Multisensor fusion
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Sensor 1. +. Sensor 2. +. Sensor 3. +. Sensor 4. Sensor 1. +. Sensor 2. Sensor 3. MULTISENSOR FUSION. Centralised Impractical Not scalable best. Architecture (US-JDL/UK-TFDF) Feature Space (Data representations, Task-specific, feedback) Dimensionality

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MULTISENSOR FUSION

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Multisensor fusion

Sensor 1

+

Sensor 2

+

Sensor 3

+

Sensor 4

Sensor 1

+

Sensor 2

Sensor 3

MULTISENSOR FUSION

  • Centralised

  • Impractical

  • Not scalable

  • best

  • Architecture

    (US-JDL/UK-TFDF)

  • Feature Space

    (Data representations,

    Task-specific, feedback)

  • Dimensionality

    (Communication bandwidth constraints,

    High  Low, increase SnR)

  • Decentralised

  • Robust

  • scalable

  • Modular

  • Needs more complex algs

  • carries risk of rumour propagation


Multisensor fusion

MULTISENSOR FUSION

  • 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

  • Uncertainty

  • Dynamics

  • Fusion is an iterative dynamical process

  • - Continually refining estimates, representations ..


Multisensor fusion

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?

Effective Sensor Fusion requires key elements:

How does the Brain deal with the same problems?

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?)


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