Extended Chirp Scaling-Baseband Azimuth Scaling-Based Azimuth–Range Decouple $L_{1}$ Regularization for TOPS SAR Imaging via CAMP

This paper proposes a novel azimuth–range decouple-based <inline-formula> <tex-math notation="LaTeX">$L_{1}$ </tex-math></inline-formula> regularization imaging approach for the focusing in terrain observation by progressive scans (TOPS) synthetic aperture radar (SAR). Since conventional <inline-formula> <tex-math notation="LaTeX">$L_{1}$ </tex-math></inline-formula> regularization technique requires transferring the (2-D) echo data into a vector and reconstructing the scene via 2-D matrix operations leading to significantly more computational complexity, it is very difficult to apply in high-resolution and wide-swath SAR imaging, e.g., TOPS. The proposed method can achieve azimuth–range decouple by constructing an approximated observation operator to simulate the raw data, the inverse of matching filtering (MF) procedure, which makes large-scale sparse reconstruction, or called compressive sensing reconstruction of surveillance region with full- or downsampled raw data in TOPS SAR possible. Compared with MF algorithm, e.g., extended chirp scaling-baseband azimuth scaling, it shows huge potential in image performance improvement; while compared with conventional <inline-formula> <tex-math notation="LaTeX">$L_{1}$ </tex-math></inline-formula> regularization technique, it significantly reduces the computational cost, and provides similar image features. Furthermore, this novel approach can also obtain a nonsparse estimation of considered scene retaining a similar background statistical distribution as the MF-based image, which can be used to the further application of SAR images with precondition being preserving image statistical properties, e.g., constant false alarm rate detection. Experimental results along with a performance analysis validate the proposed method.