B. Haywood

A survey on ISAR autofocusing techniques

Over many years of research, several ISAR autofocusing techniques have been proposed. Today, we can divide them into two main categories: parametric and non-parametric techniques. The prominent point processing and the phase gradient algorithm are two classical examples of non-parametric techniques, whereas the more recent image contrast and entropy based techniques represent a new generation of parametric techniques. In this paper, the advantages and disadvantages of each technique are highlighted and a performance analysis is carried out by means of ISAR image reconstruction of real data.

Use of 3D ship scatterer models from ISAR image sequences for target recognition

Abstract Traditionally, inverse synthetic aperture radar (ISAR) image frames are classified individually in an automatic target recognition system. When information from different image frames is combined, it is usually in the context of time-averaging to remove statistically independent noise fluctuations between images. The sea state induced variability of the ship target projections between frames, however, also provides additional information about the target, which can be used to construct a 3D representation of the target scatterer positions. In this paper, a method for classifying a ship based on 3D scatterer information from a sequence of 2D ISAR images is described. A preliminary classification result for simulated ISAR images of nine types of ship is also provided.

Clean technique for polarimetric ISAR

Inverse synthetic aperture radar (ISAR) images are often used for classifying and recognising targets. To reduce the amount of data processed by the classifier, scattering centres are extracted from the ISAR image and used for classifying and recognising targets. This paper addresses the problem of estimating the position and the scattering vector of target scattering centres from polarimetric ISAR images. The proposed technique is obtained by extending the CLEAN technique, which was introduced in radar imaging for extracting scattering centres from single-polarisation ISAR images. The effectiveness of the proposed algorithm, namely, the Polarimetric CLEAN (Pol-CLEAN) is tested on simulated and real data.

Ship motion estimation from ISAR data

The motion of ships seen through a high resolution coherent radar is of interest in object recognition based on Doppler imaging. This paper describes a preliminary investigation into the estimation of ship motion based on an assumption of sinusoidal motion.

Features for high resolution radar range profile based ship classification

This study investigates a variety of features in the context of automated ship classification of high resolution radar range profile. The features used are length, scatterer count, centres of mass, quantised range profile and Fourier modified direct Mellin transform coefficients. The results of evaluation using a modest database of high resolution range profiles, collected using an airborne radar, are then presented.

Image contrast and entropy based autofocusing for polarimetric ISAR

In recent studies the possibility of extending autofocusing techniques to fully polarimetric ISAR systems has been proposed. The image contrast and entropy based autofocusing techniques have been proposed in the last decade as some of the most common techniques for obtaining well focused single polarisation ISAR images. In this paper, the two techniques are extended and applied to fully polarimetric ISAR data. A performance analysis is then provided and compared to single polarisation ISAR using real data.

Polarimetric phase gradient autofocus

In the past decade the use of fully polarimetric SAR (polSAR) systems has increased significantly due to their effectiveness in target classification and detection applications. While polSAR imagery has been extensively used to distinguish between different scattering mechanisms in a scene, there has been a lack of research in the exploitation of polarimetry to assist in image formation and in particular autofocus for fine resolution SAR. In this paper an extension of the phase gradient algorithm (PGA) for polSAR imaging is proposed and its effectiveness is tested on simulated and real data.

Eigenspace-based motion compensation for ISAR target imaging

A novel motion compensation technique is presented for the purpose of forming focused ISAR images which exhibits the robustness of parametric methods but overcomes their convergence difficulties. Like the most commonly used parametric autofocus techniques in ISAR imaging (the image contrast maximization and entropy minimization methods) this is achieved by estimating a targets radial motion in order to correct for target scatterer range cell migration and phase error. Parametric methods generally suffer a major drawback, namely that their optimization algorithms often fail to converge to the optimal solution. This difficulty is overcome in the proposed method by employing a sequential approach to the optimization, estimating the radial motion of the target by means of a range profile cross-correlation, followed by a subspace-based technique involving singular value decomposition (SVD). This two-stage approach greatly simplifies the optimization process by allowing numerical searches to be implemented in solution spaces of reduced dimension.

Polarimetric ISAR autofocussing techniques: Comparison of results

This paper briefly describes a number of scalar, partially polarimetric, and fully polarimetric ISAR image autofocusing approaches that are based upon the PPP and ICBA autofocusing techniques. All approaches are then compared and contrasted using real world data provided by the Defence Science and Technology Organisation of Australia. From this analysis the ICBA approach results in an improved image focus as defined by the image contrast, image entropy and image peak measures, whereas the performance of the PPP based approaches are mixed at best.

Discrete 2-D system identification for imaging rotating radar targets

Abstract A new approach to high-resolution radar imaging is presented. The starting is the notion that there exists an image of the target which needs to be identified. This image consists of a finite array of pixel elements, each with a magnitude and phase representing the complex reflectivity of target scatterers. The radar observation process is then modelled as a sequence of matrix operations applied to the image. We first treat this problem in a general way, then impose practical constraints to finally deal with the problem of a rotating target. One solution for recovering the image from the measurements in this case is obtained by inverting the matrix operations, although the rotation rate of the target is required. A second solution based on maximum likelihood techniques is derived and the connection with the pseudoinverse is shown. When the rotation rate of the target is unknown, the maximum likelihood solution can still be found through minimization of a non-convex matrix trace cost function. It is also observed that for unknown target rotation rate the maximum likelihood solution gives the same image estimate as does the conventional Fourier transform solution. Some results of applying this identification approach to simulated and experimental data are presented. Various extensions are proposed, including a recursive solution.