William Marsh Rice University

Mark A Davenport

Richard G Baraniuk

Petros T Boufounos

Jason N Laska

Michael B Wakin

Marco F Duarte

Dror Z Baron

Shriram Sarvotham

Joel A Tropp

Yehia Massoud

Sami Kirolos

Tamer Ragheb

Stephen R Schnelle

Mohamed Elnozahi

A method for compressive domain filtering and interference cancelation processes compressive measurements to eliminate or attenuate interference while preserving the information or geometry of the set of possible signals of interest. A signal processing apparatus assumes that the interfering signal lives in or near a known subspace that is partially or substantially orthogonal to the signal of interest, and then projects the compressive measurements into an orthogonal subspace and thus eliminate or attenuate the interference. This apparatus yields a modified set of measurements that can provide a stable embedding of the set of signals of interest, in which case it is guaranteed that the processed measurements retain sufficient information to enable the direct recovery of this signal of interest, or alternatively to enable the use of efficient compressive-domain algorithms for further processing. The method and apparatus operate directly on the compressive measurements to remove or attenuate unwanted signal components.

Method and apparatus for compressive domain filtering and interference cancellation

A typical data acquisition system takes periodic samples of a signal, image, or other data, often at the so-called Nyquist/Shannon sampling rate of two times the data bandwidth in order to ensure that no information is lost. In applications involving wideband signals, the Nyquist/Shannon sampling rate is very high, even though the signals may have a simple underlying structure. Recent developments in mathematics and signal processing have uncovered a solution to this Nyquist/Shannon sampling rate bottlenck for signals that are sparse or compressible in some representation. We demonstrate and reduce to practice methods to extract information directly from an analog or digital signal based on altering our notion of sampling to replace uniform time samples with more general linear functionals. One embodiment of our invention is a low-rate analog-to-information converter that can replace the high-rate analog-to-digital converter in certain applications involving wideband signals. Another embodiment is an encoding scheme for wideband discrete-time signals that condenses their information content.

Method and apparatus for on-line compressed sensing

A method for estimating and tracking locally oscillating signals. The method comprises the steps of taking measurements of an input signal that approximately preserve the inner products among signals in a class of signals of interest and computing an estimate of parameters of the input signal from its inner products with other signals. The step of taking measurements may be linear and approximately preserve inner products, or may be non-linear and approximately preserves inner products. Further, the step of taking measurements is nonadaptive and may comprise compressive sensing. In turn, the compressive sensing may comprise projection using one of a random matrix, a pseudorandom matrix, a sparse matrix and a code matrix. The step of tracking said signal of interest with a phase-locked loop may comprise, for example, operating on compressively sampled data or by operating on compressively sampled frequency modulated data, tracking phase and frequency.

Method and apparatus for compressive parameter estimation and tracking

A method for automatic gain control comprising the steps of measuring a signal using compressed sensing to produce a sequence of blocks of measurements, applying a gain to one of the blocks of measurements, adjusting the gain based upon a deviation of a saturation rate of the one of the blocks of measurements from a predetermined nonzero saturation rate and applying the adjusted gain to a second of the blocks of measurements. Alternatively, a method for automatic gain control comprising the steps of applying a gain to a signal, computing a saturation rate of the signal and adjusting the gain based upon a difference between the saturation rate of the signal and a predetermined nonzero saturation rate.

Method and apparatus for automatic gain control for nonzero saturation rates

The recently introduced theory of Compressive Sensing (CS) enables a new method for signal recovery from incomplete information (a reduced set of 'compressive' linear measurements), based on the assumption that the signal is sparse in some dictionary. Such compressive measurement schemes are desirable in practice for reducing the costs of signal acquisition, storage, and processing. However, the current CS framework considers only a certain task (signal recovery) and only in a certain model setting (sparsity). We show that compressive measurements are in fact information scalable, allowing one to answer a broad spectrum of questions about a signal when provided only with a reduced set of compressive measurements. These questions range from complete signal recovery at one extreme down to a simple binary detection decision at the other. (Questions in between include, for example, estimation and classification.) We provide techniques such as a 'compressive matched filter' for answering several of these questions given the available measurements, often without needing to first reconstruct the signal. In many cases, these techniques can succeed with far fewer measurements than would be required for full signal recovery, and such techniques can also be computationally more efficient. Based on additional mathematical insight, we discuss information scalable algorithms in several model settings, including sparsity (as in CS), but also in parametric or manifold-based settings and in model-free settings for generic statements of detection, classification, and estimation problems.

Method and apparatus for signal detection, classification and estimation from compressive measurements

A method for recovering a signal by measuring the signal to produce a plurality of compressive sensing measurements, discarding saturated measurements from the plurality of compressive sensing measurements and reconstructing the signal from remaining measurements from the plurality of compressive sensing measurements. Alternatively, a method for recovering a signal comprising the steps of measuring a signal to produce a plurality of compressive sensing measurements, identifying saturated measurements in the plurality of compressive sensing measurements and reconstructing the signal from the plurality of compressive sensing measurements, wherein the recovered signal is constrained such that magnitudes of values corresponding to the identified saturated measurements are greater than a predetermined value.

Method and apparatus for signal reconstruction from saturated measurements

A method for approximating a plurality of digital signals or images using compressed sensing. In a scheme where a common component xof said plurality of digital signals or images an innovative component xof each of said plurality of digital signals each are represented as a vector with m entries, the method comprises the steps of making a measurement y, where ycomprises a vector with only nentries, where nis less than m, making a measurement yfor each of said correlated digital signals, where ycomprises a vector with only nentries, where nis less than m, and from each said innovation components y, producing an approximate reconstruction of each m-vector xusing said common component yand said innovative component y.

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Houston, TX, United States of America

Mark A Davenport