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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

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
slide2

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 ..
slide3

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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