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Felix Sawo

Dipl.-Ing.

Auszeichnungen

• Best Paper Award (PDF) für Sensor Node Localization Methods based on Local Observations of Distributed Natural Phenomena (PDF) bei der
  

IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2008), Seoul, Korea.

Wissenschaftlicher Werdegang

Forschungsinteressen

• Sensor-Aktor-Netzwerke
• System- und Schätztheorie
• Modellierung, Identifikation und Rekonstruktion verteilter Phänomene
• Robotik

Mitglied im DFG Graduiertenkolleg 1194 "Selbstorganisiernde Sensor-Aktor-Netzwerke".

Publikationen

tm - Technisches Messen, Oldenbourg Verlag, 77(10):551-557, October, 2010.

URL

Author : Achim Kuwertz, Marco F. Huber, Felix Sawo, Uwe D. Hanebeck
Title : Modellbasierte Quellenverfolgung in räumlich ausgedehnten Phänomenen mittels Sensoreinsatzplanung
In : tm - Technisches Messen, Oldenbourg Verlag
Date : October 2010

Abstract

Bewegte Quellen können durch Emission räumlich
ausgedehnte Phänomene wie beispielsweise Schadstoff- oder
Temperaturverteilungen erzeugen. Zur Lokalisierung von Quellen
mit unbekannter Position stehen in vielen Aufgabenstellungen
Informationen nur indirekt durch die verteilte Vermessung des
induzierten Phänomens zur Verfügung - etwa unter Verwendung
stationärer oder mobiler Sensoren. Dieser Beitrag stellt
modellbasierte Verfahren für eine echtzeitfähige Lokalisierung
und Verfolgung von bewegten Quellen vor. Zur gezielten Maximierung
des Informationsgehalts der Messungen wird dabei eine vorausschauende
Sensoreinsatzplanung genutzt, welche eine hohe Lokalisierungsgüte bei
geringem Aufwand ermöglicht.

Verteilte Messsysteme, pp. 179-191, KIT Scientific Publishing, March, 2010.

URL

Author : Achim Kuwertz, Marco F. Huber, Felix Sawo, Uwe D. Hanebeck
Title : Sensoreinsatzplanung zur Verfolgung von Quellen räumlich ausgedehnter Phänomene
In : Verteilte Messsysteme
Date : March 2010

Abstract

Räumlich ausgedehnte Phänomene wie Schadstoffverteilungen in Gewässern
oder Temperaturverteilungen in Räumen werden vielfach durch unbekannte, aber gegebenenfalls
sich bewegende Quellen erzeugt. Allerdings stehen in vielen praktisch relevanten Aufgabenstellungen
Informationen zur Lokalisierung einer derartigen Quelle nur indirekt durch eine Vermessung des
induzierten Phänomens zur Verfügung, welche den Einsatz eines verteilten Messsystems erfordert.
Die Messungen können dabei beispielsweise von einem stationären Sensornetz oder von mobilen
Sensoren stammen. In diesem Beitrag werden modellbasierte Verfahren zu echtzeitfähigen Lokalisierung
und schritthaltenden Verfolgung von Quellen vorgestellt, welche gezielt räumlich und zeitlich
verteilte Messungen einsetzen. Um den Informationsgewinn und somit den Nutzen verteilter Messungen zu
maximieren, spielt bei diesem Verfahren neben einer mathematischen Modellierung auch eine vorausschauende
Sensoreinsatzplanung eine zentrale Rolle. Das in diesem Beitrag vorgeschlagene Planungsverfahren
ermöglicht dabei die effiziente und ressourcenschonende Verfolgung beweglicher Quellen bei gleichzeitig
hoher Lokalisierungsgenauigkeit.

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

Author : Julian Hörst, Felix Sawo, Vesa Klumpp, Uwe D. Hanebeck, Dietrich Fränken
Title : Extension of the Sliced Gaussian Mixture Filter with Application to Cooperative Passive Target Tracking
In : Proceedings of the 12th International Conference on Information Fusion (Fusion 2009)
Date : July 2009

Abstract

This paper copes with the problem of nonlinear Bayesian state estimation.
A nonlinear filter, the Sliced Gaussian Mixture Filter (SGMF), employs linear
substructures in the nonlinear measurement and prediction model in order to
simplify the estimation process.
Here, a special density representation, the sliced Gaussian mixture
density, is used to derive an exact solution of the Chapman-Kolmogorov equation.
The sliced Gaussian mixture density is obtained by a systematic and deterministic
approximation of a continuous density minimizing a certain distance measure.
In contrast to previous work, improvements of the SGMF presented here include an
extended system model and the processing of multi-dimensional nonlinear
subspaces. As an application for the SGMF, cooperative passive target tracking,
where sensors take angular measurements from a target, is considered in this paper.
Finally, the performance of the proposed estimator is compared to the
marginalized particle filter (MPF) in simulations.

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

Author : Marco F. Huber, Achim Kuwertz, Felix Sawo, Uwe D. Hanebeck
Title : Distributed Greedy Sensor Scheduling for Model-based Reconstruction of Space-Time Continuous Physical Phenomena
In : Proceedings of the 12th International Conference on Information Fusion (Fusion 2009)
Date : July 2009

Abstract

A novel distributed sensor scheduling method for large-scale sensor
networks observing space-time continuous physical phenomena is
introduced. In a first step, the model of the distributed phenomenon
is spatially and temporally decomposed leading to a linear
probabilistic finite-dimensional model. Based on this representation,
the information gain of sensor measurements is evaluated by means of
the so-called covariance reduction function. For this reward function,
it is shown that the performance of the greedy sensor scheduling is at
least half that of the optimal scheduling considering long-term
effects. This finding is the key for distributed sensor scheduling,
where a central processing unit or fusion center is unnecessary, and
thus, scaling as well as reliability is ensured. Hence, greedy
scheduling in combination with a proposed hierarchical communication
scheme requires only local sensor information and communication.

Proceedings of the 2008 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2008), pp. 301-308, Seoul, Republic of Korea, August, 2008.

Author : Felix Sawo, Thomas C. Henderson, Christopher Sikorski, Uwe D. Hanebeck
Title : Sensor Node Localization Methods based on Local Observations of Distributed Natural Phenomena
In : Proceedings of the 2008 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2008)
Date : August 2008

Abstract

This paper addresses the model-based localization
of sensor networks based on local observations of a distributed
phenomenon. For the localization process, we propose the
rigorous exploitation of strong mathematical models of distributed
phenomena. By unobtrusively exploiting background
phenomena, the individual sensor nodes can be localized by
only observing its local surrounding without the necessity of
heavy infrastructure. In this paper, we introduce two novel
approaches: (a) the polynomial system localization method
(PSL-method) and (b) the simultaneous reconstruction and
localization method (SRL-method). The first approach (PSLmethod)
is based on restating the mathematical model of the
distributed phenomenon in terms of a polynomial system.
These equations depend on both the state of the phenomenon
and the node locations. Solving the system of polynomials
for each individual sensor node directly leads to the desired
locations. The second approach (SRL-method) basically regards
the localization problem as a simultaneous state and parameter
estimation problem with the node locations as parameters. By
this means, the distributed phenomenon is reconstructed and
the individual nodes are localized in a simultaneous fashion.
In addition, within this framework the uncertainties in the
mathematical model and the measurements are considered.
The performance of the two different localization approaches
is demonstrated by means of simulation results.

Winner Best Paper Award

Proceedings of the 17th IFAC World Congress (IFAC 2008), 17, Seoul, Republic of Korea, July, 2008.

Author : Felix Sawo, Frederik Beutler, Uwe D. Hanebeck
Title : Decentralized State Estimation of Distributed Phenomena based on Covariance Bounds
In : Proceedings of the 17th IFAC World Congress (IFAC 2008)
Date : July 2008

Abstract

This paper addresses the problem of decentralized state estimation of distributed physical
phenomena observed by a sensor network. The centralized approaches are not scalable for large
sensor networks, because all information has to be transmitted to a powerful central processing node
requiring an extensive amount of communication bandwidth and a lot of processing power. Thus, for a
decentralized reconstruction of distributed phenomena, we propose a novel methodology consisting of
three steps: (a) conversion of the distributed phenomenon into a lumped-parameter system description,
(b) decomposition of the resulting system in order to map the description to the actual sensor network,
and (c) decomposition of the density representation leading to a decentralized estimation approach. The
main problem of a decentralized approach is that due to the propagation of local information through the
network, unknown correlations are caused. This fact needs to be considered during the reconstruction
process in order to get correct and consistent estimation results. For that reason, we employ a robust
estimator (based on Covariance Bounds) for the local reconstruction update on each sensor node. By this
means, the individual sensor nodes are able to estimate the local state of the distributed phenomenon
using local estimates obtained and communicated by adjacent nodes only. The information about their
correlations is not stored in the sensor network.

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

Author : Vesa Klumpp, Felix Sawo, Uwe D. Hanebeck, Dietrich Fränken
Title : The Sliced Gaussian Mixture Filter for Efficient Nonlinear Estimation
In : Proceedings of the 11th International Conference on Information Fusion (Fusion 2008)
Date : July 2008

Abstract

This paper addresses the efficient state estimation
for mixed linear/nonlinear dynamic systems with noisy measurements.
Based on a novel density representation – sliced Gaussian
mixture density – the decomposition into a (conditionally) linear
and nonlinear estimation problem is derived. The systematic
approximation procedure minimizing a certain distance measure
allows the derivation of (close to) optimal and deterministic
estimation results. This leads to high-quality representations of
the measurement-conditioned density of the states and, hence, to
an overall more efficient estimation process. The performance of
the proposed estimator is compared to state-of-the-art estimators,
like the well-known marginalized particle filter.

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

Author : Felix Sawo, Vesa Klumpp, Uwe D. Hanebeck
Title : Simultaneous State and Parameter Estimation of Distributed-Parameter Physical Systems based on Sliced Gaussian Mixture Filter
In : Proceedings of the 11th International Conference on Information Fusion (Fusion 2008)
Date : July 2008

Abstract

This paper presents a method for the simultaneous
state and parameter estimation of finite-dimensional models of
distributed systems monitored by a sensor network. In the
first step, the distributed system is spatially and temporally
decomposed leading to a linear finite-dimensional model in state
space form. The main challenge is that the simultaneous state and
parameter estimation of such systems leads to a high-dimensional
nonlinear problem. Thanks to the linear substructure contained
in the resulting finite-dimensional model, the development of an
overall more efficient estimation process is possible. Therefore,
in the second step, we propose the application of a novel density
representation – sliced Gaussian mixture density – in order to
decompose the estimation problem into a (conditionally) linear
and a nonlinear problem. The systematic approximation procedure
minimizing a certain distance measure allows the derivation
of (close to) optimal and deterministic results. The proposed
estimation process provides novel prospects in sensor network
applications. The performance is demonstrated by means of
simulation results.

Informatics in Control, Automation and Robotics, Selected Papers from ICINCO 2006, Series: Lecture Notes in Electrical Engineering, 15, Springer, 2008.

URL

Author : Felix Sawo, Kathrin Roberts, Uwe D. Hanebeck
Title : Model-Based Reconstruction of Distributed Phenomena Using Discretized Representations of Partial Differential Equations
In : Informatics in Control, Automation and Robotics, Selected Papers from ICINCO 2006, Series: Lecture Notes in Electrical Engineering
Date : 2008

Abstract

This article addresses the model-based reconstruction and prediction of distributed phenomena
characterized by partial differential equations, which are monitored by sensor networks. The novelty of the
proposed reconstruction method is the systematic approach and the integrated treatment of uncertainties,
which occur in the physical model and arise naturally from noisy measurements. By this means it is possible
not only to reconstruct the entire phenomenon, even at non-measurement points, but also to reconstruct the
complete density function of the state characterizing the distributed phenomenon. In the first step, the
partial differential equation, i.e., distributed-parameter system, is spatially and temporally decomposed
leading to a finite-dimensional state space form. In the next step, the state of the resulting lumped-parameter
system, which provides an approximation of the solution of the underlying partial differential equation,
is dynamically estimated under consideration of uncertainties. By using the estimation results, several
additional tasks can be achieved by the sensor network, e.g. optimal sensor placement, optimal scheduling,
model improvement, and system identification. The performance of the proposed model-based reconstruction
method is demonstrated by means of simulations.

Proceedings of the 10th International Conference on Information Fusion (Fusion 2007), Quebec, Canada, July, 2007.

Author : Felix Sawo, Marco F. Huber, Uwe D. Hanebeck
Title : Parameter Identification and Reconstruction for Distributed Phenomena Based on Hybrid Density Filter
In : Proceedings of the 10th International Conference on Information Fusion (Fusion 2007)
Date : July 2007

Abstract

This paper addresses the problem of model-based reconstruction and
parameter identification of distributed phenomena characterized by
partial differential equations. The novelty of the proposed method
is the systematic approach and the integrated treatment of uncertainties,
which naturally occur in the physical system and arise from noisy
measurements. The main challenge of accurate reconstruction is that
model parameters, i.e., diffusion coefficients, of the physical model
are not known in advance and usually need to be identified. Generally,
the problem of parameter identification leads to a nonlinear estimation
problem. Hence, a novel efficient recursive procedure is employed.
Unlike other estimators, the so-called Hybrid Density Filter not
only assures accurate estimation results for nonlinear systems, but
also offers an efficient processing. By this means it is possible
to reconstruct and identify distributed phenomena monitored by autonomous
wireless sensor networks. The performance of the proposed estimation
method is demonstrated by means of simulations.

tm - Technisches Messen, Oldenbourg Verlag, 3:75-90, March, 2007.

Author : Dietrich Brunn, Felix Sawo, Uwe D. Hanebeck
Title : Modellbasierte Vermessung verteilter Phänomene und Generierung optimaler Messsequenzen
In : tm - Technisches Messen, Oldenbourg Verlag
Date : March 2007

Abstract

Dieser Beitrag befasst sich mit modellbasierten Methoden zur Vermessung
verteilter physikalischer Phänomene. Diese Methoden zeichnen
sich durch eine systematische Behandlung von Unsicherheiten aus,
so dass neben der Rekonstruktion der vollständigen Wahrscheinlichkeitsdichte
der relevanten Größen aus einer geringen Anzahl von zeit-,
orts- und wertdiskreten Messungen auch die Generierung optimaler
Messsequenzen möglich ist. Es wird dargestellt, wie eine Beschreibung
für ein verteilt-parametrisches System in Form einer partiellen
Differentialgleichung, welche einen unendlich-dimensionalen Zustandsraum
beschreibt, in eine konzentriert-parametrische Form konvertiert wird.
Diese kann als Grundlage für den Entwurf klassischer Schätzer,
wie z. B. des Kalman-Filters, dienen. Ferner wird eine Methode zur
Sensoreinsatzplanung vorgestellt, mit der eine optimale Sequenz von
Messparametern bestimmt werden kann, um mit einem minimalen Messaufwand
die Unsicherheit auf ein gewünschtes Maß zu reduzieren. Die
Anwendung dieser Methoden wird an zwei Beispielen, einer Temperaturverteilung
und der Verformung einer Führungsschiene, demonstriert. Zusätzlich
werden die Herausforderungen bei der Behandlung nichtlinearer Systeme
und die Probleme bei der dezentralen Verarbeitung, wie sie typischerweise
beim Einsatz von Sensornetzwerken auftreten, diskutiert.

Proceedings of the 2006 IEEE Conference on Decision and Control (CDC 2006), pp. 1303-1308, San Diego, California, USA, December, 2006.

Author : Dietrich Brunn, Felix Sawo, Uwe D. Hanebeck
Title : Nonlinear Multidimensional Bayesian Estimation with Fourier Densities
In : Proceedings of the 2006 IEEE Conference on Decision and Control (CDC 2006)
Date : December 2006

Abstract

Efficiently implementing nonlinear Bayesian estimators is still an
unsolved problem, especially for the multidimensional case. A trade-off
between estimation quality and demand on computational resources
has to be found. Using multidimensional Fourier series as representation
for probability density functions, so called Fourier densities, is
proposed. To ensure non-negativity, the approximation is performed
indirectly via Psi-densities, of which the absolute square represent
the Fourier density. It is shown that PSI-densities can be determined
using the efficient fast Fourier transform algorithm and their coefficients
have an ordering with respect to the Hellinger metric. Furthermore,
the multidimensional Bayesian estimator based on Fourier Densities
is derived in closed form. That allows an efficient realization of
the Bayesian estimator where the demands on computational resources
are adjustable.

Proceedings of the 2006 IEEE International Conference on Control Applications (CCA 2006), Munich, Germany, October, 2006.

Author : Felix Sawo, Dietrich Brunn, Uwe D. Hanebeck
Title : Parameterized Joint Densities with Gaussian Mixture Marginals and their Potential Use in Nonlinear Robust Estimation
In : Proceedings of the 2006 IEEE International Conference on Control Applications (CCA 2006)
Date : October 2006

Abstract

This paper addresses the challenges of the fusion of two random vectors
with imprecisely known stochastic dependency. This problem mainly
occurs in decentralized estimation, e.g. of a distributed phenomenon,
where the stochastic dependencies between the individual states are
not stored. To cope with such problems we propose to exploit parameterized
joint densities with both Gaussian marginals and Gaussian mixture
marginals. Under structural assumptions these parameterized joint
densities contain all information about the stochastic dependencies
between their marginal densities in terms of a generalized correlation
parameter vector xi. The parameterized joint densities are applied
to the prediction step and the measurement step under imprecisely
known correlation leading to a whole family of possible estimation
results. The resulting density functions are characterized by the
generalized correlation parameter vector xi. Once this structure
and the bounds of these parameters are known, it is possible to find
bounding densities containing all possible density functions, i.e.,
conservative estimation results.

Informationsfusion in der Mess- und Sensortechnik, pp. 75-90, Universitätsverlag Karlsruhe, September, 2006.

Author : Dietrich Brunn, Felix Sawo, Uwe D. Hanebeck
Title : Informationsfusion für verteilte Systeme
In : Informationsfusion in der Mess- und Sensortechnik
Date : September 2006

Abstract

Dieser Beitrag befasst sich mit modellbasierten Methoden zur Vermessung
verteilter physikalischer Phänomene. Diese Methoden zeichnen
sich durch eine systematische Behandlung stochastischer Unsicherheiten
aus, so dass neben der Rekonstruktion der vollständigen Wahrscheinlichkeitsdichte
der relevanten Größen aus einer geringen Anzahl von zeit-,
orts- und wertdiskreten Messungen auch die Generierung optimaler
Messsequenzen möglich ist. Es wird dargestellt, wie eine Beschreibung
für ein verteilt-parametrisches System in Form einer partiellen
Differentialgleichung, welche einen unendlich-dimensionalen Zustandsraum
beschreibt, in eine konzentriert-parametrische Form konvertiert
wird. Diese kann als Grundlage für den Entwurf klassischer Schätzer,
wie z.B. des Kalman Filters, dienen. Ferner wird eine Methode zur
Sensoreinsatzplanung vorgestellt, mit der eine optimale Sequenz von
Messparametern bestimmt werden kann, um mit einem minimalen Messaufwand
die Unsicherheit auf ein gewünschtes Maß zu reduzieren. Die
Anwendung dieser Methoden wird an zwei Beispielen, einer Temperaturverteilung
und der Verformung einer Führungsschiene, demonstriert. Zusätzlich
werden die Herausforderungen bei der Behandlung nichtlinearer Systeme
und die Probleme bei der dezentralen Verarbeitung, wie sie typischerweise
beim Einsatz von Sensornetzwerken auftreten, diskutiert.

Proceedings of the 2006 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2006), pp. 312-322, Heidelberg, Germany, September, 2006.

Author : Dietrich Brunn, Felix Sawo, Uwe D. Hanebeck
Title : Efficient Nonlinear Bayesian Estimation based on Fourier Densities
In : Proceedings of the 2006 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2006)
Date : September 2006

Abstract

Efficiently implementing nonlinear Bayesian estimators is still not
a fully solved problem. For practical applications, a trade-off between
estimation quality and demand on computational resources has to be
found. In this paper, the use of nonnegative Fourier series, so-called
Fourier densities, for Bayesian estimation is proposed. By using
the absolute square of Fourier series for the density representation,
it is ensured that the density stays nonnegative. Nonetheless, approximation
of arbitrary probability density functions can be made by using the
Fourier integral formula. An efficient Bayesian estimator algorithm
with constant complexity for nonnegative Fourier series is derived
and demonstrated by means of an example.

Proceedings of the 3rd International Conference on Informatics in Control, Automation and Robotics (ICINCO 2006), pp. 16-23, Setúbal, Portugal, August, 2006.

Author : Felix Sawo, Kathrin Roberts, Uwe D. Hanebeck
Title : Bayesian Estimation of Distributed Phenomena using Discretized Representations of Partial Differential Equations
In : Proceedings of the 3rd International Conference on Informatics in Control, Automation and Robotics (ICINCO 2006)
Date : August 2006

Abstract

This paper addresses a systematic method for the reconstruction and
the prediction of a distributed phenomenon characterized by partial
differential equations, which is monitored by a sensor network. In
the first step, the infinite-dimensional partial differential equation,
i.e. distributed-parameter system, is spatially and temporally decomposed
leading to a finite-dimensional state space form. In the next step,
the state of the resulting lumped-parameter system, which provides
an approximation of the solution of the underlying partial differential
equations, is dynamically estimated under consideration of uncertainties
both occurring in the system and arising from noisy measurements.
By using the estimation results, several additional tasks can be
achieved by the sensor network, e.g. optimal sensor placement, optimal
scheduling, and model improvement. The performance of the proposed
model-based reconstruction method is demonstrated by means of simulations.

Proceedings of the 9th International Conference on Information Fusion (Fusion 2006), Florence, Italy, July, 2006.

Author : Felix Sawo, Dietrich Brunn, Uwe D. Hanebeck
Title : Parameterized Joint Densities with Gaussian and Gaussian Mixture Marginals
In : Proceedings of the 9th International Conference on Information Fusion (Fusion 2006)
Date : July 2006

Abstract

In this paper we attempt to lay the foundation for a novel filtering
technique for the fusion of two random vectors with imprecisely known
stochastic dependency. This problem mainly occurs in decentralized
estimation, e.g., of a distributed phenomenon, where the stochastic
dependencies between the individual states are not stored. Thus,
we derive parameterized joint densities with both Gaussian marginals
and Gaussian mixture marginals. These parameterized joint densities
contain all information about the stochastic dependencies between
their marginal densities in terms of a parameter vector xi, which
can be regarded as a generalized correlation parameter. Unlike the
classical correlation coeffcient, this parameter is a suffcient measure
for the stochastic dependency even characterized by more complex
density functions such as Gaussian mixtures. Once this structure
and the bounds of these parameters are known, bounding densities
containing all possible density functions could be found.

Proceedings of the 2005 IEEE Conference on Control Applications (CCA 2005), pp. 1200-1205, August, 2005.

Author : Felix Sawo, Masayuki Fujita, Oliver Sawodny
Title : Passivity-Based Dynamic Visual Feedback Control of Manipulators with Kinematic Redundancy
In : Proceedings of the 2005 IEEE Conference on Control Applications (CCA 2005)
Date : August 2005

Selected Talks

2009

Felix Sawo
Nichtlineare Zustands- und Parameterschätzung für Räumlich-Verteilte Systeme (Über.)
Promotionsprüfung
Karlsruhe, 30. Januar 2009

2008

Felix Sawo
Nonlinear State and Parameter Estimation of Distributed Systems
Abschlussvortrag zum Teilprojekt I1 des GRK 1194
Bad Herrenalb, 30. September 2008

Felix Sawo, Vesa Klumpp, Uwe D. Hanebeck
Simultaneous State and Parameter Estimation of Distributed-Parameter Physical Systems based on Sliced Gaussian Mixture Filter
The 11th International Conference on Information Fusion (Fusion 2008)
Köln, 1. Juli 2008

Vesa Klumpp, Felix Sawo, Uwe D. Hanebeck, Dietrich Fränken
The Sliced Gaussian Mixture Filter for Efficient Nonlinear Estimation
The 11th International Conference on Information Fusion (Fusion 2008)
Köln, 1. Juli 2008

Felix Sawo
Research Stay at School of Computing, University of Utah - Report -
Treffen des Graduiertenkolleges 1194 "Selbstorganisierende Sensor-Aktor-Netwerke"
Karlsruhe, 7. Juli 2008

Uwe D. Hanebeck, Felix Sawo, Klaus-Dieter Sommer
Bayes´scher Ansatz zur konsistenten Unsicherheitsanalyse und Bewertung von stationären, dynamischen und verteiltparametrischen Messsystemen
14. GMA/ITG-Fachtagung
Ludwigsburg, 11. März 2008

Uwe D. Hanebeck, Felix Sawo
Simultaneous State and Parameter Estimation for Reconstructing Distributed Physical Phenomena
Ringvorlesung - Graduiertenkolleg 1194 "Selbstorganisierende Sensor-Aktor-Netzwerke"
Karlsruhe, 11. Januar 2008

2007

Felix Sawo, Uwe D. Hanebeck
Simultaneous State and Parameter Estimation for Reconstructing Distributed Physical Phenomena
Finnisch-Deutsches Kooperatives Graduiertenschulen-Netzwerk
Karlsruhe, 27. November 2007

Felix Sawo, Uwe D. Hanebeck
A Bayesian Approach to Consistently Describing and Analysing
Steady-State, Dynamic, and Distributed Parametric Measurement Systems
238th PTB Seminar "Analysis of Dynamic Measurements"
Berlin, 6. November 2007

Felix Sawo, Uwe D. Hanebeck
Dezentrale Rekonstruktion von verteilten Phänomenen aus orts- und zeitdiskreten Messungen
Klausurtagung des Graduiertenkolleges 1194
Dagstuhl, 18. September 2007

Felix Sawo, Marco Huber, Uwe D. Hanebeck
Parameter Identification and Reconstruction Based on Hybrid Density Filter for Distributed Phenomena
10th International Conference on Information Fusion (Fusion 2007)
Quebec, Canada, 10. Juli 2007

Felix Sawo, Uwe D. Hanebeck
Decentralized Reconstruction of Distributed Phenomena by Means of Sensor Networks
Treffen der Graduiertenkollegs "zehn plus eins"
"Zehn Informatik-Graduiertenkollegs und ein Informatik-Forschungskolleg stellen sich vor"
Dagstuhl, 6. Juni 2007

Uwe D. Hanebeck, Felix Sawo, Marco Huber
Information Processing in Sensor Networks for Model-Based
Reconstruction and Identification of Distributed Phenomena
ICRA 2007 Tutorial
Università di Roma "La Sapienza", Rom, Italien, 10. April 2007

Felix Sawo, Marco Huber, Heiko Hamann
Information Processing in Sensor-Actuator-Networks (Translation)
Kooperationsseminar des Graduiertenkolleges 1194 und Zeuss
Karlsruhe, 8. Januar 2007

2006

Dietrich Brunn, Felix Sawo, Uwe D. Hanebeck
Nonlinear Multidimensional Bayesian Estimation with Fourier Densities
IEEE Conference on Decision and Control (CDC 2006)
San Diego, California, 13. Dezember 2006

Uwe D. Hanebeck, Felix Sawo, Marco Huber
Model-Based Decentral Information Processing in Sensor-Actuator-Networks (Translation)
Ringvorlesung - Graduiertenkolleg 1194 "Selbstorganisierende Sensor-Aktor-Netzwerke"
Karlsruhe, 8. Dezember 2006

Felix Sawo, Uwe D. Hanebeck
Decentral Reconstruction of Distributed Continuous Phenomena based on Discrete Spatio-Temporal Measurements (Translation)
Workshop - Graduiertenkolleg 1194 "Selbstorganisierende Sensor-Aktor-Netzwerke"
Dagstuhl, 10. Oktober 2006

Felix Sawo, Dietrich Brunn, Uwe D. Hanebeck
Parameterized Joint Densities with Gaussian Mixture Marginals
and their Potential Use in Nonlinear Robust Estimation
IEEE International Conference on Control Applications (CCA 2006)
München, 4. Oktober 2006

Dietrich Brunn, Felix Sawo, Uwe D. Hanebeck
Efficient Nonlinear Bayesian Estimation based on Fourier Densities
IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2006)
Heidelberg, 5. September 2006

Felix Sawo, Kathrin Roberts, Uwe D. Hanebeck
Bayesian Estimation of Distributed Phenomena
using Discretized Representations of Partial Differential Equations
3rd International Conference on Informatics in Control, Automation and Robotics (ICINCO 2006)
Setubal, Portugal, 2. August 2006

Felix Sawo, Dietrich Brunn, Uwe D. Hanebeck
Parameterized Joint Densities with Gaussian and Gaussian Mixture Marginals
9th International Conference on Information Fusion (Fusion 2006)
Florenz, Italien, 11. Juli 2006

Uwe D. Hanebeck, Felix Sawo, Dietrich Brunn
Information Fusion for Distributed Systems (Translation)
Expertenforum: Informationsfusion in der Mess- und Sensortechnik 2006
Veröffentlicht als Buch, erschienen im Universitätsverlag Karlsruhe
Eisenach, 21-22. Juni 2006

2005

Felix Sawo, Masayuki Fujita, Oliver Sawodny
Passivity-Based Dynamic Visual Feedback Control of Manipulators with Kinematic Redundancy
IEEE International Conference on Control Applications (CCA 2005)
Toronto, Kanada, 28. August 2005