Presented By: Aerospace Engineering
Chair's Distinguished Lecture: Random Finite Set Multi-object Tracking and Sensor Control for Autonomous Aerospace Vehicles
Keith A. LeGrand
Ph.D. Candidate
Laboratory for Intelligent Systems and Controls
Cornell University
Multi-object tracking is the process of simultaneously estimating an unknown number of objects and their partially hidden states using unlabeled noisy measurement data. Common applications of multi-object tracking algorithms include space situational awareness (SSA), air traffic control, missile defense, and autonomous vehicles. In recent years, a new branch of statistical calculus known as random finite set (RFS) theory has provided a unifying formalism for rigorously defining multi-sensor multi-object tracking problems such that provably Bayes-optimal solutions may be found. This talk presents recent advances in RFS theory with applications to space-based SSA, airborne surveillance, and search and rescue. Additionally, this talk introduces a novel approach for incorporating negative information, such as the absence of detections, and evidence from ambiguous measurements, such as natural language statements, in multi-object tracking and control problems. Finally, this talk will present the latest application of RFS theory to intelligent sensor control, wherein multi-object information measures are formulated to autonomously manipulate an aerial sensor to search for and track multiple ground vehicles.
About the speaker...
Keith LeGrand is a Ph.D. candidate in the Laboratory for Intelligent Systems and Controls (LISC) at Cornell University and a National Defense Science and Engineering Graduate (NDSEG) Fellow. Prior to that, he was a Senior Member of Technical Staff at Sandia National Laboratories in Albuquerque, New Mexico where he conducted research in inertial navigation, space systems, and multi-object tracking. He received the B.S. and M.S. degrees in Aerospace Engineering from the Missouri University of Science and Technology. His research has been recognized by several best paper awards, including first and second place best student paper awards at the International Conference on Information Fusion in 2020 and 2021, and second place in the Frank J Redd Student Scholarship Competition at the Small Satellite conference in 2013. His research interests include multi-sensor multi-object tracking; autonomous aerospace systems; guidance, navigation, and control; and information theory.
https://umich.zoom.us/j/93096785925; Passcode: AE585
Ph.D. Candidate
Laboratory for Intelligent Systems and Controls
Cornell University
Multi-object tracking is the process of simultaneously estimating an unknown number of objects and their partially hidden states using unlabeled noisy measurement data. Common applications of multi-object tracking algorithms include space situational awareness (SSA), air traffic control, missile defense, and autonomous vehicles. In recent years, a new branch of statistical calculus known as random finite set (RFS) theory has provided a unifying formalism for rigorously defining multi-sensor multi-object tracking problems such that provably Bayes-optimal solutions may be found. This talk presents recent advances in RFS theory with applications to space-based SSA, airborne surveillance, and search and rescue. Additionally, this talk introduces a novel approach for incorporating negative information, such as the absence of detections, and evidence from ambiguous measurements, such as natural language statements, in multi-object tracking and control problems. Finally, this talk will present the latest application of RFS theory to intelligent sensor control, wherein multi-object information measures are formulated to autonomously manipulate an aerial sensor to search for and track multiple ground vehicles.
About the speaker...
Keith LeGrand is a Ph.D. candidate in the Laboratory for Intelligent Systems and Controls (LISC) at Cornell University and a National Defense Science and Engineering Graduate (NDSEG) Fellow. Prior to that, he was a Senior Member of Technical Staff at Sandia National Laboratories in Albuquerque, New Mexico where he conducted research in inertial navigation, space systems, and multi-object tracking. He received the B.S. and M.S. degrees in Aerospace Engineering from the Missouri University of Science and Technology. His research has been recognized by several best paper awards, including first and second place best student paper awards at the International Conference on Information Fusion in 2020 and 2021, and second place in the Frank J Redd Student Scholarship Competition at the Small Satellite conference in 2013. His research interests include multi-sensor multi-object tracking; autonomous aerospace systems; guidance, navigation, and control; and information theory.
https://umich.zoom.us/j/93096785925; Passcode: AE585
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