Principles of Estimation and Secure Sensor Data Processing

The participants learn to independently explore and understand a given topic and present it to the other participants in a concise and coherent way.

Intended Participants	Bachelor Students
Instructors	Marko Ristic, Benjamin Noack
SWS	2
Credits	3
Languages	English
Prerequisites	Understanding of linear algebra Understanding of probability and statistics Understanding of algorithms and data structures

Course Description

The processing of sensor-produced data is an important task in robotics. Mobile robots, autonomous vehicles, assistance programs and many more all rely on accurate estimates of their current state to make safe and logical decisions. As more of these systems are beginning to rely on open networks and cloud computing, the privacy of their data is also becoming an increasing concern. Securely processing this data and developing suitable estimation algorithms therefore plays an important role in the safe and reliable processing of sensor data.

This seminar covers the topics required for understanding the basics of state estimation and the security challenges that they can be faced with. Students will establish a common understing of this subject as well as be given individually assigned topics to study and present. Examples of covered topics include Kalman filtering, Gaussian noise, security challenges for public clouds and encrypted signal processing.

Registration

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