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MULTISENSOR FUSION  Architecture (US-JDL/UK-TFDF)  Feature Space (Data representations, Task-specific, feedback)  Dimensionality (Communication bandwidth.

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Presentation on theme: "MULTISENSOR FUSION  Architecture (US-JDL/UK-TFDF)  Feature Space (Data representations, Task-specific, feedback)  Dimensionality (Communication bandwidth."— Presentation transcript:

1 MULTISENSOR FUSION  Architecture (US-JDL/UK-TFDF)  Feature Space (Data representations, Task-specific, feedback)  Dimensionality (Communication bandwidth constraints, High  Low, increase SnR) Sensor 1 Sensor 2 Sensor Sensor 1 Sensor Sensor 3 Sensor 4 Centralised -Impractical -Not scalable -best Decentralised -Robust -scalable -Modular -Needs more complex algs - carries risk of rumour propagation

2 MULTISENSOR FUSION  Uncertainty  Dynamics  Data, sensor, communication noise, high level ignorance, model uncertainty  `soft’ decisions – Bayesian inference framework… but ….  Incorrect use of independence between models  Veto Effect  Inaccurate estimation of probabilities can lead to severe distortion of decisions (product rule dominated by low probability errors)  Simpler decision methods more robust  Fusion is an iterative dynamical process - Continually refining estimates, representations..

3 MULTISENSOR FUSION  How do constraints on communication bandwidth and processing limit architectures for fusion?  How does the brain create and modify its data representation?  How does the brain encode time, dynamics and use feedback?  How does the brain encode and process probabilities and uncertain knowledge? Apart from very low level (cellular/subcellular) and very high level binding, the brain appears to leave data sources fragmented. Why? (interesting clinical exception in synaesthesia! – do we learn ICA?) Effective Sensor Fusion requires key elements: How does the Brain deal with the same problems?


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