Science Applications International Corporation

USA As Represented by Secretary of the Navy

Jay Scott Goldstein

Hanna Elizabeth Witzgall

Karl Robert Gerlach

Michael Picciolo

David Charles Ricks

Image pixel intensity data is transformed to a holographic representation of the image. A subset of the holographic representation is modeled. Model parameters constitute a compressed image representation. A two-dimensional Fourier transform can be applied to obtain the holographic image. Modeling includes applying an analysis portion of an adaptive analysis/synthesis prediction methodology to a subset of the holographic representation. Linear prediction can be the adaptive analysis/synthesis prediction methodology. Prior to modeling, one-dimensional Fourier transform can be performed on the holographic representation and the linear prediction is one-dimensional. Model parameters are preferably quantized. Embodiments include determining error between the model and the model's input data. There the compressed image representation the error, which also can be quantized. The subset of the holographic representation can be less than all the representation. The subset can be a plurality of complete rows; preferably substantially symmetric about 0 Hz.

Compression for holographic data and imagery

In a digital signal processor (DSP), input data is configured as a data matrix comprising data samples collected from an input signal. A weight vector is applied to the matrix, where the weight vector comprises three parts including (a) a rank reduction transformation produced by decomposition of data samples in a multistage Wiener filter having a plurality of stages, each stage comprising projection onto two subspaces. Each subsequent stage comprises projecting data transformed by the preceding second subspace onto each of a first subspace comprising a normalized cross-correlation vector at the previous stage and a second subspace comprising the null space of the normalized cross-correlation vector of the current stage, to form a reduced rank data matrix. Part (b) of the weight vector comprises minimizing mean squared error in the reduced rank data space. The output is a linear estimate of input data.

System and method for linear prediction

An adaptive signal processing system utilizes a Multistage Weiner Filter having an analysis section and a synthesis section that includes a processor. The processor includes an algorithm for generating a data adaptive linear transformation, computing an adaptive weighting wof the data, and applying the computed adaptive weighting wto a function of a main input signal and an auxiliary input signal to generate an output signal. A plurality of building blocks in a Gram-Schmidt cascaded canceller-type configuration sequentially decorrelate input signals from each other to produce a single filtered output signal. Each building block generates an adaptive weight wthat is applied to generate a local output signal. The effect of non-Gaussian noise contamination on the convergence MOE of the system is negligible. In addition, when desired signal components are included in weight training data they cause little loss of noise cancellation.

Multistage median cascaded canceller

A method for analyzing data, the data characterized by a set of scalars and a set of vectors, to analyze the data into components related by statistical correlations. In preferred embodiments, the invention includes steps or devices for, receiving a set of a scalars and a set of vectors as the inputs; calculating a correlation direction vector associated with the scalar and vector inputs; calculating the inner products of the input vectors with the correlation direction vector; multiplying the inner products onto the correlation direction vector to form a set of scaled correlation direction vectors; and subtracting the scaled correlation direction vectors from the input vectors to find the projections of the input vectors orthogonal to the correlation direction vector. The outputs are the set of scalar inner products and the set of vectors orthogonal to the correlation vector. The steps or devices can be repeated in cascade to form a multi-stage analysis of the data. The invention can also be used with a steering vector preceding the adaptive analysis stages.

System and method for adaptive filtering

In a digital signal processor (DSP), input data is configured as a data matrix comprising data samples collected from an input signal. A weight vector is applied to the data matrix, where the weight vector comprises three parts including (a) a rank reduction transformation produced by decomposition of the data samples in a multistage Wiener filter having a plurality of stages, each stage comprising projection onto two subspaces. Each subsequent stage comprises projecting data transformed by the preceding second subspace onto each of a first subspace comprising a normalized cross-correlation vector at the previous stage and a second subspace comprising the null space of the normalized cross-correlation vector of the current stage, to form a reduced rank data matrix. Part (b) of the weight vector comprises minimizing the mean squared error in the reduced rank data space. The output is a linear estimate of the input data. In one embodiment, further processing produces an estimate of the power spectral density of the input signal.

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Centerville, VA, United States of America

Jay Scott Goldstein