EE4760 Probablistic sensor fusion

1. FOUNDATIONS OF ESTIMATION THEORY. Basics of estimation theory, linear estimators, MLE, Bayesian inference, MMSE, MAP, recursive filtering, Wiener filtering, Kalman filtering,
2. NONLINEAR ESTIMATION. Extended Kalman filtering, unscented Kalman filtering, particle filtering, smoothing and its connection to nonlinear least squares.
3. GAUSSIAN PROCESSES. Background, interpretation, model assumptions, computational complexity, practical considerations, physics-inspired state space models.
4. DISTRIBUTED FILTERING: Distributed Kalman Filters and Distributed Gaussian Processes
5. PRACTICAL APPLICATIONS AND CHALLENGES IN NONLINEAR ESTIMATION. Hands-on application of sensor fusion and distribtuted algorithms on real world challenges e.g., localization, target tracking and field estimation.

Study Goals

At the end of the course you should be able to:

• Apply EKF / UKF / PF / Gaussian processes to a real-life data sequence
• Assess the results and compare between different algorithms and different settings
• Discuss and compare the results

Teachers

Last modified: 2026-02-08