The current state and future of Data Fusion

(a Meta-tracking perspective)

Simon Godsill

SigProC Laboratory

University of Cambridge

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Where we are today…

The community has made leaps and bounds since the early days of detection and tracking of single objects:

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We have sophisticated algorithms for fusion, detection and tracking of multiple objects under:

– Heavy clutter

– Complex nonlinear dynamics

– Multiple data asynchronous data streamsFUSION 2017

We can trade off computational complexity with accuracy and sophistication in the modelling

[Kalman filter, … Sigma-point filter, …Point process statistical models, … Particle filters…, Homotopy based methods, …,MCMC/ Particle MCMC]FUSIO

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Where are the gaps?

So, have we for practical purposes solved the problems of Data Fusion?

Happily (for us…) there are many outstanding issues that need to be solved:

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Challenges

`Fusion’ data is increasingly high-dimensional and multimodal, e.g.:

– High-resolution,

dynamic imagery:

– Textual and other `non-numeric’ data descriptions, social network/ internet data, …

– Evolving network/group

structured data:

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The questions

Not only are the data more challenging; we are increasingly being asked much more challenging questions:

– Determine full `situational awareness’

– Automated decision making/recommendation

– Learning of group/network behaviours

– `Intentionality’ analysis (where are you heading, what is your goal?)FUSIO

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Meta-tracking

We are now in the realms of `meta’-tracking: learning underlying structure and properties of many (interacting) entities, and accompanying probabilistic uncertainties

This also involves statistical inference for `Big’ data problemsFUSIO

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Future perspectives

In principle these things can be solved with probabilistic inference and decision theory (Bayes , ML,…)

However, the inference is large-scale (must be scalable), the objectives and the models are often under-specified (fuzzy).

Future work must focus on scalable, parallelisable large-scale inference – currently a major focus of the applied statistical community, but engineers must contribute too…

Combinations of Machine learning advances with signal processing fundamentals… Cannot currently expect Deep Learning etc. to learn all of the low-level representations/ inference techniques (but don’t always have huge data resources for training…)

Discussion…FUSION 2017