T2 - Revision history

Faber at 09:37, 29 June 2016

2016-06-29T09:37:19Z

Faber at 08:27, 14 June 2016

2016-06-14T08:27:38Z

Faber at 12:54, 13 June 2016

2016-06-13T12:54:58Z

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 applications however, the equations describing the state propagation and/or that for the measurement process are non-linear. As
 long as those non-linearities remain mild in a certain sense, relatives of the Kalman filter suffice to provide suitable estimates
-with at least approximate consistency.
+with at least approximate consistency.<br />
 The proposed tutorial focusses on a variety of those relatives. It starts by recalling the basic relationships and properties of
 the Kalman filter. As a motivation for the later topics, its different optimality properties are shortly discussed as are various
 ways to write in particular its update equation (e. g., the Joseph form and the information form). The latter then leads over to
 the aspect of data fusion with convex combination (CC) and covariance intersection (CI) as two prominent approaches. The
-interacting multiple model (IMM) is presented as a method to deal with jump-Markovian linear systems.
+interacting multiple model (IMM) is presented as a method to deal with jump-Markovian linear systems.<br />
 Starting from those results for linear systems, the generalization to non-linear systems is discussed. The best linear unbiased
 estimator (BLUE) filter is introduced and general-purpose approximations like the extended Kalman filter (EKF), the unscented
 Kalman filter (UKF), and the Gauss filter (GF) are put in its context, detailed for polar measurements. For that same example,
 converted measurement (CM) filters are examined. While being straight-forward to implement, they come with the inherent
-problem of generating estimation bias. This problem and proposed solutions are investigated.
+problem of generating estimation bias. This problem and proposed solutions are investigated.<br />
 An alternative to converting the measurements to state-(sub-)space is given by a conversion of the states to measurement
 space. For the example of angular-only measurements, log-spherical coordinates (LSCs) are presented as such a suitable statespace

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 '''Length:''' 3 hours
-'''Intended Audience:'''
+'''Intended Audience and Prerequisites:''' The tutorial is intendend for listeners interested in non-linear state estimation problems and fundamental aspects of solving
-'''Description:'''
+'''Description:''' The Kalman filter is well-known as the workhorse of recursive state estimation. For linear state estimation problems
+under noisy measurements, the Kalman filter provides consistent estimates being optimal in various senses. In many tracking
-'''Prerequisites:'''
+applications however, the equations describing the state propagation and/or that for the measurement process are non-linear. As
 '''Presenter:''' [mailto:dietrich.fraenken@airbus.com Dietrich Fränken]
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