T12 - Revision history

Faber at 08:48, 29 June 2016

2016-06-29T08:48:13Z

Faber at 08:09, 1 April 2016

2016-04-01T08:09:04Z

Faber at 10:54, 21 March 2016

2016-03-21T10:54:39Z

Faber at 13:25, 24 February 2016

2016-02-24T13:25:12Z

Faber at 09:51, 24 February 2016

2016-02-24T09:51:39Z

Faber at 09:51, 24 February 2016

2016-02-24T09:51:11Z

Faber at 09:49, 24 February 2016

2016-02-24T09:49:56Z

Faber at 09:43, 24 February 2016

2016-02-24T09:43:38Z

Faber at 09:43, 24 February 2016

2016-02-24T09:43:22Z

Faber at 16:53, 23 February 2016

2016-02-23T16:53:53Z

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 <!--        FUSION 2016 Tutorials        -->
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 <!--        T12 Implementations of Random-Finite-Set-Based Multi-Target Filters        -->
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-[[File:Cd0b00_d92f75fa3e9445979ac32d86a85165bd.gif|120px|Conference Catalysts, LLC|link=http://www.conferencecatalysts.com/]][[File:Congress-and-more_logo.png|congress and morecongress and more|link=http://congressandmore.de/]]
+[[File:Cd0b00_d92f75fa3e9445979ac32d86a85165bd.gif|112px|Conference Catalysts, LLC|link=http://www.conferencecatalysts.com/]][[File:Blank.png|link=]][[File:Congress-and-more_logo.png|congress and morecongress and more|link=http://congressandmore.de/]]
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 | style="padding:2px;" | <h2 id="mp-tfa-h2" style="margin:3px; background:#ceecf2; font-family:inherit; font-size:120%; font-weight:bold; border:1px solid #a3babf; text-align:left; color:#000; padding:0.2em 0.4em;">T12 Implementations of Random-Finite-Set-Based Multi-Target Filters</h2>
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-| style="color:#000;" | <div id="mp-tfa" style="padding:2px 5px">'''Length:''' 3 hours (half day)
+| style="color:#000;" | <div id="mp-tfa" style="padding:2px 5px">'''Length:''' 3 hours
 '''Intended Audience:''' Anyone who is interested in multi-target tracking.

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 | style="color:#000;" | <div id="mp-tfa" style="padding:2px 5px">'''Length:''' 3 hours (half day)
-'''Intended Audience:'''Anyone who is interested in multi-target tracking.
+'''Intended Audience:''' Anyone who is interested in multi-target tracking.
 '''Description:''' The Finite Set Statistics framework for multi-sensor multi-target tracking has attached considerable interest in recent years. It provides a unified perspective of multi-target tracking in a very intuitive manner by drawing direct parallels with the simpler problem of single-target tracking. This framework has lead to the development of multi-target filters such as the Probability Hypothesis Density (PHD), Cardinalized PHD (CPHD), Multi-Bernoulli filters and recently, the Generalized Labeled Multi-Bernoulli filter. In this tutorial, we show how these filters are implemented and illustrate via Matlab how these filters work. In particular, the tutorial will present the implementations of<br />
@@ Line 19: / Line 19: @@
 Matlab code will be provided to the participants. It is envisaged that participants will come away with sufficient know-how to implement and apply these algorithms in their work.
-'''Prerequisites:'''Working knowledge of random variable, probability density function, Gaussian distribution, and concepts such as state space models. Taking Ron Mahler's companion tutorial " A Finite-Set Statistics Prime" is desirable. This tutorial is also a prerequisite for Stephan Reuter and Karl Granstrom's tutorial on extended target tracking.
+'''Prerequisites:''' Working knowledge of random variable, probability density function, Gaussian distribution, and concepts such as state space models. Taking Ron Mahler's companion tutorial " A Finite-Set Statistics Prime" is desirable. This tutorial is also a prerequisite for Stephan Reuter and Karl Granstrom's tutorial on extended target tracking.
 '''Presenter:''' [mailto:ba-ngu.vo@curtin.edu.au Ba-Ngu Vo] and Ba-Tuong Vo

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 | style="padding:2px;" | <h2 id="mp-tfa-h2" style="margin:3px; background:#ceecf2; font-family:inherit; font-size:120%; font-weight:bold; border:1px solid #a3babf; text-align:left; color:#000; padding:0.2em 0.4em;">T12 Implementations of Random-Finite-Set-Based Multi-Target Filters</h2>
 |-
-| style="color:#000;" | <div id="mp-tfa" style="padding:2px 5px">'''Length:''' 1/2 day<br />
+| style="color:#000;" | <div id="mp-tfa" style="padding:2px 5px">'''Length:''' 1/2 day
-'''Intended Audience:'''Anyone who is interested in multi-target tracking.<br />
 '''Description:''' The Finite Set Statistics framework for multi-sensor multi-target tracking has attached considerable interest in recent years. It provides a unified perspective of multi-target tracking in a very intuitive manner by drawing direct parallels with the simpler problem of single-target tracking. This framework has lead to the development of multi-target filters such as the Probability Hypothesis Density (PHD), Cardinalized PHD (CPHD), Multi-Bernoulli filters and recently, the Generalized Labeled Multi-Bernoulli filter. In this tutorial, we show how these filters are implemented and illustrate via Matlab how these filters work. In particular, the tutorial will present the implementations of<br />
 *(1) Single target tracking in clutter<br />
@@ Line 15: / Line 17: @@
 *(4) PHD and CPHD filters<br />
 *(5) Generalized Labeled Multi-Bernoulli filter.<br />
-Matlab code will be provided to the participants. It is envisaged that participants will come away with sufficient know-how to implement and apply these algorithms in their work.<br />
+Matlab code will be provided to the participants. It is envisaged that participants will come away with sufficient know-how to implement and apply these algorithms in their work.
-'''Prerequisites:'''Working knowledge of random variable, probability density function, Gaussian distribution, and concepts such as state space models. Taking Ron Mahler's companion tutorial " A Finite-Set Statistics Prime" is desirable. This tutorial is also a prerequisite for Stephan Reuter and Karl Granstrom's tutorial on extended target tracking.<br />
-'''Presenter:''' [mailto:ba-ngu.vo@curtin.edu.au Ba-Ngu Vo] and Ba-Tuong Vo<br />
+'''Prerequisites:'''Working knowledge of random variable, probability density function, Gaussian distribution, and concepts such as state space models. Taking Ron Mahler's companion tutorial " A Finite-Set Statistics Prime" is desirable. This tutorial is also a prerequisite for Stephan Reuter and Karl Granstrom's tutorial on extended target tracking.
 '''Ba-Ngu Vo''' received his B.Sc. degree in Pure Mathematics and B.E. degree in Electrical Engineering with first class honors in 1994, and PhD in 1997. He had held various research positions before joining the department of Electrical and Electronic Engineering at the University of Melbourne in 2000. In 2010, he joined the School of Electrical Electronic and Computer Engineering at the University of Western Australia as Winthrop Professor and Chair of Signal Processing. Currently he is Professor and Chair of Signals and Systems in the Department of Electrical and Computer Engineering at Curtin University. Prof. Vo is a recipient of the Australian Research Council’s inaugural Future Fellowship His research interests are Signal Processing, Systems Theory and Stochastic Geometry with emphasis on target tracking, robotics, computer vision and space situational awareness.

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 Both presenters were recipients of the 2010 Australian Museum DSTO Eureka Prize for "Outstanding Science in Support of Defence or National Security".
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