Tutorials - Revision history

Faber at 12:35, 6 September 2016

2016-09-06T12:35:31Z

Faion at 15:27, 1 August 2016

2016-08-01T15:27:28Z

Faber at 12:00, 18 July 2016

2016-07-18T12:00:03Z

Faber at 11:59, 18 July 2016

2016-07-18T11:59:03Z

Faion at 10:25, 18 July 2016

2016-07-18T10:25:23Z

Faber at 09:39, 29 June 2016

2016-06-29T09:39:22Z

Faber at 08:44, 14 June 2016

2016-06-14T08:44:44Z

Faber at 12:50, 13 June 2016

2016-06-13T12:50:08Z

Faber at 12:39, 13 June 2016

2016-06-13T12:39:42Z

Show changes

Faber at 09:26, 1 April 2016

2016-04-01T09:26:18Z

@@ Line 163: / Line 163: @@
 |-
 | style="color:#000;" | <div id="mp-tfa" style="padding:2px 5px">
-'''Presenter:''' [mailto:benjamin.noack@kit.edu Benjamin Noack] and [mailto:florian.pfaff@kit.edu Florian Pfaff]<br />
+'''Presenter:''' [mailto:florian.pfaff@kit.edu Florian Pfaff] and [mailto:benjamin.noack@kit.edu Benjamin Noack]<br />
 '''Length:''' 3 hours<br />
 '''Brief description:''' The tutorial covers various Kalman-filter-like estimators when the observed process has high uncertainties and therefore cannot be captured accurately with a linear models and perfectly selected noise distributions. The tutorial focusses on hybrid estimators relying on the combination of stochastic and set-membership approaches. Several solutions will be presented during this tutorial along with new challenges showing the versatility of hybrid estimation.<br />

@@ Line 57: / Line 57: @@
 |-
 |style="text-align:center;background-color:#e7deef;" | '''Morning'''<br />
-'''08:00–11:00'''
+'''08:30–11:30'''
 |style="text-align:center;background-color:#f7eff7;" | '''[[Tutorials#tutorial7| T7]]'''<br />
 Extended Object Tracking: Theory and Applications<br />
@@ Line 69: / Line 69: @@
 |-
-|style="width:7em;text-align:center;background-color:#e7deef;"| '''11:00–11:30'''
+|style="width:7em;text-align:center;background-color:#e7deef;"| '''11:30–12:30'''
 |colspan="9" style="width:15em;text-align:center;background-color:#f7eff7;"|
 '''Lunch break'''<br />
@@ Line 75: / Line 75: @@
 |-
 |style="text-align:center;background-color:#e7deef;" | '''Mid Day'''
-'''11:30–14:30'''
+'''12:30–15:30'''
 |style="text-align:center;background-color:#f7eff7;" | '''[[Tutorials#tutorial8| T8A]]'''<br />
 Proactive Optimal Control to Infer Information Faster<br />
@@ Line 87: / Line 87: @@
 |-
-|style="width:7em;text-align:center;background-color:#e7deef;"| '''14:30–15:00'''
+|style="width:7em;text-align:center;background-color:#e7deef;"| '''15:30–16:00'''
 |colspan="9" style="width:15em;text-align:center;background-color:#f7eff7;"|
 '''Coffee/tea break'''<br />
@@ Line 93: / Line 93: @@
 |-
 |style="text-align:center;background-color:#e7deef;" | '''Afternoon'''<br />
-'''15:00–18:00'''
+'''16:00–19:00'''
 |style="text-align:center;background-color:#f7eff7;" | '''[[Tutorials#tutorial8| T8B]]'''<br />
 Proactive Optimal Control to Infer Information Faster<br />
@@ Line 105: / Line 105: @@
 |-
-|style="width:7em;text-align:center;background-color:#e7deef;"| '''18:00–20:30'''
+|style="width:7em;text-align:center;background-color:#e7deef;"| '''19:00–21:00'''
 |colspan="9" style="width:15em;text-align:center;background-color:#f7eff7;"|
 '''Welcome Reception'''<br />

@@ Line 1: / Line 1: @@
 <!--        CALL FOR PAPERS        -->
 {| id="mp-upper" style="width: 100%; margin:4px 0 0 0; background:none; border-spacing: 0px;"
 <!--        CFP Download        -->
 | class="MainPageBG" style="width:100%; border:1px solid #bdd6c6; background:#e7f7e7; vertical-align:top; color:#000;" |

← Older revision		Revision as of 11:59, 18 July 2016
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	\|-		\|-

		+	<!-- MFI 2016 Tutorial Time Table -->
		+	{\| id="mp-upper" style="width: 100%; margin:4px 0 0 0; background:none; border-spacing: 0px;"
		+	<!-- MFI 2016 Tutorial Time Table -->
		+	<div id="timetable"></div>
		+	\| class="MainPageBG" style="width:100%; border:1px solid #d6bdde; background:#f7eff7; vertical-align:top; color:#000;" \|
		+	{\| id="mp-left" style="width:100%; vertical-align:top; background:#e7f7e76;"
		+	\| style="padding:2px;" \| <h2 id="mp-tfa-h2" style="margin:4px; background:#e7deef; font-family:inherit; font-size:120%; font-weight:bold; border:1px solid #d6bdde; text-align:left; color:#000; padding:0.2em 0.4em;">Time Table for the Tutorials Day on Monday, September 19, 2016</h2>
		+	\|-
		+	\| style="color:#000;" \| <div id="mp-tfa" style="padding:2px 5px">
		+	{\| class="wikitable" style="text-align:center;background-color:#e7deef;"
		+	! style="width:8em;text-align:center;background-color:#e7deef;"\|
		+	! style="width:15em;text-align:center;background-color:#e7deef;"\|'''Room A'''
		+	! style="width:15em;text-align:center;background-color:#e7deef;"\|'''Room B'''
		+	! style="width:15em;text-align:center;background-color:#e7deef;"\|'''Room C'''
		+	\|-
		+	! style="width:8em;text-align:center;background-color:#e7deef;"\|'''Topic'''
		+	! style="width:15em;text-align:center;background-color:#e7deef;"\|'''Tracking & Control'''
		+	! style="width:15em;text-align:center;background-color:#e7deef;"\|'''Sensor MFI'''
		+	! style="width:15em;text-align:center;background-color:#e7deef;"\|'''State Estimation'''
		+	\|-
		+	\|style="text-align:center;background-color:#e7deef;" \| '''Morning'''<br />
		+	'''08:00–11:00'''
		+	\|style="text-align:center;background-color:#f7eff7;" \| '''[[Tutorials#tutorial7\| T7]]'''<br />
		+	Extended Object Tracking: Theory and Applications<br />
		+	''K. Granström''
		+	\|style="text-align:center;background-color:#f7eff7;" \| '''[[Tutorials#tutorial4\| T4]]'''<br />
		+	Multisensor MFI in Automated driving<br />
		+	''J. Elfring''
		+	\|style="text-align:center;background-color:#f7eff7;" \| '''[[Tutorials#tutorial1\| T1]]'''<br />
		+	Introduction to distributed event-based state estimation<br />
		+	''S. Trimpe''
		+
		+	\|-
		+	\|style="width:7em;text-align:center;background-color:#e7deef;"\| '''11:00–11:30'''
		+	\|colspan="9" style="width:15em;text-align:center;background-color:#f7eff7;"\|
		+	'''Lunch break'''<br />
		+
		+	\|-
		+	\|style="text-align:center;background-color:#e7deef;" \| '''Mid Day'''
		+	'''11:30–14:30'''
		+	\|style="text-align:center;background-color:#f7eff7;" \| '''[[Tutorials#tutorial8\| T8A]]'''<br />
		+	Proactive Optimal Control to Infer Information Faster<br />
		+	''R. Urniezius''
		+	\|style="text-align:center;background-color:#f7eff7;" \| '''[[Tutorials#tutorial5\| T5]]'''<br />
		+	MFI and Bayesian Reasoning with Subjective Logic<br />
		+	''A. Josang''
		+	\|style="text-align:center;background-color:#f7eff7;" \| '''[[Tutorials#tutorial2\| T2]]'''<br />
		+	Relatives of the Kalman Filter for Tracking and MFI<br />
		+	''D. Fränken''
		+
		+	\|-
		+	\|style="width:7em;text-align:center;background-color:#e7deef;"\| '''14:30–15:00'''
		+	\|colspan="9" style="width:15em;text-align:center;background-color:#f7eff7;"\|
		+	'''Coffee/tea break'''<br />
		+
		+	\|-
		+	\|style="text-align:center;background-color:#e7deef;" \| '''Afternoon'''<br />
		+	'''15:00–18:00'''
		+	\|style="text-align:center;background-color:#f7eff7;" \| '''[[Tutorials#tutorial8\| T8B]]'''<br />
		+	Proactive Optimal Control to Infer Information Faster<br />
		+	''R. Urniezius''
		+	\|style="text-align:center;background-color:#f7eff7;" \| '''[[Tutorials#tutorial6\| T6]]'''<br />
		+	Passive Surveillance - Advanced Algorithms and Challenging Applications<br />
		+	''W. Koch''
		+	\|style="text-align:center;background-color:#f7eff7;" \| '''[[Tutorials#tutorial3\| T3]]'''<br />
		+	Robust Kalman Filtering<br />
		+	''F. Pfaff''
		+
		+	\|-
		+	\|style="width:7em;text-align:center;background-color:#e7deef;"\| '''18:00–20:30'''
		+	\|colspan="9" style="width:15em;text-align:center;background-color:#f7eff7;"\|
		+	'''Welcome Reception'''<br />
		+	\|}
		+
		+	\|-
		+	\|}
		+	\| style="border:1px solid transparent;" \|<br />
		+	\|-

	<!-- MFI 2016 Tutorials -->		<!-- MFI 2016 Tutorials -->

@@ Line 27: / Line 27: @@
 * [[Tutorials#tutorial3| T3 Robust Kalman filtering]]
 * [[Tutorials#tutorial4| T4 Multisensor fusion in Automated driving]]
-* [[Tutorials#tutorial5| T5 Fusion and Bayesian Reasoning with Subjective Logic.]]
+* [[Tutorials#tutorial5| T5 Fusion and Bayesian Reasoning with Subjective Logic]]
 * [[Tutorials#tutorial6| T6 Passive Surveillance - Advanced Algorithms and Challenging Applications]]
 * [[Tutorials#tutorial7| T7 Extended object tracking: Theory and applications]]
@@ Line 118: / Line 118: @@
 {| id="mp-upper" style="width: 100%; margin:4px 0 0 0; background:none; border-spacing: 0px;"
 <div id="tutorial5"></div>
-<!--        T5 Fusion and Bayesian Reasoning with Subjective Logic.        -->
+<!--        T5 Fusion and Bayesian Reasoning with Subjective Logic        -->
 | class="MainPageBG" style="width:100%; border:1px solid #d6bdde; background:#f7eff7; vertical-align:top; color:#000;" |
 {| id="mp-left" style="width:100%; vertical-align:top; background:#e7f7e76;"
-| style="padding:2px;" | <h2 id="mp-tfa-h2" style="margin:4px; background:#e7deef; font-family:inherit; font-size:120%; font-weight:bold; border:1px solid #d6bdde; text-align:left; color:#000; padding:0.2em 0.4em;">T5 Fusion and Bayesian Reasoning with Subjective Logic.</h2>
+| style="padding:2px;" | <h2 id="mp-tfa-h2" style="margin:4px; background:#e7deef; font-family:inherit; font-size:120%; font-weight:bold; border:1px solid #d6bdde; text-align:left; color:#000; padding:0.2em 0.4em;">T5 Fusion and Bayesian Reasoning with Subjective Logic</h2>
 |-
 | style="color:#000;" | <div id="mp-tfa" style="padding:2px 5px">

← Older revision		Revision as of 09:39, 29 June 2016
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		+	{{Organisation}}
	__NOTOC____NOEDITSECTION__		__NOTOC____NOEDITSECTION__

@@ Line 143: / Line 143: @@
 |-
 | style="color:#000;" | <div id="mp-tfa" style="padding:2px 5px">
-'''Presenter:''' [mailto: W.Koch@ieee.org Wolfgang Koch ]<br />
+'''Presenter:''' [mailto:W.Koch@ieee.org Wolfgang Koch]<br />
 '''Length:''' 3 hours<br />
 '''Brief description:''' The tutorial covers partly material of the recently published book of the presenter (Tracking and Data Fusion, Springer 2014) and thus provides a guided introduction to deeper reading with a particular focus on passive surveillance. Starting point is the well-known JDL model of sensor data and information fusion that provides general orientation within the world of fusion methodologies and its various applications, covering a dynamically evolving field of ever increasing relevance. Using the JDL model as a guiding principle, the tutorial introduces into advanced fusion technologies based on practical examples taken from real world applications.<br />