperture radar. IEEE Geoscience and Remote Sensing Magazine, 1(1), 6–43. doi:10.1109/mgrs.2013.2248301
• Wang, G., Dong, X., Bao, Q., Zhu, D., & Xu, X. (2015). An unfocused SAR design to improve azimuth resolution of dual-frequency full-polarized scatterometer, 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Milan, Italy, pp. 4878-4881, doi: 10.1109/IGARSS.2015.7326924
• Curlander, J. C., & McDonough, R. N. (1991). Synthetic Aperture Radar Systems and Signal Processing, John Wiley & Sons.
• Onat, E., & Serin, M. (2011). Implementation of digital pulse doppler radar signal generator and receiver. 2011 IEEE 19th Signal Processing and Communications Applications Conference (SIU), 710–713. doi:10.1109/SIU.2011.5929749
• Schleher, D. Curtis. (2010). MTI and pulsed Doppler radar with MATLAB, 2d ed. (CD-ROM included), Artech House
• Scarborough, S. M., Gorham, L., Minardi, M. J., Majumder, U. K., Judge, M. G., Moore, L., Novak, L., Jaroszewksi, S., Spoldi, L., & Pieramico, A. (2010). A Challenge Problem for SAR Change Detection and Data Compression, in SPIE, Orlando, Florida.
• Özdemir, E. G., Zengin, T. U., & Güleç, H. A. (2024). Orman ekosistemindeki ağaç boylarının, optik, radar, lazer altimetre uydu verileri ve yardımcı kaynaklar kullanılarak Google Earth Engine platformunda modellenmesi. Geomatik, 9(2), 259-268. doi: 10.29128/geomatik.1449670
• Yakar, M., & Dogan, Y. (2018, November). 3D Reconstruction of residential areas with SfM photogrammetry. In Conference of the Arabian Journal of Geosciences (pp. 73-75). Cham: Springer International Publishing.
• Skolnik, M. I. (2008). Radar Handbook, Third Edition.
• Lesnik, C., Kawalec, A., and Serafin, P. (2011). A real time SAR processor implementation with FPGA, WIT Transactions on Modelling and Simulation, 51(10), 435-444, doi.org/10.2495/CMEM110381
• Di, W., Chen, C., & Liu, Y. (2018). FPGA-based parallel system for synthetic aperture radar imaging. 2018 International Conference on Electronics Technology (ICET), 430-433.doi:10.1109/ELTECH.2018.8401417
• Chua, M. Y., Koo, V. C., Lim, H. S., and Chan, Y. K. (2020). An FPGA based Real-Time Multi-Target Synthetic Aperture Radar Echoes Synthesizer, Journal of Engineering Technology and Applied Physics, 2 (2). pp. 10-16. ISSN 2682-8383, doi.org/10.33093/jetap.2020.2.2.2
• Baungarten-Leon, E. I., Martı]n-del-Campo-Becerra, G. D., Ortega-Cisneros, S., Schlemon, M., Rivera, J., & Reigber, A. (2023). Towards On-Board SAR Processing with FPGA Accelerators and a PCIe Interface. Electronics, 12(12), 2558. https://doi.org/10.3390/electronics12122558
• Mandapati, S., Balss, U., & Breit, H. (2024). Real Time Floating Point SAR Focusing on FPGA. EUSAR 2024; 15th European Conference on Synthetic Aperture Radar, 60–65.
• Rupniewski, M., Mazurek, G., Gambrych, J., Nałęcz, M., & Karolewski, R. (2016). A Real-Time Embedded Heterogeneous GPU/FPGA Parallel System for Radar Signal Processing. 2016 Intl IEEE Conferences on Ubiquitous Intelligence & Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People, and Smart World Congress (UIC/ATC/ScalCom/CBDCom/IoP/SmartWorld), 189–1197. doi:10.1109/UIC-ATC-ScalComCBDCom-IoP-SmartWorld.2016.0182