The Charles Stark Draper Laboratory, Inc.

Massachusetts Institute of Technology

Christopher C Yu

Yaron Rachlin

Juha-Pekka J Laine

Benjamin F Lane

Piotr Indyk

Robin M A Dawson

Brian Gardiner

Rishi Gupta

Eric Price

William W Whitacre

Rami S Mangoubi

Michael F McManus

Lei Hamilton

Steven Hoeschele

Patrick O'Shea

Timothy Galvin

Charles A McPherson

Local terrain feature location data is obtained from a local sensor device at a user location without a prior-known global position. The local terrain feature location data characterizes relative distances and directions to a plurality of local terrain features nearest to the user location. Global terrain feature location data stored in at least one hardware memory device is accessed. The global terrain feature location data characterizes relative distances and directions between a plurality of distinctive terrain features located in a defined terrain region in terms of absolute global location coordinates. The local terrain feature location data is compared to the global terrain feature location data to develop multiple pattern matching hypotheses, wherein each pattern matching hypothesis characterizes a likelihood of a subset of the local terrain features matching a subset the global terrain features. Global location coordinates for the user location is then determined from the pattern matching hypotheses.

Lost-in-forest GPS-denied positioning system

Systems and methods may identify at least a first set of right singular vectors and a first set of singular values of a subset of the matrix, reduce the subset by an amount of energy of a selected data entry of the subset based on the first set of right singular vectors and the first set of singular values, incorporate a new data entry from the matrix into the subset, update the first set of right singular vectors and the first set of singular values of the subset based on the new data entry by a singular value decomposition (SVD) update, and generate the low-rank approximation of the matrix based on the updated first set of right singular vectors and the updated first set of singular values.

Systems and methods for low-rank matrix approximation

Methods and apparatuses for compressive sensing that enable efficient recovery of features in an input signal based on acquiring a few measurements corresponding to the input signal. One method of compressive sensing includes folding an image to generate first and second folds, and recovering a feature of the image based on the first and second folds without reconstructing the image. One example of a compressive sensing apparatus includes a lens, a focal plane array coupled to the lens and configured to generate first and second folds based on the image, and a decoder configured to receive the first and second folds and to recover a feature of the image without reconstructing the image. The feature may be a local geometric feature or a corner. Compressive sensing methods and apparatuses for determining translation and rotation between two images are also disclosed.

Compressive sensing with local geometric features

A method and apparatus for estimating and compensating for a broad class of non-Gaussian sensor and process noise. In one example, a coded filter combines a dynamic state estimator (for example, a Kalman filter) and a non-linear estimator to provide approximations of the non-Gaussian process and sensor noise associated with a dynamic system. These approximations are used by the dynamic state estimator to correct sensor measurements or to alter the dynamic model governing evolution of the system state. Examples of coded filters leverage compressive sensing techniques in combination with error models based on concepts of compressibility and the application of efficient convex optimization processes.

Coded filter

A pixel array including circuitry for combining charges accumulated by individual pixels in the array enables addition and/or subtraction of individual pixel values, prior to their digitization, in the pixel array.

Systems and methods for adding or subtracting pixels

In general, in one embodiment, a starfield image as seen by an object is analyzed. Compressive samples are taken of the starfield image and, in the compressed domain, processed to remove noise. Stars in the starfield image are identified and used to determine an attitude of the object.

Attitude estimation by reducing noise with dragback

Attitude estimation in compressed domain

Attitude estimation with compressive sampling of starfield data

Growing community of inventors

Belmont, MA, United States of America

Christopher C Yu