Sensor Fusion Course

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.

Période	24 nov. 2025 → 3 déc. 2025
Type d'événement	Cours
Emplacement	Cranfield, Royaume-UniAfficher sur la carte
Degré de reconnaissance	International