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Extended Chirp Scaling-Baseband Azimuth Scaling-Based Azimuth-Range Decouple L1 Regularization for TOPS SAR Imaging via CAMP
This paper proposes a novel azimuth–range
decouple-based L1 regularization imaging approach for the
focusing in terrain observation by progressive scans (TOPS)
synthetic aperture radar (SAR). Since conventional L1 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 L1 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