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Novel imaging methods of stepped frequency radar based on compressed sensing

Novel imaging methods of stepped frequency radar based on compressed sensing

作     者:Jihong Liu Shaokun Xu Xunzhang Gao Xiang Li 

作者机构:Institute of Space Electronic Technology School of Electronic Science and Engineering National University of Defense Technology Changsha 410073 P. R. China 

出 版 物:《Journal of Systems Engineering and Electronics》 (系统工程与电子技术(英文版))

年 卷 期:2012年第23卷第1期

页      面:47-56页

核心收录:

学科分类:080904[工学-电磁场与微波技术] 0808[工学-电气工程] 0809[工学-电子科学与技术(可授工学、理学学位)] 08[工学] 0810[工学-信息与通信工程] 081105[工学-导航、制导与控制] 0802[工学-机械工程] 081001[工学-通信与信息系统] 081002[工学-信号与信息处理] 0825[工学-航空宇航科学与技术] 0811[工学-控制科学与工程] 0822[工学-轻工技术与工程] 0812[工学-计算机科学与技术(可授工学、理学学位)] 

基  金:supported by the Prominent Youth Fund of the National Natural Science Foundation of China (61025006) 

主  题:radar imaging compressed sensing (CS) stepped frequency random sampling. 

摘      要:The theory of compressed sensing (CS) provides a new chance to reduce the data acquisition time and improve the data usage factor of the stepped frequency radar system. In light of the sparsity of radar target reflectivity, two imaging methods based on CS, termed the CS-based 2D joint imaging algorithm and the CS-based 2D decoupled imaging algorithm, are proposed. These methods incorporate the coherent mixing operation into the sparse dictionary, and take random measurements in both range and azimuth directions to get high resolution radar images, thus can remarkably reduce the data rate and simplify the hardware design of the radar system while maintaining imaging quality. Ex- periments from both simulated data and measured data in the anechoic chamber show that the proposed imaging methods can get more focused images than the traditional fast Fourier trans- form method. Wherein the joint algorithm has stronger robustness and can provide clearer inverse synthetic aperture radar images, while the decoupled algorithm is computationally more efficient but has slightly degraded imaging quality, which can be improved by increasing measurements or using a robuster recovery algorithm nevertheless.

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