Efficient Attributed Scatter Center Extraction Based on Image-Domain Sparse Representation

作     者：Yang, Dongwen Ni, Wei Du, Lan Liu, Hongwei Wang, Jiadong 

作者机构：Xidian Univ Natl Lab Radar Signal Proc Xian 710071 Peoples R China CSIRO Data61 Sydney NSW 2122 Australia 

出 版 物：《IEEE TRANSACTIONS ON SIGNAL PROCESSING》 (IEEE Trans Signal Process)

年 卷 期：2020年第68卷

页      面：4368-4381页

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 

基　　金：National Science Foundation of China [61771362, U1833203, 61671354] 111 Project [B18039] 

主　　题：Signal processing algorithms Scattering Frequency-domain analysis Matching pursuit algorithms Dictionaries Synthetic aperture radar Computational modeling Synthetic aperture radar (SAR) image attributed scattering model (ASM) orthogonal matching pursuit (OMP) quasi-Newton algorithm 

摘      要：As an efficient way to interpret the measurements of high-frequency synthetic aperture radar (SAR), an attributed scattering center (ASC) model provides concise and physically relevant features of complex targets. However, accurate extractions of ASCs have been heavily penalized by high memory requirements and computational complexity. We propose to convert SAR measurements to sparse representations in the image domain where the ASC model parameters can be estimated by using an orthogonal matching pursuit (OMP) algorithm or its Newtonlized variation. Two important new properties of the ASC model are unveiled in the image domain, namely, translatability and additivity. The properties can help save the dictionary of OMP from sampling the position and length parameters. The atoms of the dictionary become localized, thereby reducing the dictionary size and accelerating ASC extractions. Extensive experiments are conducted based on open-source XPATCH Backhoe data, measured MSTAR data, and synthetic backscatter data. The results show that the proposed approach is able to outperform existing image-domain algorithms in terms of accuracy and noise resistance, and outperform existing frequency-domain algorithms in terms of memory requirement and runtime.