Direct Implementation of Circular SAR Imaging Using Back Projection Algorithm

Circular SAR (Synthetic Aperture Radar) imaging is a crucial technology in radar imaging that enables 360-degree full-aspect imaging of targets by moving the radar platform along a circular trajectory. The Back Projection (BP) algorithm is a classical time-domain imaging technique well-suited for circular SAR imaging scenarios.

The core concept of the BP algorithm involves back-projecting the radar-received echo signals to each pixel in the imaging area. In implementation, for every pixel in the imaging plane, the algorithm calculates the distance from each radar position along the circular trajectory to that pixel. Based on this distance, it determines the time delay of the echo signal and finally forms the imaging result through coherent integration.

Implementing the circular SAR BP algorithm in MATLAB typically requires the following steps: Parameter Configuration: Define radar parameters (carrier frequency, bandwidth), circular trajectory parameters (radius, number of sampling points), and imaging area resolution using structures or configuration files. Echo Signal Simulation: Generate circular SAR echo data by modeling point targets using mathematical formulations, or directly load measured data from datasets. Imaging Grid Establishment: Create mesh grid points within the imaging region using functions like meshgrid(), where each point represents a pixel to be imaged. Range Calculation and Time Delay Compensation: For each pixel, compute distances from all radar positions using vectorized geometric calculations, then apply phase and amplitude compensation to echo signals based on these distances. Coherent Integration: Accumulate all compensated signals from trajectory points using sum() or cumsum() functions to obtain the pixel's imaging value. Image Display: After processing all pixels, perform normalization using normalization techniques and display results with imaging functions like imagesc() or pcolor().

The BP algorithm offers high imaging accuracy and adaptability to arbitrary trajectories, but requires substantial computational resources, particularly for large imaging areas or dense trajectory points. MATLAB implementations can optimize efficiency through vectorized operations using array programming or parallel computing with Parallel Computing Toolbox. Circular SAR combined with BP algorithm has significant applications in military reconnaissance, disaster monitoring, and other fields requiring high-resolution imaging.