Improved range-Doppler algorithm with grey wolf optimization for lunar remote sensing imaging using bistatic-SAR

Ground-based bistatic synthetic aperture radar (Bi-SAR) is an important remote sensing imaging method that can be used for celestial imaging. At present, the imaging research of Bi-SAR mainly uses ephemeris to calculate the translational and rotational data of the Moon’s revolution required for imaging, but this method relies on detailed observation parameters. Without relying on ephemeris, we optimize the translational compensation process and images using the grey wolf optimization (GWO) algorithm based on the range-Doppler algorithm (RDA). First, the work explains the characteristics of lunar echoes and the principle of RDA from the perspective of simplifying models and signal formulas. Then, the optimization principle of the GWO algorithm is elaborated and combined with RDA to propose the GWO-RDA imaging process. Finally, imaging tests on GWO-RDA are conducted with real lunar radar data. The article also conducted imaging comparison experiments on the data using other methods. The results of image comparison and numerical comparison show that compared with other methods, GWO-RDA has a better motion compensation effect on data, and the final imaging effect is also better than other methods. In addition, by visualizing the iterative process, it can be found that GWO-RDA can converge quickly and accurately in the early stages of iteration, indicating its good computational efficiency.