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Extended Chirp Scaling-Baseband Azimuth Scaling-Based Azimuth–Range Decouple L Regularization for TOPS SAR Imaging via CAMP
This paper proposes a novel azimuth-range decouple-based L_{1} regularization imaging approach for the focusing in terrain observation by progressive scans (TOPS) synthetic aperture radar (SAR). Since conventional L_{1} 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 L_{1} 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.
Extended Chirp Scaling-Baseband Azimuth Scaling-Based Azimuth–Range Decouple L Regularization for TOPS SAR Imaging via CAMP
This paper proposes a novel azimuth-range decouple-based L_{1} regularization imaging approach for the focusing in terrain observation by progressive scans (TOPS) synthetic aperture radar (SAR). Since conventional L_{1} 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 L_{1} 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.
Extended Chirp Scaling-Baseband Azimuth Scaling-Based Azimuth–Range Decouple L Regularization for TOPS SAR Imaging via CAMP
Bi, Hui (author) / Zhang, Bingchen / Zhu, Xiao Xiang / Jiang, Chenglong / Hong, Wen
2017
Article (Journal)
English
Local classification TIB:
770/3710/5670
BKL:
38.03
Methoden und Techniken der Geowissenschaften
/
74.41
Luftaufnahmen, Photogrammetrie
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