Course Code
sfa
Duration
14 hours (usually 2 days including breaks)
Overview
Sensor Fusion is the combination and integration of data from multiple sensors to provide a more accurate, reliable and contextual view of data.
Sensor Fusion implementations require algorithms to filter and integrate different data sources.
Audience
This course is targeted at engineers, programmers and architects who deal with multi-sensor implementations.
Course Outline
Introduction to Sensor Fusion
- Sensor Fusion Overview
- Errors in Raw Data
Multisensor Fusion Architecture
- Architectural Taxonomy
- Centralized vs Decentralized
- Local vs Global Interaction
- Hierarchy
Fusion Methods
- Bayesian Networks
- Probabilistic Grids
- The Kalman Filter
- Markov chain Monte Carlo
- Alternatives to Probability
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