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Frederik Beutler

	Dr.-Ing. Research Assistant, Ph.D. Student
	Address:	Karlsruher Institut für Technologie Institut für Anthropomatik Gebäude 50.20 Raum 133 Adenauerring 2 D-76131 Karlsruhe
	Walk-in hours:	on arrangement
	Phone:	+49-721-608-47640
	E-mail:	Frederik.Beutler@kit.edu

Academic Career

• 05/09 Graduation as Dr.-Ing., Universität Karlsruhe (TH), PhD Thesis: "Probabilistic Model-Based Signal Processing for Instantaneous Pose Estimation" (Translation)
• since 04/03 Research Assistant at the Intelligent Sensor-Actuator-Systems Lab, Faculty of Informatics, Universität Karlsruhe (TH).
• 09/02 Diploma thesis at the Laboratory for Digital Signal Processing, Technische Universität Kaiserslautern. Topic: "Ein Lautsprecher-Analysator mit Modulen für Time- Delay Spectrometry (TDS), Maximalfolgenmesstechnik (MLS), Thiele-Small- Parameter und Erfassung nichtlinearer Verzerrungen in einer Matlab-Umgebung".
• 05/01 Student research project at the Laboratory for Communications Engineering. Topic: "Untersuchung von blinden Verfahren zur Kanalschätzung und zur Entzerrung auf der Basis von Higher-Order Statistics"
• 10/00 - 09/01 Erasmus-coordinator in the AG-Auslandskontakte.
• 10/96 - 09/02 Student of Electrical Engineering and Information Technology at the Technische Universität Kaiserslautern.

Research Interests

digital signal processing, data fusion, nonlinear estimation, localization, tracking systems

Publications

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

Author : Marco F. Huber, Frederik Beutler, Uwe D. Hanebeck
Title : (Semi-)Analytic Gaussian Mixture Filter
In : Proceedings of the 18th IFAC World Congress (IFAC 2011)
Date : August 2011

Abstract

In nonlinear filtering, special types of Gaussian mixture filters are a straightforward extension of Gaussian filters,
where linearizing the system model is performed individually for each Gaussian component.
In this paper, two novel types of linearization are combined with Gaussian mixture filters.
The first linearization is called analytic stochastic linearization, where the linearization is performed analytically and exactly,
i.e., without Taylor-series expansion or approximate sample-based density representation.
In cases where a full analytical linearization is not possible, the second approach decomposes the nonlinear system into a set of nonlinear subsystems that are
conditionally integrable in closed form. These approaches are more accurate than fully applying classical linearization.

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

Author : Marco F. Huber, Frederik Beutler, Uwe D. Hanebeck
Title : Semi-Analytic Gaussian Assumed Density Filter
In : Proceedings of the 2011 American Control Conference (ACC 2011)
Date : June 2011

Abstract

For Gaussian Assumed Density Filtering based on moment matching, a framework
for the efficient calculation of posterior moments is proposed that
exploits the structure of the given nonlinear system. The key idea is a
careful discretization of some dimensions of the state space only in order
to decompose the system into a set of nonlinear subsystems that are
conditionally integrable in closed form. This approach is more efficient
than full discretization approaches. In addition, the new decomposition is
far more general than known Rao-Blackwellization approaches relying on
conditionally linear subsystems. As a result, the new framework is
applicable to a much larger class of nonlinear systems.

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.

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

Author : Frederik Beutler, Marco F. Huber, Uwe D. Hanebeck
Title : Optimal Stochastic Linearization for Range-based Localization
In : Proceedings of the 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2010)
Date : October 2010

Abstract

In range-based localization, the trajectory of a
mobile object is estimated based on noisy range measurements
between the object and known landmarks. In order to deal
with this uncertain information, a Bayesian state estimator
is presented, which exploits optimal stochastic linearization.
Compared to standard state estimators like the Extended
or Unscented Kalman Filter, where a point-based Gaussian
approximation is used, the proposed approach considers the
entire Gaussian density for linearization. By employing the common
assumption that the state and measurements are jointly
Gaussian, the linearization can be calculated in closed form
and thus analytic expressions for the range-based localization
problem can be derived.

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

Author : Ferdinand Packi, Antonia Pérez Arias, Frederik Beutler, Uwe D. Hanebeck
Title : A Wearable System for the Wireless Experience of Extended Range Telepresence
In : Proceedings of the 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2010)
Date : October 2010

Abstract

Extended range telepresence aims at enabling a
user to experience virtual or remote environments, taking his
own body movements as an input to define walking speed and
viewing direction. Therefore, localization and tracking of the
users pose (position and orientation) is necessary to perform
a body-centered scene rendering. Visual and acoustic feedback
is provided to the user by a head mounted display (HMD).
To allow for free movement within the user environment, the
tracking system is supposed to be user-wearable and entirely
wireless. Consequently, a lightweight design is presented fea-
turing small dimensions to fit into a conventional 13”laptop
backpack, which satisfies the above stated demands for highly
immersive extended range telepresence scenarios. Dedicated
embedded hardware combined with off-the-shelf components
is employed to form a robust, low-cost telepresence system that
can be easily installed in any living room.

Proceedings of the 2010 IEEE International Conference on Indoor Positioning and Indoor Navigation (IPIN 2010), Zürich, Switzerland, September, 2010.

Author : Ferdinand Packi, Frederik Beutler, Uwe D. Hanebeck
Title : Wireless Acoustic Tracking for Extended Range Telepresence
In : Proceedings of the 2010 IEEE International Conference on Indoor Positioning and Indoor Navigation (IPIN 2010)
Date : September 2010

Abstract

Telepresence systems enable a user to experience
virtual or distant environments by providing sensory feedback.
Appropriate devices include head mounted displays (HMD) for
visual perception, headphones for auditory response, or even
haptic displays for tactile sensation and force feedback. While
most common designs use dedicated input devices like joysticks
or a space mouse, the approach followed in the present work
takes the user’s position and viewing direction as an input, as he
walks freely in his local surroundings. This is achieved by using
acoustic tracking, where the user’s pose (position and orientation)
is estimated on the basis of ranges measured between a set
of wall-fastened loudspeakers and a microphone array fixed on
the user’s HMD. To allow for natural user motion, a wearable,
fully wireless telepresence system is introduced. The increase in
comfort compared to wired solutions is obvious, as the user’s
awareness of distracting cables is taken away during walking.
Also the lightweight design and small dimensions contribute to
ergonomics, as the whole assembly fits well into a small backpack.

Proceedings of the 2010 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2010), Salt Lake City, Utah, USA, September, 2010.

Author : Frederik Beutler, Marco F. Huber, Uwe D. Hanebeck
Title : Semi-Analytic Stochastic Linearization for Range-Based Pose Tracking
In : Proceedings of the 2010 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2010)
Date : September 2010

Abstract

In range-based pose tracking, the translation and
rotation of an object with respect to a global coordinate system
has to be estimated. The ranges are measured between the
target and the global frame. In this paper, an intelligent decomposition
is introduced in order to reduce the computational
effort for pose tracking. Usually, decomposition procedures only
exploit conditionally linear models. In this paper, this principle
is generalized to conditionally integrable substructures and
applied to pose tracking. Due to a modified measurement
equation, parts of the problem can even be solved analytically.

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

Author : Frederik Beutler, Uwe D. Hanebeck
Title : A Two-Step Approach for Offset and Position Estimation from Pseudo-Ranges Applied to Multilateration Tracking
In : Proceedings of the 13th International Conference on Information Fusion (Fusion 2010)
Date : July 2010

Abstract

In multilateration tracking, an object, e.g., an airplane, emits a known
reference signal, which is received by several base stations (sensors) located at
known positions. The receiving times of the signal at the sensors correspond to the times of
arrival (TOA) plus an unknown offset, because the emission time is unknown.
Usually, for estimating the position of the object, the receiving times are
converted to a larger number of time differences of arrival (TDOA) in order
to eliminate the unknown offset. To avoid this conversion, the proposed
approach directly uses the receiving times. This is achieved by 1. determining the optimal offset from the redundant measurements in closed
form and 2. by considering a modified measurement equation. As a result,
position estimation can be performed by optimal stochastic linearization.

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

Author : Patrick Dunau, Ferdinand Packi, Frederik Beutler, Uwe D. Hanebeck
Title : Efficient Multilateration Tracking System with Concurrent Offset Estimation using Stochastic Filtering Techniques
In : Proceedings of the 13th International Conference on Information Fusion (Fusion 2010)
Date : July 2010

Abstract

Multilateration systems operate by deter-
mining distances between a signal transmitter and a
number of receivers. In aerial surveillance, radio sig-
nals are emitted as Secondary Surveillance Radar (SSR)
by the aircraft, representing the signal transmitter. A
number of base stations (sensors) receive the signals at
different times. Most common approaches use time dif-
ference of arrival (TDOA) measurements, calculated by
subtracting receiving times of one receiver from another.
As TDOAs require intersecting hyperboloids, which is
considered a hard task, this paper follows a different ap-
proach, using raw receiving times. Thus, estimating the
signal\'s emission time is required, captured as a com-
mon offset within an augmented version of the system
state. This way, the multilateration problem is reduced
to intersecting cones. Estimation of the aircraft\'s posi-
tion based on a nonlinear measurement model and an
underlying linear system model is achieved using a lin-
ear regression Kalman filter [1, 2]. A decomposed com-
putation of the filter step is introduced, allowing a more
efficient calculation.

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

Author : Vesa Klumpp, Frederik Beutler, Uwe D. Hanebeck, Dietrich Fränken
Title : The Sliced Gaussian Mixture Filter with Adaptive State Decomposition Depending on Linearization Error
In : Proceedings of the 13th International Conference on Information Fusion (Fusion 2010)
Date : July 2010

Abstract

In this paper, a novel nonlinear/non-linear model
decomposition for the Sliced Gaussian Mixture Filter is presented.
Based on the level of nonlinearity of the model, the overall estimation
problem is decomposed into a severely nonlinear and a slightly
nonlinear part, which are processed by different estimation techniques.
To further improve the efficiency of the estimator, an adaptive state
decomposition algorithm is introduced that allows decomposition
according to the linearization error for nonlinear system and
measurement models. Simulations show that this approach has orders of
magnitude less complexity compared to other state of the art
estimators, while maintaining comparable estimation errors.

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

Author : Frederik Beutler, Marco F. Huber, Uwe D. Hanebeck
Title : Gaussian Filtering using State Decomposition Methods
In : Proceedings of the 12th International Conference on Information Fusion (Fusion 2009)
Date : July 2009

Abstract

State estimation for nonlinear systems generally
requires approximations of the system or the probability
densities, as the occurring prediction and filtering equations
cannot be solved in closed form. For instance, Linear Regression
Kalman Filters like the Unscented Kalman Filter
or the considered Gaussian Filter propagate a small set of
sample points through the system to approximate the posterior
mean and covariance matrix. To reduce the number of
sample points, special structures of the system and measurement
equation can be taken into account. In this paper, two
principles of system decomposition are considered and applied
to the Gaussian Filter. One principle exploits that only
a part of the state vector is directly observed by the measurement.
The second principle separates the system equations
into linear and nonlinear parts in order to merely approximate
the nonlinear part of the state. The benefits of both
decompositions are demonstrated on a real-world example.

IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2009) in Taipei, Taiwan, pp. 3413-3416, April, 2009.

Author : Frederik Beutler, Marco F. Huber, Uwe D. Hanebeck
Title : Instantaneous Pose Estimation using Rotation Vectors
In : IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2009) in Taipei, Taiwan
Date : April 2009

Abstract

An algorithm for estimating the pose, i.e., translation and rotation, of
an extended target object is introduced. Compared to conventional
methods, where pose estimation is performed on the basis of timeof-
flight (TOF) measurements between external sources and sensors
attached to the object, the proposed approach directly uses the amplitude
values measured at the sensors for estimation purposes without
an intermediate TOF estimation step. This is achieved by modeling
the wave propagation by a nonlinear dynamic system comprising a
system and a measurement equation. The nonlinear system equation
includes a model of the time-variant structure of the object rotation
based on rotation vectors. As a result, the measured amplitude values
at the sensors can be processed instantaneously in a recursive
fashion. Uncertainties in the measurement process are systematically
considered by employing a stochastic filter for estimating the
pose, i.e., the state of the nonlinear dynamic system.

Journal of Robotics and Autonomous Systems, Selected papers from 2006 IEEE International Conference on Multisensor Fusion and Integration (MFI 2006), 57(3):249-258, March, 2009.

URL

Author : Frederik Beutler, Marco F. Huber, Uwe D. Hanebeck
Title : Probabilistic Instantaneous Model-Based Signal Processing applied to Localization and Tracking
In : Journal of Robotics and Autonomous Systems, Selected papers from 2006 IEEE International Conference on Multisensor Fusion and Integration (MFI 2006)
Date : March 2009

Abstract

In this paper, a probabilistic approach for estimating time
and space-variant parameters of a system, based on sequentially received
discrete-time signal values, is presented. The system description is the
solution of a linear partial differential equation (PDE). The PDE describes
for example the wave propagation of an acoustic wave in a localization
system. The solution of the PDE is given by a time-variant and space-variant
impulse response. This impulse response is characterized by the time and
space-variant parameters in order to track an object, which emits for example
an acoustic signal. For estimating the position of the object in an
instantaneous way a Bayesian approach has to be used, which considers the
dynamic behavior of the parameters in a system model and uncertainties in a
stochastic manner by means of probability density functions. Hence, the new
approach provides a probabilistic instantaneous model-based signal processing,
where the sequentially measured signal values are processed directly and known
reference signal sequences are interpreted as part of a time-variant nonlinear
measurement equation.

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 2006 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2006), pp. 311-316, Heidelberg, Germany, September, 2006.

Author : Frederik Beutler, Uwe D. Hanebeck
Title : The Probabilistic Instantaneous Matching Algorithm
In : Proceedings of the 2006 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2006)
Date : September 2006

Abstract

A new Bayesian filtering technique for estimating signal parameters
directly from discrete-time sequences is introduced. The so called
probabilistic instantaneous matching algorithm recursively updates
the probability density function of the parameters for every received
sample and, thus, provides a high update rate up to the sampling
rate with high accuracy. In order to do so, one of the signal sequences
is used as part of a time-variant nonlinear measurement equation.
Furthermore, the time-variant nature of the parameters is explicitly
considered via a system equation, which describes the evolution of
the parameters over time. An important feature of the probabilistic
instantaneous matching algorithm is that it provides a probability
density function over the parameter space instead of a single point
estimate. This probability density function can be used in further
processing steps, e.g. a range based localization algorithm in the
case of time-of-arrival estimation.

Proceedings of the 2nd International Conference on Informatics in Control, Automation and Robotics (ICINCO 2005), 2:146-153, Barcelona, Spain, September, 2005.

Author : Daniel Hahn, Frederik Beutler, Uwe D. Hanebeck
Title : Visual Scene Augmentation for Enhanced Human Perception
In : Proceedings of the 2nd International Conference on Informatics in Control, Automation and Robotics (ICINCO 2005)
Date : September 2005

Abstract

In this paper we present an assistive system for hearing-impaired
people that consists of a wearable microphone array and an Augmented
Reality (AR) system. This system helps the user in communication
situations, where many speakers or sources of background noise are
present. In order to restore the "cocktail party" effect multiple
microphones are used to estimate the position of individual sound
sources. In order to allow the user to interact in complex situations
with many speakers, an algorithm for estimating the user\'s attention
is developed. This algorithm determines the sound sources, which
are in the user\'s focus of attention. It allows the system to discard
irrelevant information and enables the user to focus on certain aspects
of the surroundings. Based on the user\'s hearing impairment, the
perception of the speaker in the focus of attention can be enhanced,
e.g. by amplification or using a speech-to-text conversion. A prototype
has been built for evaluating this approach. Currently the prototype
is able to locate sound beacons in three-dimensional space, to perform
a simple focus estimation, and to present floating captions in the
Augmented Reality. The prototype uses an intentionally simple user
interface, in order to minimize distractions.

Proceedings of the 2nd International Conference on Informatics in Control, Automation and Robotics (ICINCO 2005), 3:150-155, Barcelona, Spain, September, 2005.

Author : Patrick Rößler, Frederik Beutler, Uwe D. Hanebeck
Title : A Framework for Telepresent Game-Play in Large Virtual Environments
In : Proceedings of the 2nd International Conference on Informatics in Control, Automation and Robotics (ICINCO 2005)
Date : September 2005

Abstract

In this paper we present a framework that provides a novel interface
to avatar control in immersive computer games. The user\'s motion
is tracked and transferred to to the game environment. This motion
data is used as control input for the avatar. The game graphics are
rendered according to the avatar\'s motion and presented to the user
on a head-mounted display. As a result, the user immerses into the
game environment and identifies with the avatar. However, without
further processing of the motion data, the virtual environment would
be limited to the size of the user\'s real environment, which is not
desirable. By using Motion Compression, the framework allows exploring
an arbitrarily large virtual environment while the user is actually
moving in an environment of limited size. Based on the proposed framework,
two game applications were implemented, a modification of a commercially
available game and a custom designed game. These two applications
prove, that a telepresence system using Motion Compression is a highly
intuitive interface to game control.

Proceedings of the 2005 IEEE International Conference on Intelligent Robots and Systems (IROS 2005), pp. 2495-2500, Edmonton, Canada, August, 2005.

Author : Patrick Rößler, Frederik Beutler, Uwe D. Hanebeck, Norbert Nitzsche
Title : Motion Compression Applied to Guidance of a Mobile Teleoperator
In : Proceedings of the 2005 IEEE International Conference on Intelligent Robots and Systems (IROS 2005)
Date : August 2005

Abstract

Telepresence aims at giving a human user the impression of being present
in a remote environment. However, the user is actually situated in
a user environment and his motion is tracked. A mobile teleoperator
in the remote environment replicates this motion. The user can thus
control the mobile teleoperator\'s locomotion by walking. A stereo-camera
system mounted on the mobile teleoperator constantly records live
camera images and transfers them to the user environment, where they
are presented to the user on a head-mounted display. This paper presents
a long distance experiment, in which a mobile teleoperator was controlled
over a standard internet connection by natural locomotion. Without
further processing of the user\'s motion data, however, only exploration
of a remote environment of the same size or smaller than the user
environment is possible. As this is not desirable, we use Motion
Compression, an optimal nonlinear transformation of the user\'s path.
This algorithm allows controlling free motion in an arbitrarily large
target environment from a limited user environment.

Proceedings of the 2005 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2005), 4:989-992, Philadelphia, Pennsylvania, USA, March, 2005.

Author : Frederik Beutler, Uwe D. Hanebeck
Title : Closed-Form Range-Based Posture Estimation Based on Decoupling Translation and Orientation
In : Proceedings of the 2005 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2005)
Date : March 2005

Abstract

For estimating the posture, i.e., position and orientation, of an
extended target based on range measurements, a new closed-form solution
is proposed, which is based on decoupling position and orientation.
For decoupling, any procedure for range-based localization of point
targets, i.e., for mere position estimation, can be used. The new
solution is suboptimal, but nevertheless provides good accuracy and
is very practical from an application point of view.

Proceedings of the 3rd IEEE Conference on Sensors (Sensors 2004), 1:413-416, Vienna, Austria, October, 2004.

Author : Frederik Beutler, Uwe D. Hanebeck
Title : A New Nonlinear Filtering Technique for Source Localization
In : Proceedings of the 3rd IEEE Conference on Sensors (Sensors 2004)
Date : October 2004

Abstract

A new model-based approach for estimating the parameters of an arbitrary
transformation between two discrete-time sequences will be introduced.
One sequence is interpreted as part of a nonlinear measurement equation,
the other sequence is typically measured sequentially. Based on every
measured value, the probability density function of the parameters
is updated using a Bayesian approach. For the evolution of the system
over time, a system equation is included. The new approach provides
a high update rate for the desired parameters up to the sampling
rate with high accuracy. It will be demonstrated for source localization
of a speaker, where the parameters describe the position of the source.