24 January 2017 Dual-channel fast iterative shrinkage-thresholding regularization algorithm for scanning radar forward-looking imaging
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Abstract
Regularization technology is an efficient and robust method to implement forward-looking imaging of scanning radar, and the selection of regularization parameter is quite important for the imaging performance. When the imaging scene contains sparse strong targets as well as a weak background, different regularization parameters should be adopted. A dual-channel fast iterative shrinkage-thresholding (D-FIST) regularization algorithm is proposed. First, by setting a threshold with mean segmentation method, the strong targets are extracted from the echo to formulate the data of channel I, and the entire data are included in channel II. The L-curve method is adopted to determine the regularization parameter for two channels data, respectively. Then, the data are processed by the FIST algorithm with corresponding parameters. At last, the imaging result is obtained by combining the results of two channels. Simulation experiments show that the imaging results of D-FIST are better than that of direct method, especially in a low SNR situation.
© 2017 Society of Photo-Optical Instrumentation Engineers (SPIE)
Ke Tan, Wenchao Li, Yulin Huang, Jianyu Yang, "Dual-channel fast iterative shrinkage-thresholding regularization algorithm for scanning radar forward-looking imaging," Journal of Applied Remote Sensing 11(1), 015008 (24 January 2017). https://doi.org/10.1117/1.JRS.11.015008 . Submission: Received: 11 August 2016; Accepted: 6 January 2017
Received: 11 August 2016; Accepted: 6 January 2017; Published: 24 January 2017
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