An improved scatter selection method for phase gradient autofocus algorithm in SAR/ISAR autofocus

PGA(phase gradient autofocus) has been widely used to remove motion induced phase errors in the ISAR(inverse synthetic aperture radar) imaging. The critical process for the processing time and image quality is windowing stage in PGA. In this paper, the new method to determine window size based on polynomial least square approximation is proposed. Moreover, dominant range bins are selected for efficient phase error estimation, which improve image quality and speed up convergence. The simulation results show that the proposed algorithm provides high quality ISAR images while computational efficiency of inherent PGA is retained.

Dual-polarized inverse synthetic aperture radar (ISAR) systems can provide more extra information than single-polarized ISAR systems, which can be used to increase image quality during the imaging process. However, there is limited research on the subject of dual-polarized ISAR. In this letter, an enhanced phase gradient autofocus (PGA) method was proposed for dual-polarized ISAR imaging. The Cloude-Pottier decomposition is employed to extract the polarization parameters <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$H$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula> from the dual-polarized primary image. Subsequently, the PGA is enhanced by selecting high-quality prominent scatterers based on these <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$H$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula> parameters. By making full use of the prominent scatterers selected by polarization information, the performance of PGA was improved, and thus, the imaging quality was significantly improved. Finally, both the real measurement data of aircraft targets and ship targets verified the effectiveness of the proposed method.

Current sonar autofocus techniques for blur removal originate in the radar community but have not provided a complete solution for Synthetic Aperture Sonar (SAS) imagery. The wide-beam, wide-band nature of SAS imagery makes implementation of Synthetic Aperture Radar (SAR) autofocus techniques difficult. This paper describes a generalisation of the standard Phase Gradient Autofocus (PGA) algorithm used in spotlight SAR that allows operation with stripmap SAS geometries. PGA uses prominent points within the target scene to estimate image blurring and phase errors. We show how PGA can be generalised to work with wide-band, wide-beam stripmap geometries. The SPGA method works by employing wave number domain 2D phase estimation techniques. The 2D phase errors are related to aperture position errors using the wave number transform. Robust sway estimates are obtained by using redundancy over a number of target points. We also present an improved Phase Curvature Autofocus (PCA) algorithm using the wave number transform. Preliminary results from the two algorithms (both on field-collected and simulated data sets) are presented and related to those obtained using previous methods. A discussion of SPGA's benefits over traditional algorithms and the limitations of the SPGA algorithm is presented. The SPGA algorithm was found to perform better than 2-D PCA on both simulated and field-collected data sets. Further testing on a variety of target scenes and imagery is required to investigate avenues of autofocus improvement.

Usually, in traditional Inverse Synthetic Aperture Radar (ISAR) systems design and mode selection for space satellite targets, coherent integration gain in azimuth direction hardly can be analyzed, which depends on target’s motion. In this study, we combine the target orbit parameters to determine its motion relative to radar and deduce coherent integration equation in ISAR imaging to realize the selection of imaging intervals based on coherent integration, which can ensure the resolution in azimuth direction. Meanwhile, we analyze the influence of target orbit altitude to echo power and imaging Signal-to-Noise Ratio (SNR) that provides a new indicator for space observation ISAR systems design. The result of simulation experiment illustrates that with target orbit altitude increasing, coherent integration gain in azimuth direction of large-angular observation offsets the decreasing of imaging SNR in a degree, which provides a brand-new perspective for space observation ISAR systems and signal processing design.

Inverse synthetic aperture radar (ISAR) imaging is typically useful when there is a need to classify, recognize, or identify a moving target of interest. In fact, an ISAR image highlights two-dimensional (2-D) geometric features of a target, which can provide indications of target's type, size, and other salient information. Such information can be then used for target classification, recognition, and identification. In this chapter, we will provide five case studies on applications of ISAR imaging. Such case studies are chosen to diversify the type of application and to give additional indications about how to interpret ISAR images. Specifically, in the first case study (Section 12.1), we show an example of ground-based ISAR images of a noncooperative sailing ship. The ISAR techniques used to form the ISAR images are the range-Doppler (Chapter 3) and the ICBA (Chapter 5) techniques. Different time windows are chosen to show the effects of the coherent processing interval (CPI) length on the ISAR image. Since the radar is ground based, this represents a case where the ISAR image is formed by exploiting only the sea surface induced target's motions. The second case study (Section 12.2) refers to a scenario where the radar is carried by an aircraft and the target is a ship at sea. In this case, both the target and the platform motions concur to the ISAR image formation. Also in this case, the range-Doppler and ICBA are used to form the ISAR image sequence. The third case study (Section 12.3) concerns dual ground-based/satellite ISAR imaging of a noncooperative sailing ship. In this experiment two radars are used to image a sailing ship at the same time. Two techniques are here used to form the ISAR images: a refocus technique (see Chapter 8) to form the satellite ISAR image; and a range-Doppler with ICBA to form the ground-based ISAR image. In this case study, we show how a dual system is able to form ISAR images of the same target with different target's views and projections. This is a simple case of multiple-perspective ISAR images, which may be looked at as a way to improve a target's classification and recognition [1]. The fourth case study (Section 12.4) shows ISAR images of four aircrafts obtained by processing data acquired with a ground-base radar. This case study may be regarded as the classic ISAR imaging example. Four different airplanes have been selected to show how their characteristics are mapped onto ISAR images. Such characteristics can then be used by classifiers for recognition purposes.

The phase-gradient autofocus (PGA) technique is robust over a wide range of imagery and phase error functions but the convergence usually requires 4-6 iterations. The authors propose to speed up the estimation convergence by selectively increasing the pool of quality synchronisation sources and not to be limited by the range pixels of the SAR map. This is highly probable since each range bin contains more than 1 prominent scatterers across the integration aperture. It is also highly probable that the least brightest selected scatterer in a range gate may turn out to be of higher energy as compared to the maximum brightest scatterer of another gate. With appropriate target filtering to finally select the quality scatterers out of the large pool and higher-order phase error measurement tool, the new algorithm achieves near convergence quality without iteration. The authors named this solution the Quality PGA algorithm (QPGA).

Inverse synthetic aperture radar (ISAR) imaging of multiple warhead targets in the same antenna beam is a big challenge when they are overlapping in the same range bin. Existing methods are usually based on single target motion compensation and they can't get clear ISAR image of multiple targets. This paper presents a new algorithm for ISAR imaging of multiple targets based on Fractional Fourier Transform (FrFT) CLEAN method to separate signals of different targets. Firstly, signal model of multiple targets is given. Then analyzed each target has different Doppler history, the signals of a certain target in a range bin correspond to a batch of multicomponent liner frequency modulation (LFM) signals. Theoretical analysis and computer simulations show the proposed method can generate the images from multiple target echoes effectively.

Synthetic aperture radar (SAR) is a critical enabling technology for maritime surveillance. However, maneuvering ships often appear defocused in SAR images, posing significant challenges for subsequent ship detection and recognition. To address this problem, this study proposes an improved iteration phase gradient resampling autofocus (IIPGRA) method. First, we extract the defocused ships from SAR images, followed by azimuth decompression and translational motion compensation. Subsequently, a centerline-driven adaptive azimuth partitioning strategy is proposed: the geometric centerline of the vessel is extracted from coarsely focused images using an enhanced RANSAC algorithm, and the target is partitioned into upper and lower sub-blocks along the azimuth direction to maximize the separation of rotational centers between sub-blocks, establishing a foundation for the accurate estimation of spatially variant phase errors. Next, phase gradient autofocus (PGA) is employed to estimate the phase errors of each sub-block and compute their differential. Then, resampling the original echoes based on this differential phase error linearizes non-uniform rotational motion. Furthermore, this study introduces the Rotational Uniformity Coefficient (β) as the convergence criterion. This coefficient can stably and reliably quantify the linearity of the rotational phase, thereby ensuring robust termination of the iterative process. Simulation and real airborne SAR data validate the effectiveness of the proposed algorithm.

Phase errors deteriorate the quality of synthetic aperture radar (SAR) images drastically. In the practical automotive SAR data acquisition, the phase error varies for different regions of the observed area. It seems reasonable to consider a similar error for targets in a small region of the SAR image. In this work, we combine a generalized method of the phase gradient autofocus (PGA) and the fast factorized back-projection (FFBP) algorithm to estimate the phase error and reconstruct compensated sub-images. The proposed method is evaluated with multi-target scene simulations and practical experiments by using a 77-GHz radar system. We will show that the space-variant phase error estimator improves the image quality with a lower computational cost than conventional methods.

The phase-gradient autofocus (PGA) technique is robust over a wide range of imagery and phase error functions, but the convergence usually requires four-six iterations. It is necessarily iterative in an attempt to converge on a dominant target against clutter interference, while sufficiently capturing the blur function. The authors propose to speed the estimation convergence by selectively increasing the pool of quality synchronization sources and not be limited by the range pixels of the SAR map. This is highly probable since each range bin contains more than one prominent scatterer across the integration aperture. It is also highly probable that the least-brightest selected scatterer in a range gate may turn out to be of higher energy as compared to the maximum brightest scatterer of another gate. With appropriate target filtering to final select the quality scatterers out of the large pool and with higher order phase error measurement tool, the new algorithm achieves near-convergence focusing quality without iteration. The authors named this solution the quality PGA (QPGA) algorithm.

The Synthetic Aperture Radar (SAR) system is a kind of modern high-resolution microwave imaging radar used in all-weather and all day long to provide remote sensing means and generate high resolution images of the land under illumination of radar beam. Unlike optical sensors, SAR algorithm needs a post-processing process on the data acquired to form the final image. In this article, we use the General Purpose Graphic Processing Units (GPGPU) to accelerate two of SAR algorithms, PGA (Phase Gradient Autofocus) and PDE (Partial Differential Equations), which are two computational intensive algorithms in the post-processing process for the system. Our work shows that the GPU architecture has different acceleration effects on the two algorithms. PGA can achieve an acceleration of 21.7% and PDE can get a speed up of 2.58\(\times \) on GPGPU. We analyse the reasons for the results and conclude that GPU is a promising platform to accelerate the SAR system.

Phase gradient autofocus (PGA) is widely used in spotlight SAR autofocus with strong robustness. However, being the different nature of synthetic aperture sonar (SAS) imagery, traditional PGA algorithm usually fails to yield satisfactory result on SAS image without modification. Three measures are taken to ensure that PGA can be applied in stripmap SAS autofocus. A SAS image is divided into sub-images which ensures that the phase error is invariant in each sub-image; The image shift in azimuth can be avoided by removing the mean value of the phase gradient and by using strong point targets in the image; The estimation accuracy can be improved by selecting range bins according to energy criteria combined with quality criteria. The proposed method is successfully applied on SAS image and excellent results are attained.

For the Inverse Synthetic Aperture Radar (ISAR) imaging, the ISAR image obtained by the Range-Doppler (RD) or time-frequency analysis methods can not display the target's real shape due to its azimuth relating to the target Doppler frequency, thus the cross-range scaling is required for ISAR image. In this paper, a fast cross-range scaling method for ISAR is proposed to estimate the Rotational Angular Velocity (RAV). Firstly, the proposed method utilizes efficient Pseudo Polar Fast Fourier Transform (PPFFT) to transform the rotational motion of two ISAR images from two different instant time into translation in the polar angle direction. Then, a new cost function called integrated correction is defined to obtain the RAV coarse estimation. Finally, the optimal RAV can be estimated using the Bisection method to realize the cross-range scaling. Compared with the available algorithms, the proposed method avoids the problems of precision loss and high computational complexity caused by interpolation operation. The results of computer simulation and real data experiments are provided to demonstrate the validity of the proposed method.

In this paper, we present a detailed analysis on the performance degradation of inverse synthetic aperture radar (ISAR) imagery with the polar format algorithm (PFA) due to the inaccurate rotation center. And a novel algorithm is developed to estimate the rotation center for ISAR targets to overcome the degradation. In real ISAR scenarios, the real rotation center shift is usually not coincided with the gravity center of the high-resolution range profile (HRRP), due to the data-driven translational motion compensation. Because of the imprecise information of rotation center, PFA image yields model errors and severe blurring in the cross-range direction. To tackle this problem, an improved PFA based on integrated cubic phase function (ICPF) is proposed. In the method, the rotation center in the slant range is estimated firstly by ICPF, and the signal is shifted accordingly. Finally, the standard PFA algorithm can be carried out straightforwardly. With the proposed method, wide-angle ISAR imagery of non-cooperative targets can be achieved by PFA with improved focus quality. Simulation and real-data experiments confirm the effectiveness of the proposal.

Two methods of focusing synthetic aperture radar (SAR) images are compared. Both a conventional subaperture cross-correlation method and a new phase gradient autofocus (PGA) algorithm developed at Sandia National Laboratories are shown to perform well if high-order phase errors are not present. With the introduction of significant high-order phase errors [e.g., due to uncompensated platform motion], both algorithms suffer a loss in performance. However, relative performance degradation is less for PGA than for the subaperture focusing technique. An explanation is presented for the observed behavior of the two autofocus techniques.© (1991) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.