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Benjamin Noack

	Dipl.-Inform. Research Assistant, Ph.D. Student
	Address:	Karlsruher Institut für Technologie Institut für Anthropomatik Gebäude 50.20 Raum 134 Adenauerring 2 D-76131 Karlsruhe
	Walk-in hours:	on arrangement
	Phone:	+49-721-608-44024
	E-mail:	Benjamin.Noack@kit.edu

Academic Career

Research Interests

• Nonlinear state estimation
• Sensor networks
• Distributed estimation and information fusion
• Imprecise probabilities, credal sets, interval probabilities

DFG Research Training Group 1194 "Self-organizing Sensor-Actuator-Networks"

Bachelor/Master Thesis Subjects

In German

Publications

Alessio Benavoli, Benjamin Noack,
Pushing Kalman's Idea to the Extremes,
Proceedings of the 15th International Conference on Information Fusion (Fusion 2012) (to appear).

Proceedings of the 15th International Conference on Information Fusion (Fusion 2012) (to appear), Singapore, July, 2012.

Author : Benjamin Noack, Florian Pfaff, Uwe D. Hanebeck
Title : Combined Stochastic and Set-membership Information Filtering in Multisensor Systems (preliminary title)
In : Proceedings of the 15th International Conference on Information Fusion (Fusion 2012) (to appear)
Date : July 2012

Proceedings of the 15th International Conference on Information Fusion (Fusion 2012) (to appear), Singapore, July, 2012.

Author : Marc Reinhardt, Benjamin Noack, Uwe D. Hanebeck
Title : An Extension to Exact T2TF for Consistent Distributed Data Fusion (preliminary title)
In : Proceedings of the 15th International Conference on Information Fusion (Fusion 2012) (to appear)
Date : July 2012

Proceedings of the 15th International Conference on Information Fusion (Fusion 2012) (to appear), Singapore, July, 2012.

Author : Marc Reinhardt, Benjamin Noack, Uwe D. Hanebeck
Title : Closed-form Optimization of Covariance Intersection for Low-Dimensional Matrices (preliminary title)
In : Proceedings of the 15th International Conference on Information Fusion (Fusion 2012) (to appear)
Date : July 2012

Proceedings of the 50th IEEE Conference on Decision and Control (CDC 2011), Orlando, Florida, USA, December, 2011.

Author : Marcus Baum, Benjamin Noack, Uwe D. Hanebeck
Title : Random Hypersurface Mixture Models for Tracking Multiple Extended Objects
In : Proceedings of the 50th IEEE Conference on Decision and Control (CDC 2011)
Date : December 2011

Abstract

This paper presents a novel method for tracking multiple extended objects.
The shape of a single extended object is modeled with a recently developed approach called Random
Hypersurface Model (RHM) that assumes a varying number of measurement sources to lie on scaled versions
of the shape boundaries. This approach is extended by introducing a so-called Mixture Random Hypersurface Model
(Mixture RHM), which allows for modeling multiple extended targets. Based on this model, a Gaussian-assumed
Bayesian tracking method that provides the means to track and estimate shapes of multiple extended targets is derived.
Simulations demonstrate the performance of the new approach.

Proceedings of the 18th IFAC World Congress (IFAC 2011), Milan, Italy, August, 2011.

Author : Benjamin Noack, Marcus Baum, Uwe D. Hanebeck
Title : Automatic Exploitation of Independencies for Covariance Bounding in Fully Decentralized Estimation
In : Proceedings of the 18th IFAC World Congress (IFAC 2011)
Date : August 2011

Abstract

Especially in the field of sensor networks and multi-robot systems, fully decentralized estimation techniques are of particular interest.
As the required elimination of the complex dependencies between estimates generally yields inconsistent results, several approaches, e.g., covariance intersection,
maintain consistency by providing conservative estimates. Unfortunately, these estimates are often too conservative and therefore, much less informative than a corresponding
centralized approach. In this paper, we provide a concept that conservatively decorrelates the estimates while bounding the unknown correlations as closely as possible.
For this purpose, known independent quantities, such as measurement noise, are explicitly identified and exploited. Based on tight covariance bounds,
the new approach allows for an intuitive and systematic derivation of appropriate tailor-made filter equations and does not require heuristics.
Its performance is demonstrated in a comparative study within a typical SLAM scenario.

Proceedings of the 14th International Conference on Information Fusion (Fusion 2011), Chicago, Illinois, USA, July, 2011.

Author : Marcus Baum, Benjamin Noack, Frederik Beutler, Dominik Itte, Uwe D. Hanebeck
Title : Optimal Gaussian Filtering for Polynomial Systems Applied to Association-free Multi-Target Tracking
In : Proceedings of the 14th International Conference on Information Fusion (Fusion 2011)
Date : July 2011

Abstract

This paper is about tracking multiple targets with the so-called Symmetric Measurement Equation (SME) filter. The SME filter uses symmetric functions, e.g.,
symmetric polynomials, in order to remove the data association uncertainty from the measurement equation. By this means, the data association problem is converted
to a nonlinear state estimation problem. In this work, an efficient optimal Gaussian filter based on analytic moment calculation for discrete-time multi-dimensional polynomial systems
corrupted with Gaussian noise is derived, and then applied to the polynomial system resulting from the SME filter. The performance of the new method is compared to an UKF implementation
by means of typical multiple target tracking scenarios.

Proceedings of the 14th International Conference on Information Fusion (Fusion 2011), Chicago, Illinois, USA, July, 2011.

Author : Benjamin Noack, Marcus Baum, Uwe D. Hanebeck
Title : Covariance Intersection in Nonlinear Estimation Based on Pseudo Gaussian Densities
In : Proceedings of the 14th International Conference on Information Fusion (Fusion 2011)
Date : July 2011

Abstract

Many modern fusion architectures are designed to process and fuse data in networked systems. %local nodes can operate independently
Alongside the advantages, such as scalability and robustness, distributed fusion techniques particularly have to tackle the problem of dependencies between locally processed data.
In linear estimation problems, uncertain quantities with unknown cross-correlations can be fused by means of the covariance intersection algorithm, which avoids overconfident
fusion results. However, for nonlinear system dynamics and sensor models perturbed by arbitrary noise, it is not only a problem to characterize and parameterize dependencies
between estimates, but also to find a proper notion of consistency. This paper addresses these issues by transforming the state estimates to a different state space, where the
corresponding densities are Gaussian and only linear dependencies between estimates, i.e., correlations, can arise.
These pseudo Gaussian densities then allow the notion of covariance consistency to be used in distributed nonlinear state estimation.

Proceedings of the 14th International Conference on Information Fusion (Fusion 2011), Chicago, Illinois, USA, July, 2011.

Author : Marc Reinhardt, Benjamin Noack, Marcus Baum, Uwe D. Hanebeck
Title : Analysis of Set-theoretic and Stochastic Models for Fusion under Unknown Correlations
In : Proceedings of the 14th International Conference on Information Fusion (Fusion 2011)
Date : July 2011

Abstract

In data fusion theory, multiple estimates are combined to yield an optimal result.
In this paper, the set of all possible results is investigated, when two random variables with unknown correlations are fused.
As a first step, recursive processing of the set of estimates is examined.
Besides set-theoretic considerations, the lack of knowledge about the unknown correlation coefficient is modeled as a stochastic quantity.
Especially, a uniform model is analyzed, which provides a new optimization criterion for the covariance intersection algorithm in scalar state spaces.
This approach is also generalized to multi-dimensional state spaces in an approximative, but fast and scalable way, so that consistent estimates are obtained.

Proceedings of the 2011 American Control Conference (ACC 2011), San Francisco, California, USA, June, 2011.

Author : Benjamin Noack, Daniel Lyons, Matthias Nagel, Uwe D. Hanebeck
Title : Nonlinear Information Filtering for Distributed Multisensor Data Fusion
In : Proceedings of the 2011 American Control Conference (ACC 2011)
Date : June 2011

Abstract

The information filter has evolved into a key tool for distributed and decentralized multisensor estimation and control.
Essentially, it is an algebraical reformulation of the Kalman filter and provides estimates on the information about an uncertain state rather than on a state itself. Whereas many practicable Kalman filtering techniques for nonlinear system and sensor models have been developed, approaches towards nonlinear information filtering are still scarce and limited.
In order to deal with nonlinear systems and sensors, this paper derives an approximation technique for arbitrary probability densities that provides the same distributable fusion structure as the linear information filter.
The presented approach not only constitutes a nonlinear version of the information filter, but it also points the direction to a Hilbert space structure on probability densities, whose vector space operations correspond to the fusion and weighting of information.

Proceedings of the 8th European Conference on Wireless Sensor Networks (EWSN 2011), 6567:147-162, Bonn, Germany, February, 2011.

URL

Author : Johannes Schmid, Frederik Beutler, Benjamin Noack, Uwe D. Hanebeck, Klaus D. Müller-Glaser
Title : An Experimental Evaluation of Position Estimation Methods for Person Localization in Wireless Sensor Networks
In : Proceedings of the 8th European Conference on Wireless Sensor Networks (EWSN 2011)
Date : February 2011

Abstract

In this paper, the localization of persons by means of a Wireless
Sensor Network (WSN) is considered. Persons carry on-body sensor
nodes and move within a WSN. The location of each person is calculated
on this node and communicated through the network to a central data
sink for visualization. Applications of such a system could be found in
mass casualty events, firefighter scenarios, hospitals or retirement homes for example.
For the location estimation on the sensor node, three derivatives of the
Kalman Filter and a closed-form solution (CFS) are applied, compared,
and evaluated in a real-world scenario. A prototype 65-node ZigBee WSN
is implemented and data are collected in in- and outdoor environments
with differently positioned on-body nodes. The described estimators are
then evaluated off-line on the experimentally collected data.
The goal of this paper is to present a comprehensive real-world evaluation of methods for
person localization in a WSN based on received signal strength (RSS) range measurements.
It is concluded that person localization in in- and outdoor environments is possible
under the considered conditions with the considered filters. The compared methods
allow for suffciently accurate localization results and are robust against
inaccurate range measurements.

tm - Technisches Messen, Oldenbourg Verlag, 77(10):544-550, October, 2010.

URL

Author : Benjamin Noack, Vesa Klumpp, Daniel Lyons, Uwe D. Hanebeck
Title : Modellierung von Unsicherheiten und Zustandsschätzung mit Mengen von Wahrscheinlichkeitsdichten
In : tm - Technisches Messen, Oldenbourg Verlag
Date : October 2010

Abstract

Die systematische Behandlung von Unsicherheiten stellt eine wesentliche
Herausforderung in der Informationsfusion dar. Einerseits müssen
geeignete Darstellungsformen für die Unsicherheiten bestimmt
werden und andererseits darauf aufbauend effiziente Schätzverfahren
hergeleitet werden. Im Allgemeinen wird zwischen stochastischen und
mengenbasierten Unsicherheitsbeschreibungen unterschieden. Dieser
Beitrag stellt ein Verfahren zur Zustandsschätzung vor, welches
simultan stochastische und mengenbasierte Fehlergrößen berücksichtigen
kann, indem unsichere Größen nicht mehr durch eine einzelne
Wahrscheinlichkeitsdichte, sondern durch eine Menge von Dichten repräsentiert
werden. Besonderes Augenmerk liegt hier auf den Vorteilen und Anwendungsmöglichkeiten
dieser Unsicherheitsbeschreibung.

Proceedings of the 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2010), Taipei, Taiwan, October, 2010.

Author : Evgeniya Bogatyrenko, Benjamin Noack, Uwe D. Hanebeck
Title : Reliable Estimation of Heart Surface Motion under Stochastic and Unknown but Bounded Systematic Uncertainties
In : Proceedings of the 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2010)
Date : October 2010

Abstract

A reliable estimation of heart surface motion
is an important prerequisite for the synchronization of surgical
instruments in robotic beating heart surgery. In general, only
an imprecise description of the heart dynamics and measurement
systems is available. This means that the estimation of heart
motion is corrupted by stochastic and systematic uncertainties.
Without consideration of these uncertainties, the obtained results
will be inaccurate and a safe robotic operation cannot be guaranteed.
Until now, existing approaches for estimating the motion of the
heart surface are either deterministic or treat only stochastic
uncertainties. The proposed method extends the heart motion
estimation to the simultaneous consideration of stochastic and
unknown but bounded systematic uncertainties. It computes dynamic
bounds in order to provide the surgeon with a guidance by
constraining the motion of the surgical instruments and thereby
protecting sensitive tissue.

Proceedings of the IEEE Multi-Conference on Systems and Control (MSC 2010), Yokohama, Japan, September, 2010.

Author : Achim Hekler, Daniel Lyons, Benjamin Noack, Uwe D. Hanebeck
Title : Nonlinear Model Predictive Control Considering Stochastic and Systematic Uncertainties with Sets of Densities
In : Proceedings of the IEEE Multi-Conference on Systems and Control (MSC 2010)
Date : September 2010

Abstract

In Model Predictive Control, the quality of control
is highly dependent upon the model of the system under control.
Therefore, a precise deterministic model is desirable. However,
in real-world applications, modeling accuracy is typically limited
and systems are generally affected by disturbances. Hence,
it is important to systematically consider these uncertainties
and to model them correctly. In this paper, we present a
novel Nonlinear Model Predictive Control method for systems
affected by two different types of perturbations that are
modeled as being either stochastic or unknown but bounded
quantities. We derive a formal generalization of the Nonlinear
Model Predictive Control principle for considering both types
of uncertainties simultaneously, which is achieved by using
sets of probability densities. In doing so, a more robust and
reliable control is obtained. The capabilities and benefits of
our approach are demonstrated in real-world experiments with
miniature walking robots.

Proceedings of the 13th International Conference on Information Fusion (Fusion 2010), Edinburgh, United Kingdom, July, 2010.

Author : Marcus Baum, Benjamin Noack, Uwe D. Hanebeck
Title : Extended Object and Group Tracking with Elliptic Random Hypersurface Models
In : Proceedings of the 13th International Conference on Information Fusion (Fusion 2010)
Date : July 2010

Abstract

This paper provides new results and
insights for tracking an extended target object
modeled with an Elliptic Random Hypersurface Model (RHM).
An Elliptic RHM specifies the relative squared Mahalanobis
distance of a measurement source to the center of the
target object by means of a one-dimensional random scaling
factor. It is shown that uniformly distributed measurement
sources on an ellipse lead to a uniformly distributed
squared scaling factor. Furthermore, a Bayesian inference
mechanisms tailored to elliptic shapes is introduced, which
is also suitable for scenarios with high measurement noise.
Closed-form expressions for the measurement update in case
of Gaussian and uniformly distributed squared scaling factors are derived.

Proceedings of the 13th International Conference on Information Fusion (Fusion 2010), Edinburgh, United Kingdom, July, 2010.

Author : Vesa Klumpp, Benjamin Noack, Marcus Baum, Uwe D. Hanebeck
Title : Combined Set-Theoretic and Stochastic Estimation: A Comparison of the SSI and the CS Filter
In : Proceedings of the 13th International Conference on Information Fusion (Fusion 2010)
Date : July 2010

Abstract

In estimation theory, mainly set-theoretic or
stochastic uncertainty is considered. In some cases, especially when
some statistics of a distribution are not known or additional
stochastic information is used in a set-theoretic estimator, both
types of uncertainty have to be considered. In this paper, two
estimators that cope with combined stoachastic and set-theoretic
uncertainty are compared, namely the Set-theoretic and Statistical
Information filter, which represents the uncertainty by means of
random sets, and the Credal State filter, in which the state
information is given by sets of probability density functions.
The different uncertainty assessment in both estimators leads to
different estimation results, even when the prior information and
the measurement and system models are equal. This paper explains
these differences and states directions, when which estimator
should be applied to a given estimation problem.

Proceedings of the 13th International Conference on Information Fusion (Fusion 2010), Edinburgh, United Kingdom, July, 2010.

Author : Benjamin Noack, Vesa Klumpp, Nikolay Petkov, Uwe D. Hanebeck
Title : Bounding Linearization Errors with Sets of Densities in Approximate Kalman Filtering
In : Proceedings of the 13th International Conference on Information Fusion (Fusion 2010)
Date : July 2010

Abstract

Applying the Kalman filtering scheme to linearized system dynamics and observation models does in general not yield optimal state estimates.
More precisely, inconsistent state estimates and covariance matrices are caused by neglected linearization errors.
This paper introduces a concept for systematically predicting and updating bounds for the linearization errors within the Kalman filtering framework.
To achieve this, an uncertain quantity is not characterized by a single probability density anymore, but rather by a set of densities and accordingly,
the linear estimation framework is generalized in order to process sets of probability densities. By means of this generalization,
the Kalman filter may then not only be applied to stochastic quantities, but also to unknown but bounded quantities.
In order to improve the reliability of Kalman filtering results, the last-mentioned quantities are utilized to bound the typically neglected nonlinear parts of a linearized mapping.

Proceedings of the IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing (SUTC 2010), Newport Beach, California, USA, June, 2010.

Author : Daniel Lyons, Benjamin Noack, Uwe D. Hanebeck
Title : A Log-Ratio Information Measure for Stochastic Sensor Management
In : Proceedings of the IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing (SUTC 2010)
Date : June 2010

Abstract

In distributed sensor networks, computational and energy resources are
in general limited. Therefore, an intelligent selection of sensors for
measurements is of great importance to ensure both high estimation
quality and an extended lifetime of the network. Methods from the theory
of model predictive control together with information theoretic measures
have been employed to pick sensors yielding measurements with high
information value. We present a novel information measure that originates from a
scalar product on a class of continuous probability densities and apply it
to the field of sensor management. Aside from its mathematical justifications
for quantifying the information content of probability densities, the most
remarkable property of the measure, an analogon of the triangle inequality
under Bayesian information fusion, is deduced. This allows for deriving
computationally cheap upper bounds for the model predictive sensor selection
algorithm and for comparing the performance of planning over different lengths of time horizons.

Verteilte Messsysteme, pp. 121-132, KIT Scientific Publishing, March, 2010.

URL

Author : Daniel Lyons, Achim Hekler, Benjamin Noack, Uwe D. Hanebeck
Title : Maße für Wahrscheinlichkeitsdichten in der informationstheoretischen Sensoreinsatzplanung
In : Verteilte Messsysteme
Date : March 2010

Abstract

Bei der Beobachtung eines räumlich verteilten Phänomens mit einer
Vielzahl von Sensoren ist die intelligente Auswahl von Messkonfigurationen aufgrund von
beschränkten Rechen- und Kommunikationskapazitäten entscheidend für die
Lebensdauer des Sensornetzes. Mit der Sensoreinsatzplanung kann die im nächsten
Zeitschritt anzusteuernde Messkonfiguration dynamisch mittels einer stochastischen
modell-prädiktiven Planung über einen endlichen Zeithorizont bestimmt werden.
Dabei wird als Gütekriterium die Maximierung des zu erwartenden Informationsgewinns
durch zukünftige Messungen unter sparsamer Verwendung der Energieressourcen gewählt.
In diesem Artikel wird ein neues Maß für kontinuierliche Wahrscheinlichkeitsdichten
vorgestellt, das sich kanonisch aus der Konstruktion eines Vektorraums für
Wahrscheinlichkeitsdichten ergibt. Dieses Maß wird als Gütefunktion in der
vorausschauenden Sensoreinsatzplanung zur Bewertung des informationstheoretischen Einfluß
von Messungen auf die aktuelle Zustandsschätzung verwendet.

Verteilte Messsysteme, pp. 167-178, KIT Scientific Publishing, March, 2010.

URL

Author : Benjamin Noack, Vesa Klumpp, Daniel Lyons, Uwe D. Hanebeck
Title : Systematische Beschreibung von Unsicherheiten in der Informationsfusion mit Mengen von Wahrscheinlichkeitsdichten
In : Verteilte Messsysteme
Date : March 2010

Abstract

Die systematische Behandlung von Unsicherheiten stellt eine wesentliche
Herausforderung in der Informationsfusion dar. Einerseits müssen geeignete Darstellungsformen
für die Unsicherheiten bestimmt werden und andererseits darauf aufbauend effiziente
Schätzverfahren hergeleitet werden. Im Allgemeinen wird zwischen stochastischen und
mengenbasierten Unsicherheitsbeschreibungen unterschieden. Dieser Beitrag stellt ein Verfahren
zur Zustandsschätzung vor, welches simultan stochastische und mengenbasierte Fehlergrößen
berücksichtigen kann, indem unsichere Größen nicht mehr durch eine einzelne
Wahrscheinlichkeitsdichte, sondern durch eine Menge von Dichten repräsentiert werden.
Besonderes Augenmerk liegt hier auf den Vorteilen und Anwendungsmöglichkeiten dieser
Unsicherheitsbeschreibung.

Proceedings of the 12th International Conference on Information Fusion (Fusion 2009), Seattle, Washington, USA, July, 2009.

Author : Benjamin Noack, Vesa Klumpp, Uwe D. Hanebeck
Title : State Estimation with Sets of Densities considering Stochastic and Systematic Errors
In : Proceedings of the 12th International Conference on Information Fusion (Fusion 2009)
Date : July 2009

Abstract

In practical applications, state estimation requires the consideration of
stochastic and systematic errors. If both error types are present, an exact
probabilistic description of the state estimate is not possible, so that
common Bayesian estimators have to be questioned. This paper introduces a
theoretical concept, which allows for incorporating unknown but bounded errors
into a Bayesian inference scheme by utilizing sets of densities. In order to
derive a tractable estimator, the Kalman filter is applied to ellipsoidal sets
of means, which are used to bound additive systematic errors. Also, an
extension to nonlinear system and observation models with ellipsoidal error
bounds is presented. The derived estimator is motivated by means of two
example applications.

Proceedings of the 11th International Conference on Information Fusion (Fusion 2008), pp. 1-8, Cologne, Germany, July, 2008.

Author : Benjamin Noack, Vesa Klumpp, Dietrich Brunn, Uwe D. Hanebeck
Title : Nonlinear Bayesian Estimation with Convex Sets of Probability Densities
In : Proceedings of the 11th International Conference on Information Fusion (Fusion 2008)
Date : July 2008

Abstract

This paper presents a theoretical framework for
Bayesian estimation in the case of imprecisely known probability
density functions. The lack of knowledge about the true density
functions is represented by sets of densities. A formal Bayesian
estimator for these sets is introduced, which is intractable for
infinite sets. To obtain a tractable filter, properties of convex
sets in form of convex polytopes of densities are investigated.
It is shown that pathwise connected sets and their convex hulls
describe the same ignorance. Thus, an exact algorithm is derived,
which only needs to process the hull, delivering tractable results
in the case of a proper parametrization. Since the estimator
delivers a convex hull of densities as output, the theoretical
grounds are laid for deriving efficient Bayesian estimators for
sets of densities. The derived filter is illustrated by means of an
example.

Selected Talks

2011

Benjamin Noack, Marcus Baum, Uwe D. Hanebeck
Automatic Exploitation of Independencies for Covariance Bounding in Fully Decentralized Estimation
18th IFAC World Congress (IFAC 2011)
Milan, August 31, 2011

Benjamin Noack
Combined Stochastic and Set-valued State Estimation with Application to Extended Object Tracking
Visit at IDSIA (Istituto Dalle Molle di Studi sull'Intelligenza Artificiale)
Lugano, August 24, 2011

Benjamin Noack, Marcus Baum, Uwe D. Hanebeck
Covariance Intersection in Nonlinear Estimation based on Pseudo Gaussian Densities
14th International Conference on Information Fusion (Fusion 2011)
Chicago, July 8, 2011

on behalf of Evgeniya Bogatyrenko, Uwe D. Hanebeck
Visual Stabilization of a Beating Heart Motion by Model-Based Transformation of Image Sequences
American Control Conference (ACC 2011)
San Francisco, July 1, 2011

Benjamin Noack, Daniel Lyons, Matthias Nagel, Uwe D. Hanebeck
Nonlinear Information Filtering for Distributed Multisensor Data Fusion
American Control Conference (ACC 2011)
San Francisco, July 1, 2011

2010

Evgeniya Bogatyrenko, Benjamin Noack, Uwe D. Hanebeck
Reliable Estimation of Heart Surface Motion under Stochastic and Unknown but Bounded Systematic Uncertainties
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2010)
Taipei, October 19, 2010

Benjamin Noack, Uwe D. Hanebeck
Unsicherheitsmodellierung und nichtlineare dezentrale Informationsfusion
Workshop - Research Training Group 1194 "Self-organizing Sensor-Actuator-Networks"
Dagstuhl, October 13, 2010

Benjamin Noack, Vesa Klumpp, Nikolay Petkov, Uwe D. Hanebeck
Bounding Linearization Errors with Sets of Densities in Approximate Kalman Filtering
13th International Conference on Information Fusion (Fusion 2010)
Edinburgh, July 29, 2010

Benjamin Noack, Vesa Klumpp, Daniel Lyons, Uwe D. Hanebeck
Systematische Beschreibung von Unsicherheiten in der Informationsfusion mit Mengen von Wahrscheinlichkeitsdichten
Panel of Experts: Verteilte Messsysteme
Braunschweig, March 25, 2010

2009

Benjamin Noack, Uwe D. Hanebeck
Reconstruction and Identification of Distributed Physical Phenomena under Stochastic and Systematic Uncertainties
Finnish-German Co-operative Graduate School Network
Karlsruhe/Paris, November 25, 2009

Benjamin Noack, Uwe D. Hanebeck
Simultane Rekonstruktion und Identifikation von nichtlinearen physikalischen Phänomenen
Workshop - Research Training Group 1194 "Self-organizing Sensor-Actuator-Networks"
Freudenstadt, October 14, 2009

Benjamin Noack, Vesa Klumpp, Uwe D. Hanebeck
State Estimation with Sets of Densities considering Stochastic and Systematic Errors
12th International Conference on Information Fusion (Fusion 2009)
Seattle, July 9, 2009

2008

Benjamin Noack, Vesa Klumpp, Dietrich Brunn, Uwe D. Hanebeck
Nonlinear Bayesian Estimation with Convex Sets of Probability Densities
11th International Conference on Information Fusion (Fusion 2008)
Cologne, July 1, 2008