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Justin Romberg

  1. Compressive Matched-Field Processing.

    Пнд, 09/05/2011 - 15:01 — SomNambul
    Authors: William Mantzel, Justin Romberg, Karim Sabra
    Subjects: Information Theory
    Abstract

    Source localization by matched-field processing (MFP) generally involves
    solving a number of computationally intensive partial differential equations.
    This paper introduces a technique that mitigates this computational workload by
    "compressing" these computations. Drawing on key concepts from the recently
    developed field of compressed sensing, it shows how a low-dimensional proxy for
    the Green's function can be constructed by backpropagating a small set of
    random receiver vectors.

  2. Matched Filtering from Limited Frequency Samples.

    Пнд, 01/17/2011 - 16:01 — SomNambul
    Authors: Justin Romberg, Michael B. Wakin, Armin Eftekhari
    Subjects: Information Theory
    Abstract

    In this paper, we study a simple correlation-based strategy for estimating
    the unknown delay and amplitude of a signal based on a small number of noisy,
    randomly chosen frequency-domain samples. We model the output of this
    "compressive matched filter" as a random process whose mean equals the scaled,
    shifted autocorrelation function of the template signal.

  3. Restricted Isometries for Partial Random Circulant Matrices.

    Втр, 10/12/2010 - 15:01 — SomNambul
    Authors: Justin Romberg, Joel A. Tropp, Holger Rauhut
    Subjects: Information Theory
    Abstract

    In the theory of compressed sensing, restricted isometry analysis has become
    a standard tool for studying how efficiently a measurement matrix acquires
    information about sparse and compressible signals. Many recovery algorithms are
    known to succeed when the restricted isometry constants of the sampling matrix
    are small. Many potential applications of compressed sensing involve a
    data-acquisition process that proceeds by convolution with a random pulse
    followed by (nonrandom) subsampling. At present, the theoretical analysis of
    this measurement technique is lacking.

  4. Sparse Channel Separation using Random Probes.

    Ср, 02/24/2010 - 16:01 — SomNambul
    Authors: Justin Romberg, Ramesh Neelamani
    Subjects: Numerical Analysis
    Abstract

    This paper considers the problem of estimating the channel response (or
    Green's function) between multiple source-receiver pairs. Typically, the
    channel responses are estimated one-at-a-time: a single source sends out a
    known probe signal, the receiver measures the probe signal convolved with the
    channel response, and the responses are recovered using deconvolution.

  5. Channel Protection: Random Coding Meets Sparse Channels.

    Пнд, 08/31/2009 - 17:16 — SomNambul
    Authors: M. Salman Asif, William Mantzel, Justin Romberg
    Subjects: Information Theory
    Abstract

    Multipath interference is an ubiquitous phenomenon in modern communication
    systems. The conventional way to compensate for this effect is to equalize the
    channel by estimating its impulse response by transmitting a set of training
    symbols. The primary drawback to this type of approach is that it can be
    unreliable if the channel is changing rapidly. In this paper, we show that
    randomly encoding the signal can protect it against channel uncertainty when
    the channel is sparse. Before transmission, the signal is mapped into a
    slightly longer codeword using a random matrix.

Math-arch

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