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Sensor Fusion Course

Activity: Participating in or organising an event (conference, measurement campaign, ...) › Attending a course without exam

Description

Topics covered by the course include:

Introduction on estimation theories and sensor fusion
Statistical analysis: Gaussian distributions, expectation operator, means and variances, maximum likelihood
Observers: Principle of internal model matching, using outputs to match internal model, full state observer, reduced state observer
Estimators: Linear Kalman Filter, Extended Kalman Filter, Unscented Kalman Filter, Adaptive Filter (IMM Filter), Information Filter, Particle Filter
Sensor integration architectures: central, hierarchical, and decentralised fusion architectures
Multiple sensor fusion: Covariance intersection, State-vector fusion (track-to-track fusion), Information fusion.
Period24 Nov 2025 → 3 Dec 2025
Event typeCourse
LocationCranfield, United KingdomShow on map
Degree of RecognitionInternational