Research Information System
International Journal of Engineering and Geosciences, vol.11, no.1, pp.1-20, 2026 (ESCI, Scopus, TRDizin)
In this paper, a 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 have been improved based on previously developed methods. These methods considered in this paper chronologically are investigated in terms of theoretical aspects in detail. 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 the cross-range resolution is influenced by both fixed and dynamic parameters, such as real antenna beam width, range, aircraft speed, and squint angle of the radar beam. These varying factors contribute to the differences in resolution across methods. Because of their disadvantages against each other, a new method, which is the fusion of them, is proposed 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, squint angle, 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 details 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 and Discrete Wavelet Transform (DWT) which is a very well-known pixel-level fusion algorithm, were also implemented for benchmarking purposes. Besides, simulations were conducted to investigate the impact of parameter variations on the images produced by the hybrid-level fusion algorithm, and the corresponding metrics were calculated and analyzed. 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.