Raytheon Company

At&t Intellectual Property I, LP

University of Florida Research Foundation Inc

Other

Michael J Hirsch

Mauricio Guilherme De Carvalho Resende

Panagote Miltiades Pardalos

Christopher R Eck

Eric D Hirsch

Joel Hirsch

David M Sudit

Rakesh Nagi

Francis J McNeal

Hector J Ortiz Pena

John T Geiss

A Large Scale LCD Display System comprising a plurality of individual image substrates in communication with a microprocessor which communicates a portion of a large overall image to be depicted by each constituent substrate such that the end result is a complete depiction of the large overall image.

LCD large-scale display

In certain embodiments, a method includes accessing first target information associated with a first target visible to a first UAV at a first time and accessing second target information, received from a neighboring UAV, associated with a second target visible to the neighboring UAV at the first time. The method further includes combining the first target information and the second target information to generate combined target information and determining, based on the combined target information, a predicted location for the first and second targets at a second time. The method further includes determining a planned route for the first UAV for the second time by solving an optimization problem, the optimization problem accounting for the determined predicted location for the first and second targets, kinematic constraints associated with the first UAV and the neighboring UAV, and line-of-sight constraints associated with the first UAV and the neighboring UAV.

Cooperative control of unmanned aerial vehicles for tracking targets

According to one embodiment, a pattern learning system includes a pattern learning tool that receives event messages in a sequential manner from multiple sensors and forms multiple sub-sequences that each includes a trigger event message, a consequence event message, and one or more intermediary event messages. The pattern learning tool then generates multiple graphs that each represents a sub-sequence of the plurality of event messages. The pattern learning tool then combines the graphs into a combined graph according to a type of each event message, and determines a causal sequence from the combined graph according to a heaviest weighted directed path from the trigger event message to the consequence event message.

Pattern learning system

A method for allocating resources includes receiving one or more parameters associated with an object of interest. At least one of the parameters corresponds to a probability that the object of interest is participating in a predetermined situation of interest. The method also includes calculating a plurality of values, based at least in part on the parameters, and selecting, based at least in part on the calculated values, one or more operations to be performed involving the object of interest. In addition the method includes generating an instruction based at least in part on the operation to be performed transmitting the instruction to an operational resource.

Optimization strategies for resource management and course of action analysis

Disclosed are method and apparatus for registering multiple sensors collecting data from multiple objects. Sensor registration is decomposed into a two-step procedure. The first step corrects systematic errors. The second step assigns objects measured by one sensor to objects measured by a second sensor. Systematic errors are corrected by generating the global minimum of a systematic error function. One embodiment for generating the global minimum uses a Continuous Greedy Randomized Adaptive Search Procedure.

Sensor registration by global optimization procedures

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St. Petersburg, FL, United States of America

Michael J Hirsch