Anduril Industries, Inc.

Numerica Corporation

Shawn Michael Herman

Jason Kyle Johnson

Evan Jackson Everett

Dylan Scott Eustice

Jeffery Brian Poore

Philip Christopher Du Toit

Provided is a method for detecting the trajectories of one or more targets in the field of view of one or more sensors, the method comprising: receiving one or more sensor frames corresponding to the one or more sensors; defining a space of allowable target states for the one or more sensor frames; specifying a set of potential target trajectories, each comprising one allowable target state for each of the one or more sensor frames; specifying target signal parameters for each of the allowable target states, such that the target signal parameters predict the expected target signal contribution corresponding to the one or more sensor frames; specifying a data fidelity objective to quantify how well the target signal parameters match the one or more sensor frames; specifying a sequence of one or more sparsity objectives to penalize a number of detected targets; determine the trajectories of one or more targets as follows: obtain values for all the target signal parameters in all the sensor frames, the obtained values being initialized values or previously optimized values, for each sparsity objective of the sequence, starting with the obtained target signal parameters, determine new target signal parameters to optimize the sum of the data fidelity objective and the sparsity objective; and storing the final trajectories in memory.

Multi-target detection using convex sparsity prior

Systems and methods are described herein for dynamically generating and updating a patrol schedule for a shift based on historic demand event data, and a predicted-demand model based on the historic demand event data. The systems and methods also receive information associated with patrol officers assigned to a shift and constraints on the officers' availability, and generate a patrol schedule comprising patrol assignments for the assigned patrol officers optimized based on at least one policing objective. The patrol schedule may be dynamically updated based on changing information and provided to the patrol officer via mobile device or display in the patrol officer's vehicle.

Systems and methods for dynamically generating patrol schedules based on historic demand data

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Fort Collins, CO, United States of America

Shawn Michael Herman