Performance Analysis of Wavenumber Domain Algorithms for Highly Squinted SAR

Abstract

Wavenumber domain algorithms have unique advantages in processing highly squinted synthetic aperture radar data. This article studies the performance of three commonly used wavenumber domain algorithms including the classical wavenumber domain (CWD) algorithm, extended wavenumber domain (EWD) algorithm, and squint wavenumber domain (SWD) algorithm. First, the wavenumber domain signal expression under the zero-Doppler and acquisition-Doppler reference geometries are both derived. Second, the internal relationship between three wavenumber domain algorithms is analyzed. A new interpretation of the relationship between the three algorithms and an interpolation strategy are given. The analysis not only provides a deeper understanding of the three algorithms, but also provides a basis for comparing them. Then, the performance of the three wavenumber domain algorithms is evaluated from the perspectives of computational complexity, image quality, and geometric position through theoretical analysis and simulation experiments. Aiming at the problem that the range and azimuth profiles are not orthogonal, a method to calculate resolution and extract profile is proposed. The results show that all three algorithms can obtain a well-focused images if full-resolution interpolation is performed, and the computational complexities of CWD and SWD are less than that of EWD.

Very High Resolution Synthetic Aperture Radar Systems and Imaging: A Review

“…Therefore, non-approximate SAR processing algorithms should be used. The wavenumber domain algorithm (WDA) is the first choice among the frequencydomain algorithms [61]. Polar coordinate algorithms, represented by the polar format algorithm (PFA) [59], have significant advantages in imaging under conditions such as high-squint and variable-range gates.…”

“…The main difference between the two is whether linear range walk correction (LRWC) is applied to handle the spectral tilt effect. Chen et al [61] comprehensively evaluated the performance of the WDA [62], [63], extended wavenumber domain algorithm [64]- [67] and squint wavenumber domain algorithm [68] [69] in the squinted mode, and proposed an efficient interpolation strategy. The method based on orthogonal spectrum first performs LRWC to reduce the 2-D coupling and then performs imaging [70]- [72].…”

Two-Dimensional Autofocus for Ultra-High-Resolution Squint Spotlight Airborne SAR Based on Improved Spectrum Modification

“…After solving the stationary phase point X * with (7) and inserting it into (5), we can obtain the expression of the 2D spectrum.…”

“…With the developments over the past few decades, it can realize ultra-high-resolution (UHR) up to the centimeter level [3][4][5]. For UHR squint spotlight SAR, the squint mode is more flexible compared with the side-looking mode [6,7], and the UHR means more detailed information about the observation scenarios of interest. Although possessing many advantages, it is more challenging to process airborne UHR squint spotlight SAR data [8], for which the severe range-azimuth coupling introduced by the squint angle, and the spatial and frequency dependence of the motion error brought by the ultra-wide bandwidth, both place higher demands on the imaging process.…”

A Deep Learning Classification Scheme for PolSAR Image Based on Polarimetric Features

“…Deep learning is a hierarchical learning method, and features extracted through this method are more discriminative [41]. Therefore, it demonstrates excellent performance in PolSAR image classification and target detection [42][43][44][45][46][47][48][49]. It has also led scholars to use various convolutional neural networks for the classification and information extraction of PolSAR images [50][51][52][53][54].…”