Optimal Asymmetric Apodizations and Shaped Pupils for Planet Finding Coronagraphy

The realization that direct imaging of extrasolar planets could be technologically feasible within the next decade or so has inspired a great deal of recent research into high-contrast imaging. We ourselves have contributed several design ideas, all of which can be described as shaped pupil coronagraphs. In this paper, we offer a complete and unified survey of asymmetric shaped pupils designs, some of which have been published in our previous papers. We also introduce a promising new design, which we call barcode masks. These masks achieve the required contrast with a fairly large discovery zone and throughput but most importantly they are perhaps the easiest to manufacture and might therefore stand up best to a refined analysis based on vector propogation techniques.Comment: 17 pages, 11 figures, submitted to Applied Optic

Joint space aspect reconstruction of wide-angle SAR exploiting sparsity

In this paper we present an algorithm for wide-angle synthetic aperture radar (SAR) image formation. Reconstruction of wide-angle SAR holds a promise of higher resolution and better information about a scene, but it also poses a number of challenges when compared to the traditional narrow-angle SAR. Most prominently, the isotropic point scattering model is no longer valid. We present an algorithm capable of producing high resolution reflectivity maps in both space and aspect, thus accounting for the anisotropic scattering behavior of targets. We pose the problem as a non-parametric three-dimensional inversion problem, with two constraints: magnitudes of the backscattered power are highly correlated across closely spaced look angles and the backscattered power originates from a small set of point scatterers. This approach considers jointly all scatterers in the scene across all azimuths, and exploits the sparsity of the underlying scattering field. We implement the algorithm and present reconstruction results on realistic data obtained from the XPatch Backhoe dataset

Solving ptychography with a convex relaxation

Ptychography is a powerful computational imaging technique that transforms a collection of low-resolution images into a high-resolution sample reconstruction. Unfortunately, algorithms that are currently used to solve this reconstruction problem lack stability, robustness, and theoretical guarantees. Recently, convex optimization algorithms have improved the accuracy and reliability of several related reconstruction efforts. This paper proposes a convex formulation of the ptychography problem. This formulation has no local minima, it can be solved using a wide range of algorithms, it can incorporate appropriate noise models, and it can include multiple a priori constraints. The paper considers a specific algorithm, based on low-rank factorization, whose runtime and memory usage are near-linear in the size of the output image. Experiments demonstrate that this approach offers a 25% lower background variance on average than alternating projections, the current standard algorithm for ptychographic reconstruction.Comment: 8 pages, 8 figure

Advanced Synthetic Aperture Radar Based on Digital Beamforming and Waveform Diversity

This paper introduces innovative SAR system concepts for the acquisition of high resolution radar images with wide swath coverage from spaceborne platforms. The new concepts rely on the combination of advanced multi-channel SAR front-end architectures with novel operational modes. The architectures differ regarding their implementation complexity and it is shown that even a low number of channels is already well suited to significantly improve the imaging performance and to overcome fundamental limitations inherent to classical SAR systems. The more advanced concepts employ a multidimensional encoding of the transmitted waveforms to further improve the performance and to enable a new class of hybrid SAR imaging modes that are well suited to satisfy hitherto incompatible user requirements for frequent monitoring and detailed mapping. Implementation specific issues will be discussed and examples demonstrate the potential of the new techniques for different remote sensing applications

Shaped Pupil Lyot Coronagraphs: High-Contrast Solutions for Restricted Focal Planes

Coronagraphs of the apodized pupil and shaped pupil varieties use the Fraunhofer diffraction properties of amplitude masks to create regions of high contrast in the vicinity of a target star. Here we present a hybrid coronagraph architecture in which a binary, hard-edged shaped pupil mask replaces the gray, smooth apodizer of the apodized pupil Lyot coronagraph (APLC). For any contrast and bandwidth goal in this configuration, as long as the prescribed region of contrast is restricted to a finite area in the image, a shaped pupil is the apodizer with the highest transmission. We relate the starlight cancellation mechanism to that of the conventional APLC. We introduce a new class of solutions in which the amplitude profile of the Lyot stop, instead of being fixed as a padded replica of the telescope aperture, is jointly optimized with the apodizer. Finally, we describe shaped pupil Lyot coronagraph (SPLC) designs for the baseline architecture of the Wide-Field Infrared Survey Telescope-Astrophysics Focused Telescope Assets (WFIRST-AFTA) coronagraph. These SPLCs help to enable two scientific objectives of the WFIRST-AFTA mission: (1) broadband spectroscopy to characterize exoplanet atmospheres in reflected starlight and (2) debris disk imaging.Comment: 41 pages, 15 figures; published in the JATIS special section on WFIRST-AFTA coronagraph

Advanced Concepts for Ultra-Wide-Swath SAR Imaging

This paper reviews advanced multi-channel SAR system concepts for the imaging of ultra-wide swaths with high azimuth resolution. Novel system architectures and operational modes are introduced and compared to each other with regard to their performance

A nonquadratic regularization-based technique for joint SAR imaging and model error correction

Regularization based image reconstruction algorithms have successfully been applied to the synthetic aperture radar (SAR) imaging problem. Such algorithms assume that the mathematical model of the imaging system is perfectly known. However, in practice, it is very common to encounter various types of model errors. One predominant example is phase errors which appear either due to inexact measurement of the location of the SAR sensing platform, or due to effects of propagation through atmospheric turbulence. We propose a nonquadratic regularization-based framework for joint image formation and model error correction. This framework leads to an iterative algorithm, which cycles through steps of image formation and model parameter estimation. This approach offers advantages over autofocus techniques that involve post-processing of a conventionally formed image. We present results on synthetic scenes, as well as the Air Force Research Laboratory (AFRL) Backhoe data set, demonstrating the effectiveness of the proposed approach