Raytheon Company

USA as Represented by Secretary of the Navy

Bbn Technologies Corporation

Kenneth J McPhillips

Arnold W Novick

Michael F Janik

Paul Patrick Audi

John R Short

Kevin Paul Bongiovanni

Christopher S Fortin

James William Casalegno

Thomas J McHale

Ilya Rozenfeld

Kurt Gent

An autonomous sonar system and method provide an arrangement capable of beamforming in three dimensions, detecting loud targets, adaptively beamforming in three dimensions to avoid the loud targets, detecting quiet targets, localizing the loud or quiet targets in range, bearing, and depth, detecting modulation of noise associated with propellers of the loud or quiet targets, generating three dimensional tracks of the loud or quiet targets in bearing, range and depth, making classification of the loud or quiet targets, assigning probabilities to the classifications, and generating classification reports according to the classifications for communication to a receiving station, all without human assistance.

Autonomous sonar system and method

A to method and system provide a range and depth localization of a passive sound generator, e.g., a submarine target. The method and system use an autocorrelation and/or a cross correlation to arrive at a plurality of range versus depth estimates, which are resolved and evaluated using likelihood factors in order to provide a range and depth localization.

Methods and systems for passive range and depth localization

A multi-stage maximum likelihood target estimator for use with radar and sonar systems is provided. The estimator is a software implemented algorithm having four computational stages. The first stage provides angle smoothing for data endpoints thereby reducing angle errors associated with tie-down times. The second stage performs a coarse grid search to obtain the initial approximate target state to be used as a starting point for stagesand. The third stage is an endpoint Gauss-Newton type maximum likelihood target estimate which determines target range along two time lines. The final refinement of the target state is obtained by the fourth stage which is a Cartesian coordinate maximum likelihood target estimate. The four-stage processing allows the use of target historic data while reducing processing time and computation power requirement.

Multi-stage maximum likelihood target estimator

A system and method can detect and track targets in a scene of interest. Interframe differencing can be performed on sequential pairs of images of the scene and a Bayesian model analyzer can obtain an interframe difference density function. The interframe difference density function can be partitioned into static and mobile regimes to provide an objective function. A tracking module can construct a level set and geodesic active contours can be determined for the targets. An adaptive control can apply a spatial transformation and level set perturbation to the geodesic contours to oscillate the contours such that the level set can drop to a lower energy level. The expanded contour can be fed back for use in processing subsequent interframe differences. Target motion analysis data, such as bearing and bearing rate data, can be extracted from the geodesic active contours by applying geometric based transformations on the curve's coordinates.

Automatic target detection and motion analysis from image data

Growing community of inventors

Warren, RI, United States of America

Kenneth J McPhillips