Generalized Nonlinear Chirp Scaling Algorithm for High-Resolution Highly Squint SAR Imaging

• Main Authors: Tianzhu Yi, Zhihua He, Feng He, Zhen Dong, Manqing Wu
• Format: Article
• Language: English
• Published: MDPI AG 2017-11-01
• Series: Sensors
• Subjects: synthetic aperture radar (SAR); linear range walk correction (LRWC); generalized nonlinear chirp scaling (GNLCS); azimuth depth of focusing (ADOF); focusing precision
• Online Access: https://www.mdpi.com/1424-8220/17/11/2568
• ISSN: 1424-8220

This paper presents a modified approach for high-resolution, highly squint synthetic aperture radar (SAR) data processing. Several nonlinear chirp scaling (NLCS) algorithms have been proposed to solve the azimuth variance of the frequency modulation rates that are caused by the linear range walk correction (LRWC). However, the azimuth depth of focusing (ADOF) is not handled well by these algorithms. The generalized nonlinear chirp scaling (GNLCS) algorithm that is proposed in this paper uses the method of series reverse (MSR) to improve the ADOF and focusing precision. It also introduces a high order processing kernel to avoid the range block processing. Simulation results show that the GNLCS algorithm can enlarge the ADOF and focusing precision for high-resolution highly squint SAR data.