Imaging with Radar Returns

A new compressed sensing (CS) image reconstruction method is proposed for high‐resolution wideband three‐dimensional synthetic aperture radar (SAR) imaging systems. In contrast to existing CS SAR methods that employ only a forward SAR transform in pre‐ or post‐processing, the proposed method employs both forward SAR and reverse SAR (R‐SAR) transforms in each CS iteration to improve the quality of reconstructed images. This study proposes a simple and elegant truncation repair method to combat the truncation error and utilises non‐uniform fast Fourier transform to reduce the SAR and R‐SAR transform errors, thereby ensuring the convergence of the CS algorithm and improving the quality of the reconstructed images. The proposed CS SAR method is applied to microwave and millimeter wave imaging systems for non‐destructive evaluation of materials embedded in stratified media. Three different specimens under test are measured by conventional uniform sampling and by random under‐sampling with 20% or 30% spatial points of the uniform sampling. The reconstructed images show that, albeit having much less measurement points, the proposed CS method achieves better image quality and lower background artefacts than the images reconstructed from the fully‐sampled uniform measurements.

Optical imaging usually suffers from aberrations that are induced by various structures when imaging biological samples. Usually aberrations degrade the imaging system performances by broadening the point spread function (PSF). Unexpectedly we show that in spatially incoherent interferometry like full-filed optical coherence tomography (FFOCT), the system PSF width is almost insensitive to aberrations. Instead of considering the PSF of a classical imaging system such as a microscope, we specifically pay attention to the system PSF of interferometric imaging systems for which an undistorted wavefront from a reference beam interferes with the distorted wavefront of the object beam. By comparing the cases of scanning OCT with spatially coherent illumination, wide-field OCT with spatially coherent illumination and FFOCT with spatially incoherent illumination, we found that in FFOCT with spatially incoherent illumination the system PSF width is almost independent of the aberrations and only its amplitude varies. This is demonstrated by theoretical analysis as well as numerical calculations for different aberrations, and confirmed by experiments with a FFOCT system. It is the first time to the best of our knowledge that such specific merit of incoherent illumination in FFOCT has been demonstrated. Based on this, the signal level is used as metric in our adaptive optics FFOCT system for retinal imaging. Only the main aberrations (defocus and astigmatism) that are dominating in eye are corrected to improve the signal to noise ratio and the high order aberrations are skipped. This would increase the correction speed thus reducing the imaging time.

Synthetic Aperture Radar (SAR) imaging systems are nowadays very common technics of imaging in remote sensing and environment survey. There are different acquisition modes: spotlight, stripmap, scan; different geometries: mono-, bi- and multi-static; and varieties of specific applications: interferometric SAR (InSAR), polarimetric SAR etc. In this paper, first a common inverse problem framework for all of them is given, and then basics of SAR imaging and the classical deterministic inversion methods are presented. Aiming at overcoming the inadequacies of deterministic methods, a general probabilistic Bayesian estimation method is pioneered for solving image reconstruction problems. In particular, two priors which simply allow the automated determination of the hyperparameters in a Type-II likelihood framework are considered. Finally, the performances of the proposed methods on synthetic data.

The point spread function (PSF) of imaging system with coded mask is generally acquired by practical measure- ment with calibration light source. As the thermal radiation of coded masks are relatively severe than it is in visible imaging systems, which buries the modulation effects of the mask pattern, it is difficult to estimate and evaluate the performance of mask pattern from measured results. To tackle this problem, a model for infrared imaging systems with masks is presented in this paper. The model is composed with two functional components, the coded mask imaging with ideal focused lenses and the imperfection imaging with practical lenses. Ignoring the thermal radiation, the systems PSF can then be represented by a convolution of the diffraction pattern of mask with the PSF of practical lenses. To evaluate performances of different mask patterns, a set of criterion are designed according to different imaging and recovery methods. Furthermore, imaging results with inclined plane waves are analyzed to achieve the variation of PSF within the view field. The influence of mask cell size is also analyzed to control the diffraction pattern. Numerical results show that mask pattern for direct imaging systems should have more random structures, while more periodic structures are needed in system with image reconstruction. By adjusting the combination of random and periodic arrangement, desired diffraction pattern can be achieved.

The Point Spread Function (PSF) is one of the key indicators characterizing the signal transfer characteristics of an imaging system. Edge method is applicable to calculate the PSF of the remote sensing imaging systems for its easy implement and robust noise-resistant ability. In this paper, a Double-Knife-Edge method is proposed to recover the degraded images using a precise estimated PSF of the imaging system. The exact motion-blur direction is estimated by image differentiation firstly. Two orthogonal edges, one of which is in the same direction as the main motion-blur, are picked up from the candidate edges via Hough transform and employed to obtain edge spread functions (ESF). Derived from these ESFs, a more accurate PSF is used to deconvolute the degraded image by an image restoration algorithm based on total variation (TV) deconvolution which is capable of suppressing the artifacts and noise. The experiment results show that this algorithm is adaptive and efficient to reconstruct remote sensing images, and the reconstructed image has better PSNR, MSE and MTF than the original degraded image.

This paper describes a 3D multi-static synthetic aperture radar (SAR) imaging system which utilises reflected GPS signals from moving objects on the Earth’s surface. The principle of bi-static radar is used to model the reflected GPS signals. The movement of a visible GPS satellite serves as a base for a synthetic aperture over an observation time period. As an example, a MATLAB simulation has been carried out in order to detect the movement of imaged objects under the assumption of one static GPS receiver with two targets which move with different speeds. The influence of the visible satellite’s position and velocity on the spatial resolution of such a SAR system is discussed. Simulation results show that by measuring the cross-correlation of the reflected GPS signal from the terrain and objects on it, the detection of the objects can enjoy a good spatial resolution for the case of moving objects and a moving GPS receiver. Furthermore, the spatial resolution is also related to the selection of visible GPS satellites with respect to their azimuths, elevations and velocities. This system has the following useful features: (a) no dedicated signal transmitter is required; (b) the GPS signal frequency is reused; (c) GPS operates round-the-clock and its signals cover the entire Earth’s surface; (d) low power consumption; and (e) known GPS signal structure.

Diagnostics are crucial to the operation of all particle accelerator facilities. They provide a means of optimising beam parameters and monitoring beam quality for users. However, for the next generation of accelerators, operating in extreme parameter spaces, most current beam diagnostics will fail; either through material failure or lack of resolution. This thesis presents three new diagnostic concepts to address this imminent diagnostic need. These novel diagnostics are supported by a simulation-based algorithm, successfully benchmarked throughout this work. All work has been conducted by the Author unless otherwise stated and referenced. The first technique measures sub-micron transverse beam profiles using optical transition radiation (OTR). Simulations were used to design a new imaging system, which was then installed at KEK (Japan). OTR images were then collected for a range of beam sizes. Comparisons were then made with simulations to successfully achieve sub-micron beam size measurements. Validation of these results was found in the agreement across different imaging systems for fixed beam parameters, providing a self-consistency check for the algorithm in lieu of alternate corroboration. The next diagnostic is a bunch length monitor for ∼<100 fs particle bunches; based on imaging the spatial distribution of coherent transition radiation (CTR) in the THz regime. Simulations demonstrated a relationship between image width and intensity, with bunch length. A THz imaging system was then installed at MAX IV (Sweden). CTR images were taken for a range of compressions. Maximum compression was successfully identified by monitoring relative changes in the properties of the images. Bunch lengths were then acquired from these images through comparison with simulations. Finally, the optical performance of a Digital Micro-mirror Device (DMD) was assessed. Image resolution produced by a DMD was quantified by measuring the Point Spread Function (PSF) of an imaging system, which was capable of remotely replacing a DMD with a planar mirror. The PSFs were measured using the sub-micron focus of a laser. Results show no measurable alteration to a PSF with the introduction of a DMD. This provides a foundation for the development of future diagnostics centred around this device. This thesis provides several validated proof-of-principle experimental results and supporting simulations. Future work has been identified for each topic, only possible following the results presented here, which will provide an array of novel next generation diagnostics.

For estimation of radar imaging system potential properties, the first-order problem is the image formation as a direct task formulation taking into account the objects' scattering, properties of medium and technical means. The character of the information signal formation on the system's receiver output determines further selection of distortion removal, filtration, interpolation and extrapolation, compression, feature extraction, and spectral estimation methods, i.e. methods of inverse problem solution, and structure of the whole system with certain tactical and technical characteristics. The problem of image formation can be solved by methods: by means of active and passive location. In the case of active system image forming consists of electromagnetic energy radiation in the determined direction and the reception of radio waves reflected from the objects. Passive systems use their own radiation of the investigated objects. Investigations in the improvement and development of new radar technologies based on methods of active and passive location are intensively performed. The results of investigation related with radar and radiometry imaging systems that are carried out at the Lviv Polytechnic National University are presented.

In the field of uncooled Long Wave Infra Red (LWIR) imaging, CMOS compatible bolometers technology is being more and more popular, exhibiting precise temperature measurement at moderate cost. The price of this technology is proportional to the number of components produced per wafer, leading to a shrinkage of the pixel. Enhancing the resolution level of the focal plane array (FPA) requires an improvement of the point spread function (PSF) of the optical system, leading to more and more complex aspheric lenses, and an increased cost of imaging systems. We propose to add a sub-wavelength blade to the existing parts of the imaging system to ease the overall improvement of the image quality in applications with a constraint budget. The main function of such a subwavelength blade should be to control the phase of the light into an optical system to compensate optical aberrations. A cost effective solution consists to make such devices using microelectronics based collective fabrication process. The main difficulty is to predict the subwavelength blade behavior within an optical system that is to say combining millimeter sized optical components that are modeled using ray-tracing or electromagnetic simulations. In this paper we present the results obtained from an effort to simulate, fabricate and characterize all-dielectric subwavelength blade. In an imaging system, our devices will have to deal with non-flat wavefronts. Our method is based on Fourier Modal Method and Angular Spectrum Method to simulate subwavelength optics into such an optical system. Finally, we have compared our simulations results to experiments on basic examples, like spherical aberration correction of a commercial lens.

Sparse radar imaging seeks to provide high-performance radar imaging technique for observed scenes by using L 1/2 regularization method. This article surveys our systematic explorations and innovative practices for tackling such problem. The key contributions include a new sparse radar imaging theory based on L 1/2 regularization theory, a new sparse radar imaging model based on echo simulation operator, a new sparse radar imaging system evaluate approach based on 3D phase diagram. The synthetic aperture radar (SAR) imaging can be formulated as a L 1/2 regularization problem, which optimizes a quadratic error term of radar imaging. The method can implement SAR imaging effectively at lower sampling rates than Nyquist rate and produce higher quality images with reduced sidelobes. Echo simulation operator is constructed to generate SAR raw data by means of inverse of matched filter-based imaging procedure, instead of the exact observation matrix. The echo simulation operator based model forms a new radar imaging method, which reduces the computational complexity and memory occupation to the same order as matched filter imaging algorithm, and hence, makes L 1/2 regularization reconstruction of large-scale considered scene become possible. 3D phase diagram is employed to analyze and evaluate the performance of sparse radar imaging system. In 3D phase diagram, success rates of signal recovery can be derived from the statistics of relative errors between the ground truth and the recovered signals. The success rates for each combination of SNR, sparsity and sampling ratio are provided to form a three-dimensional phase diagram. The trend of success rates is presented accurately by taking advantage of 3D phase transition boundary. We design the sparse radar prototype and conduct a series of airborne experiments, which demonstrate the applicability and potentials of the sparse radar imaging. 3D phase diagram is used to evaluate the relationship between reconstruction performance and signal-to- noise ratio. The experimental results verity the effectiveness of the L 1/2 regularization method and the feasibility of 3D phase diagram. The sparse radar imaging based on L 1/2 regularization has promising application prospects. Firstly, it could be generalized to deal with 3D-SAR imaging, which achieves better flexibility for system design and super-resolution capability of height dimension for reconstruction property. Then, we review some of the most popular techniques to exploit sparsity, for recovering high-dimensional matrices and higher order tensors from compressive measurements, and further emphasize the broad application prospects of higher order compressive sensing in 3D-SAR imaging.

Synthetic aperture radar (SAR) and inverse synthetic aperture radar (ISAR) are enhanced resolution imaging radar systems. These systems receive coherent radar signal returns from an illuminated scene and use the signal modulation related to terrain reflectivity and relative motion between the scene and the observation platform to form an image of the scene. The extraction, storage, and coherent processing of these modulation terms represent an enormous signal processing task which classically has been performed by optical techniques. The flexibility and reduced cost of modern high performance, high bandwidth digital signal processing systems is the motivation for full DSP based imaging radar systems. In this paper we review the common DSP tasks performed by basic SAR and ISAR imaging radars.

In this paper the effects of scattering, with four atmospheric fog aerosols models, on the overall point spread function (PSF) of an imaging system are investigated.The PSF and radiometric calculations are then used to estimate the mean numbers of direct path and scattered photons detected by the imaging system in the visible and near infra-red (NIR) spectral bands of the electro-magnetic spectrum (EMS), for a horizontal near-ground imaging scenario.The image spectrum signal-to-noise ratio (SNR) is then used to estimate the noise effective spatial resolution of the imaging system which emphasizes the degrading effect of fog aerosol models on the spatial resolution of the imaging systems.

This paper investigates a new multifrequency compressed sensing (CS) model for 2-D near-field microwave and millimeter-wave synthetic aperture radar (SAR) imaging system, which usually collects multifrequency sparse data. Spatial data of each frequency are represented as a hierarchical tree structure under a wavelet basis and spatial data of different frequencies are modeled as a joint structure, because they are highly correlated. Based on the developed multifrequency CS model, a new CS approach is proposed by exploiting both the intrafrequency and interfrequency correlations, and enriches the existing CS approaches for 2-D near-field microwave and millimeter-wave SAR image reconstruction from undersampled measurements. Combining a splitting Bregman update with a variation of the parallel Fast Iterative Shrinkage-Thresholding Algorithm-like proximal algorithm, the proposed CS approach minimizes a linear combination of five terms: a least squares data fitting, a $\textrm {multi-}\ell _{1}$ norm, a multitotal variation norm, a joint-sparsity $\ell _{21}$ norm, and a tree-sparsity overlapping $\ell _{21}$ norm. Simulation and experimental results demonstrate the superior performance of the proposed approach in terms of both efficiency and convergence speed.

The Pacific Northwest National Laboratory has developed three-dimensional holographic (synthetic aperture) radar imaging techniques and systems for a wide variety of near-field applications. These applications include radar crosssection (RCS) imaging, personnel screening, standoff concealed weapon detection, concealed threat detection, throughbarrier imaging, ground penetrating radar (GPR), and non-destructive evaluation (NDE). Sequentially-switched linear arrays are used for many of these systems to enable high-speed data acquisition and 3-D imaging. In this paper, the techniques and systems will be described along with imaging results that demonstrate the utility of near-field 3-D radar imaging for these compelling applications.

Eddy-current imaging has been described in detail in previous publications [1,2,3]. As with other imaging systems, the image of an object represents blurring of the structures of the object by the system point spread function (PSF). Differing from other imaging systems, the PSF is very large and causes great spatial blurring of the object. This is because the eddy-current probe is a coil; its active area is much greater than the size of a beam of light, sound, or x-rays.