Department of Mechanical and Aerospace Engineering
An introductory course on learning and intelligent-systems techniques for the modeling, planning, and control of dynamic sensors. Methods for intelligent sensor fusion, sensor management, and mobile sensor navigation and control will be covered in detail in this course. Topics also include neural networks, Bayesian networks, and information theory, as they apply to problems drawn from environmental monitoring, sensor path planning, sensing-and-pursuit games, target tracking, classification, and satisficing searches.
Scope of the course
Motivating Sensing Applications
Sensor Field-of-View (FoV)
Environmental Variability and Feedback
Sensor Performance and Estimation
Probability of Detection and False Alarms
Sensor Placement, Navigation, and Control
Integrated Control and Navigation
Elements from the following topics will be reviewed throughout the semester:
Linear systems, systems theory, graph theory, probability theory, information theory, optimal control, and planning.
Silvia Ferrari and Tom A. Wettergren Information-driven Planning and Control, CRC Press, to appear in 2017
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