Comparative Analysis of the Reverse Backprojection Algorithm for ISAR Signal Reconstruction and Imaging in LEO Scenarios.

Sammanfattning

Inverse Synthetic Aperture Radar (ISAR) is a radar imaging technique that utilizes the translation and rotation of a target to create high resolution images at long distances. Compared to optical sensors, radars work independently of external illumination and in all weather conditions. ISAR is used for identification and classification of moving targets, with applications including surveillance, space based observations, and non-cooperative target analysis. This thesis implements the Reverse Backprojection Algorithm (RBPA) for the synthesis of raw ISAR data trough inversion of a focused ISAR image back to the data domain. Two slightly different methods are considered and compared: the Convolutional Reverse Backprojection Algorithm (CRBPA) and the Efficient Reverse Backprojection Algorithm (ERBPA). To verify the accuracy of raw data generation using the implemented algorithms, an independent validation code was developed. This code includes the whole process from signal generation to image formation using the regular Backprojection Algorithm (BPA). The results demonstrate that the implemented algorithms can successfully reconstruct raw ISAR data, enabling separate processing of unfocused image sub-regions.