Eigenspace-Based Motion Compensation for ISAR Target Imaging
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Eigenspace-Based Motion Compensation for ISAR Target Imaging D. Yau, P. E. Berry, and B. Haywood Electronic Warfare and Radar Division, Department of Defence, Defence Science and Technology Organisation (DSTO), Australian Government, Edinburgh, South Australia 5111, Australia Received 8 June 2005; Revised 17 October 2005; Accepted 24 November 2005 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 target’s 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. Copyright © 2006 D. Yau et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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INTRODUCTION
Imaging of targets using inverse synthetic aperture radar (ISAR) exploits the large effective aperture induced by the relative translational and rotational motion between radar and target and has the ability to create high-resolution images of moving targets from a large distance. The technique is independent of range if rotational motion is significant, and it therefore has good potential to support automatic target recognition. A target image is formed by estimating the locations of target scatterers in both range and cross-range but the scatterer motion needs to be compensated for in order to avoid image blurring which can occur due to scatterer migration between range cells and scatterer acceleration. The common autofocusing methods can be categorized into parametric and nonparametric approaches. Computationally, nonparametric methods are much more efficient and easy to implement. The compensation for translational motion normally comprises two separate steps: range cell realignment and phase-error correction. Range cell realignment is considered to be routine and is based upon, for instance, the correlation method (see Chen and Andrews [1]) or the minimum-entropy method (see Wang and Bao [2]). Phase autofocus is more stringent in its requirements and many nonparametric methods have been proposed, most of
which track the phase history of an isolated dominant scatterer (prominent
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