Abstract
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.
Highlights
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
Our technique is compared to Haywood-Evans MSA [5] and PGA [8] motion compensation techniques with a signalto-noise ratio (SNR) of 20 dB
This paper has proposed a new parametric autofocus method for simultaneously realigning range and compensating for phase by estimating radial velocity and acceleration using a combination of range profile correlation for velocity estimation and subspace eigenvector rotation for acceleration estimation
Summary
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 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 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]). The phase-gradient algorithm (PGA, see Wahl et al [8]) is another popular nonparametric technique, which iteratively estimates the residual phase by integrating over range an estimate of its derivative (gradient). Parametric methods (Berizzi and Corsini [9], Xi et al [10], Wu et al [11], and Wang et al [12]) are much more robust but more numerically intensive
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