EECI08: State Estimation and Sensor Fusion - Revision history

Murray at 20:24, 1 March 2009

2009-03-01T20:24:31Z

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	{{eeci-sp08 header\|next=[[~~NCS~~: Packet-Based Estimation and Control\|Packet-Based Control]]\|prev=[[~~NCS~~: Optimization-Based Control\|Optimization-Based Control]]}}		{{eeci-sp08 header\|next=[[EECI08: Packet-Based Estimation and Control\|Packet-Based Control]]\|prev=[[EECI08: Optimization-Based Control\|Optimization-Based Control]]}}

	{{righttoc}}		{{righttoc}}

Murray: EECI: State Estimation and Sensor Fusion moved to EECI08: State Estimation and Sensor Fusion

2009-03-01T20:15:05Z

EECI: State Estimation and Sensor Fusion moved to EECI08: State Estimation and Sensor Fusion

← Older revision		Revision as of 20:15, 1 March 2009
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Murray: /* Additional Information */

2008-03-29T01:02:16Z

Additional Information

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	== Additional Information ==		== Additional Information ==
	* <p>[http://www.cs.unc.edu/~welch/kalman/ The Kalman Filter], G. Welch and G. Bishop. A webpage with many links on Kalman filter.</p>		* <p>[http://www.cs.unc.edu/~welch/kalman/ The Kalman Filter], G. Welch and G. Bishop. A webpage with many links on Kalman filter.</p>
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Murray at 01:02, 29 March 2008

2008-03-29T01:02:02Z

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 This lecture provides a review of key results in state estimation and sensor fusion that will be built upon in future lectures.  We briefly summarize Kalman filtering and and describe how to use Kalman filters to obtain optimal sensor fusion in a centralized setting.  We also discuss several variants that are of use in a computationally-rich, networked environment: information filters, moving horizon estimation and particle filters.  Extensions to networked sensors and distributed sensing are discussed in the follow two lectures.
-{| width=100% border=1
+==  Lecture Materials ==
 * Lecture slides: {{eeci-sp08 pdf|L6_fusion.pdf|State Estimation and Sensor Fusion}}
 * Lecture notes: {{ncsbook|fusion|Chapter 4 - State Estimation and Sensor Fusion}}
-====  Additional Information ====
+== Reading ==
-* {{obc08|Chapters 4-7}} - This is a set of notes from a course on optimization-based control that covers much of the background material required for this lecture, including random processes and Kalman filters.
+* <p>{{obc08|Chapters 4-7}} - This is a set of notes from a course on optimization-based control that covers much of the background material required for this lecture, including random processes and Kalman filters.</p>
 * <p>[http://www.cs.unc.edu/~welch/media/pdf/kalman_intro.pdf An Introduction to the Kalman Filter], G. Welch and G. Bishop. A brief introduction to the Kalman filter in discrete time. No proofs are given, but it is a good first read.</p>
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 * <p>[http://www.amazon.com/gp/product/0486439380/102-3301256-1504117?v=glance&n=283155 Optimal Filtering], B.D.O Anderson and J.B. Moore. Dover Books on Engineering, 2005. A reissue of a book from  1979. It contains a detailed mathematical presentation of filtering problems and the Kalman filter. A very good book.</p>

Murray: /* Further Reading */

2008-03-29T00:24:57Z

Murray: /* Additional Information */

2008-03-29T00:24:47Z

Additional Information

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	==== Additional Information ====		==== Additional Information ====
	* {{obc08\|Chapters 4-7}} - This is a set of notes from a course on optimization-based control that covers much of the background material required for this lecture, including random processes and Kalman filters.		* {{obc08\|Chapters 4-7}} - This is a set of notes from a course on optimization-based control that covers much of the background material required for this lecture, including random processes and Kalman filters.
			* <p>[http://www.cs.unc.edu/~welch/kalman/ The Kalman Filter], G. Welch and G. Bishop. A webpage with many links on Kalman filter.</p>
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Murray: /* Further Reading */

2008-03-29T00:24:31Z

Murray at 00:22, 29 March 2008

2008-03-29T00:22:40Z

← Older revision		Revision as of 00:22, 29 March 2008
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	==== Additional Information ====		==== Additional Information ====
			* {{obc08\|Chapters 4-7}} - This is a set of notes from a course on optimization-based control that covers much of the background material required for this lecture, including random processes and Kalman filters.

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	==== Further Reading ====		==== Further Reading ====

Murray at 15:59, 28 March 2008

2008-03-28T15:59:54Z

← Older revision		Revision as of 15:59, 28 March 2008
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	==== Lecture Materials ====		==== Lecture Materials ====
	* Lecture slides: {{eeci-sp08 pdf ~~placeholder~~\|~~L5_fusion~~.pdf\|State Estimation and Sensor Fusion}}		* Lecture slides: {{eeci-sp08 pdf\|L6_fusion.pdf\|State Estimation and Sensor Fusion}}
	* Lecture notes: {{ncsbook\|fusion\|Chapter 4 - State Estimation and Sensor Fusion}}		* Lecture notes: {{ncsbook\|fusion\|Chapter 4 - State Estimation and Sensor Fusion}}

Murray: NCS: State Estimation and Sensor Fusion moved to EECI: State Estimation and Sensor Fusion

2008-03-28T15:19:55Z

NCS: State Estimation and Sensor Fusion moved to EECI: State Estimation and Sensor Fusion

← Older revision		Revision as of 15:19, 28 March 2008
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← Older revision		Revision as of 00:24, 29 March 2008
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	* <p>[http://www.cs.unc.edu/~welch/kalman/kalmanPaper.html A New Approach to Linear Filtering and Prediction Problem], R.E. Kalman. ''Transactions of the ASME'', Series D, 1960. A classical paper. Still very readable. It uses different notation than the lecture, and present a different and more general proof. </p>		* <p>[http://www.cs.unc.edu/~welch/kalman/kalmanPaper.html A New Approach to Linear Filtering and Prediction Problem], R.E. Kalman. ''Transactions of the ASME'', Series D, 1960. A classical paper. Still very readable. It uses different notation than the lecture, and present a different and more general proof. </p>

			* <p>[http://www.amazon.com/gp/product/0486439380/102-3301256-1504117?v=glance&n=283155 Optimal Filtering], B.D.O Anderson and J.B. Moore. Dover Books on Engineering, 2005. A reissue of a book from 1979. It contains a detailed mathematical presentation of filtering problems and the Kalman filter. A very good book.</p>

← Older revision		Revision as of 00:24, 29 March 2008
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	==== Further Reading ====		==== Further Reading ====
			* <p>[http://www.cs.unc.edu/~welch/media/pdf/kalman_intro.pdf An Introduction to the Kalman Filter], G. Welch and G. Bishop. A brief introduction to the Kalman filter in discrete time. No proofs are given, but it is a good first read.</p>

			* <p>[http://en.wikipedia.org/wiki/Kalman_filter Wikipedia: Kalman Filter] A webpage that gives a proof and some applications.</p>

			* <p>[http://www.cs.unc.edu/~welch/kalman/kalmanPaper.html A New Approach to Linear Filtering and Prediction Problem], R.E. Kalman. ''Transactions of the ASME'', Series D, 1960. A classical paper. Still very readable. It uses different notation than the lecture, and present a different and more general proof. </p>