SAR moving object imaging using sparsity imposing priors

Abstract

Synthetic aperture radar (SAR) returns from a scene with motion can be viewed as data from a stationary scene, but with phase errors due to motion. Based on this perspective, we formulate the problem of SAR imaging of motion-containing scenes as one of joint imaging and phase error compensation. The proposed method is based on the minimization of a cost function which involves sparsity-imposing regularization terms on the reflectivity field to be imaged, considering that it admits a sparse representation as well as on the spatial structure of the motion-related phase errors, reflecting the assumption that only a small percentage of the entire scene contains moving objects. To incorporate the spatial structure of the phase errors into the problem, we provide three different sparsity-enforcing prior terms. In order to achieve computational gains, we also present a two-step version of our approach, which first determines regions of interest that are likely to contain the moving objects and then applies our sparsity-driven approach for joint image reconstruction and autofocusing in such a spatially constrained setting. Our preliminary experiments demonstrate the effectiveness of this new moving target SAR imaging approach.

SAR imaging of moving targets by subaperture based low-rank and sparse decomposition

“…offering much better resolution (super-resolution in some cases [11,12]) and enhanced reconstruction quality compared to conventional SAR imaging [13,14,15,16]. Some promising sparsity-based moving target imaging approaches have also been suggested as in [17,18] which handle the additional phase terms caused by targets' motion as errors in the imaging model of a static SAR scene. However, these methods assume a comparatively high signal to clutter ratio (SCR) for best performance.…”

“…A sparsity-driven framework was proposed in [17,18] for SAR moving target imaging which treats the phase terms induced by the target motion as errors in the imaging model. Here, we provide an overview of their approach.…”

Iterative minimum entropy algorithm for refocusing of moving targets in SAR images

“…position offset and defocus. In order to accurately compensate the phase error, a variety of phase compensation methods have been proposed in the past few decades [10–17]. The phase gradient autofocusing (PGA) method proposed in [10] is a typical non‐parametric method of phase error estimation.…”

Refocusing of Moving Targets in SAR Images via Parametric Sparse Representation

“…However, due to the heavy computational burden, this method may not be suitable for these targets occupying a large number of resolution cells. Onhon et al utilize the sparsity-driven autofocus framework to solve the problem of moving target imaging in [18], where the phase error induced by target movement is corrected by the non-quadratic regularization approach. However, this method ignores the relationship between the phase error and the motion parameters.…”

“…Since the radar echo reflected from man-made moving targets are usually stronger than the background, in recent years, many sparsity-aware methods have been applied to SAR moving target parameter estimation and imaging [18][19][20][21][22][23][24][25]. Ref.…”