Browsing by Author "Marks, Daniel L"

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    Adaptive millimeter-wave synthetic aperture imaging for compressive sampling of sparse scenes.
    (Opt Express, 2014-06-02) Mrozack, Alex; Heimbeck, Martin; Marks, Daniel L; Richard, Jonathan; Everitt, Henry O; Brady, David J
    We apply adaptive sensing techniques to the problem of locating sparse metallic scatterers using high-resolution, frequency modulated continuous wave W-band RADAR. Using a single detector, a frequency stepped source, and a lateral translation stage, inverse synthetic aperture RADAR reconstruction techniques are used to search for one or two wire scatterers within a specified range, while an adaptive algorithm determined successive sampling locations. The two-dimensional location of each scatterer is thereby identified with sub-wavelength accuracy in as few as 1/4 the number of lateral steps required for a simple raster scan. The implications of applying this approach to more complex scattering geometries are explored in light of the various assumptions made.
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    Compressive holography of diffuse objects.
    (Appl Opt, 2010-12-01) Choi, Kerkil; Horisaki, Ryoichi; Hahn, Joonku; Lim, Sehoon; Marks, Daniel L; Schulz, Timothy J; Brady, David J
    We propose an estimation-theoretic approach to the inference of an incoherent 3D scattering density from 2D scattered speckle field measurements. The object density is derived from the covariance of the speckle field. The inference is performed by a constrained optimization technique inspired by compressive sensing theory. Experimental results demonstrate and verify the performance of our estimates.
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    Compressive sensing and adaptive sampling applied to millimeter wave inverse synthetic aperture imaging
    (Optics Express, 2017-02-06) Zhu, Ruoyu; Richard, Jonathan T; Brady, David J; Marks, Daniel L; Everitt, Henry O
    © 2017 Optical Society of America.In order to improve speed and efficiency over traditional scanning methods, a Bayesian compressive sensing algorithm using adaptive spatial sampling is developed for single detector millimeter wave synthetic aperture imaging. The application of this algorithm is compared to random sampling to demonstrate that the adaptive algorithm converges faster for simple targets and generates more reliable reconstructions for complex targets.
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    Imaging through turbulence using compressive coherence sensing.
    (Opt Lett, 2010-03-15) Wagadarikar, Ashwin A; Marks, Daniel L; Choi, Kerkil; Horisaki, Ryoichi; Brady, David J
    Previous studies have shown that the isoplanatic distortion due to turbulence and the image of a remote object may be jointly estimated from the 4D mutual intensity across an aperture. This Letter shows that decompressive inference on a 2D slice of the 4D mutual intensity, as measured by a rotational shear interferometer, is sufficient for estimation of sparse objects imaged through turbulence. The 2D slice is processed using an iterative algorithm that alternates between estimating the sparse objects and estimating the turbulence-induced phase screen. This approach may enable new systems that infer object properties through turbulence without exhaustive sampling of coherence functions.
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    Improving Radar Imaging with Computational Imaging and Novel Antenna Design
    (2017) Zhu, Ruoyu

    Traditional radar imaging systems are implemented using the focal plane

    technique, steering beam antennas, or synthetic aperture imaging. These conventional

    methods require either a large number of sensors to form a focal plane array similar to the

    idea of an optical camera, or a single transceiver mechanically scanning the field of view.

    The former results in expensive systems whereas the latter results in long acquisition time.

    Computational imaging methods are widely used for the ability to acquire information

    beyond the recorded pixels, thus are ideal options for reducing the number of radar

    sensors in radar imaging systems. Novel antenna designs such as the frequency diverse

    antennas are capable of optimizing antennas for computational imaging algorithms. This

    thesis tries to find a solution for improving the efficiency of radar imaging using a method

    that combines computational imaging and novel antenna designs. This thesis first

    proposes two solutions to improve the two aspects of the tradeoff respectively, i.e. the

    number of sensors and mechanical scanning. A method using time-of-flight imaging

    algorithm with a sparse array of antennas is proposed as a solution to reduce the number

    of sensors required to estimate a reflective surface. An adaptive algorithm based on the

    Bayesian compressive sensing framework is proposed as a solution to minimize

    mechanical scanning for synthetic aperture imaging systems. The thesis then explores the

    feasibility to further improve radar imaging systems by combining computational

    imaging and antenna design methods as a solution. A rapid prototyping method for

    manufacturing custom-designed antennas is developed for implementing antenna

    designs quickly in a laboratory environment. This method has facilitated the design of a

    frequency diverse antenna based on a leaky waveguide design, which can be used under

    computational imaging framework to perform 3D imaging. The proposed system is

    capable of performing imaging and target localization using only one antenna and

    without mechanical scanning, thus is a promising solution to ultimately improve the

    efficiency for radar imaging.