Filtering estimation

The objective of this course is to introduce the Kalman filter and its variants as a tool for estimating the state of a dynamic system in the presence of measurement noise, and for filtering the measurements.

-Statistical analysis of the behavior of dynamic systems subjected to random disturbances.
-Bayesian estimation and optimal filtering equations
-Kalman filtering algorithms and their variants (extended Kalman filter and unscented Kalman filter).
-Filter properties and tuning.
-Selection and development of a model for filter synthesis based on objectives
-Application to engine load estimation,
-Application to sensorless motor control.

• Analog and Digital Electronics, RF and Microwave Electronics
• Electromagnetism and Propagation Phenomena in Free Space and Guided Waves
• Component Physics, Integrated Circuits, Packaging, Printed Circuit Boards (PCBs)
• Power Electronics and Converters