摘要
Inverse synthetic aperture radar (ISAR) image can be represented and reconstructed by sparse recovery (SR) approaches. However, the existing SR algorithms, which are used for ISAR imaging, have suffered from high computational cost and poor imaging quality under a low signal to noise ratio (SNR) condition. This paper proposes a fast decoupled ISAR imaging method by exploiting the inherent structural sparse information of the targets. Firstly, the ISAR imaging problem is decoupled into two sub-problems. One is range direction imaging and the other is azimuth direction focusing. Secondly, an efficient two-stage SR method is proposed to obtain higher resolution range profiles by using jointly sparse information. Finally, the residual linear Bregman iteration via fast Fourier transforms (RLBI-FFT) is proposed to perform the azimuth focusing on low SNR efficiently. Theoretical analysis and simulation results show that the proposed method has better performence to efficiently implement higher-resolution ISAR imaging under the low SNR condition.
Inverse synthetic aperture radar(ISAR) image can be represented and reconstructed by sparse recovery(SR) approaches. However, the existing SR algorithms, which are used for ISAR imaging, have suffered from high computational cost and poor imaging quality under a low signal to noise ratio(SNR)condition. This paper proposes a fast decoupled ISAR imaging method by exploiting the inherent structural sparse information of the targets. Firstly, the ISAR imaging problem is decoupled into two sub-problems. One is range direction imaging and the other is azimuth direction focusing. Secondly, an efficient two-stage SR method is proposed to obtain higher resolution range profiles by using jointly sparse information. Finally, the residual linear Bregman iteration via fast Fourier transforms(RLBI-FFT) is proposed to perform the azimuth focusing on low SNR efficiently. Theoretical analysis and simulation results show that the proposed method has better performence to efficiently implement higher-resolution ISAR imaging under the low SNR condition.
基金
supported by the National Natural Science Foundation of China(61671469)
作者简介
XIANG Long was born in 1978. He received his Ph.D. degree from Air Force Early Warning Academy in 2010. He is a lecturer at the Air Force Early Warning Academy, Wuhan, China. His research interests are radar system, radar imaging, and compressed sensing. E-mail: dick 500@163.com;LI Shaodong was born in 1987. He received his Ph.D. degree from Air Force Early Warning Academy in 2016. He is an engineer at Unit 93253 of the PLA, Dalian, China. His research interests are compressed sensing and inverse synthetic aperture radar imaging. E-mail: liying198798@126.com;YANG Jun was born in 1973. He received his Ph.D. degree from Air Force Engineering University, Xi’an, China in 2003. Now, he is a professor at the Air Force Early Warning Academy, Wuhan, China. His research interests are radar system, radar imaging and compressed sensing. E-mail: yangjem@126.com;CHEN Wenfeng was born in 1989. He is a Ph.D. candidate at Air Force Early Warning Academy, Wuhan, China. His research interests are compressed sensing and Bi-ISAR imaging. E-mail: chenwf925@163.com;XIANG Hu was born in 1978. He received his master’s degree from Air Force EarlyWarning Academy in 2007. He is a lecturer at Air Force Early Warning Academy, Wuhan, China. His research interests are radar system, target detection and imaging. E-mail: huker1978@sina.com.
