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Marcus Baum

	Dipl.-Inform. Research Assistant, Ph.D. Student
	Address:	Karlsruher Institut für Technologie Institut für Anthropomatik Gebäude 50.20 Raum 130 Adenauerring 2 D-76131 Karlsruhe
	Walk-in hours:	on arrangement
	Phone:	+49-721-608-46797
	E-mail:	Marcus.Baum@kit.edu

Academic Career

Research Interests

• Extended object and group tracking

My research deals with Bayesian methods for tracking an extended object or a group target, which gives rise to a varying number of measurements from different measurement sources.

For this purpose, the shape of the object is estimated in addition to its kinematic state (e.g., position and velocity).

Marcus Baum, Benjamin Noack, Uwe D. Hanebeck,
Random Hypersurface Mixture Models for Tracking Multiple Extended Objects,


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.

Marcus Baum, Uwe D. Hanebeck,
Shape Tracking of Extended Objects and Group Targets with Star-Convex RHMs,


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



Author : Marcus Baum, Uwe D. Hanebeck
Title : Shape Tracking of Extended Objects and Group Targets with Star-Convex RHMs
In : Proceedings of the 14th International Conference on Information Fusion (Fusion 2011)
Date : July 2011


Abstract

This paper is about tracking an extended object or a group target, which gives rise to a varying number of measurements from different measurement sources.
For this purpose, the shape of the target is tracked in addition to its kinematics. The target extent is modeled with a new approach called Random Hypersurface Model (RHM)
that assumes varying measurement sources to lie on scaled versions of the shape boundaries. In this paper, a star-convex RHM is introduced
for tracking star-convex shape approximations of targets. Bayesian inference for star-convex RHM is performed by means of a Gaussian-assumed state estimator
allowing for an efficient recursive closed-form measurement update. Simulations demonstrate the performance of this approach for typical extended object and group tracking scenarios.

Winner Best Student Paper Award

• Association-free methods for multi-target tracking

I am investigating the use of symmetric state transformation for multi-target tracking problems.

For instance, symmetric state transformations can be used for deriving optimal point estimates based on multi-target densities.

By this means, the known shortcomings of MMSE estimates for multi-target states can be avoided.

Marcus Baum, Uwe D. Hanebeck,
Using Symmetric State Transformations for Multi-Target Tracking,


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



Author : Marcus Baum, Uwe D. Hanebeck
Title : Using Symmetric State Transformations for Multi-Target Tracking
In : Proceedings of the 14th International Conference on Information Fusion (Fusion 2011)
Date : July 2011


Abstract

This paper is about the use of symmetric state transformations for multi-target tracking. First, a novel method for obtaining point estimates for multi-target states is proposed.
The basic idea is to apply a symmetric state transformation to the original state in order to compute a minimum mean-square-error (MMSE) estimate in a transformed state.
By this means, the known shortcomings of MMSE estimates for multi-target states can be avoided. Second, a new multi-target tracking method based on state transformations is suggested,
which entirely performs the time and measurement update in a transformed space and thus, avoids the explicit calculation of data association hypotheses and removes the target identity
from the estimation problem. The performance of the new approach is evaluated by means of tracking two crossing targets.

• Curve fitting

Fitting curves, e.g., ellipses or circles, to noisy data points is a fundamental sensor data processing problem.

I am working on Bayesian state estimation techniques, e.g., Gaussian estimators, for curve fitting.

Marcus Baum, Uwe D. Hanebeck,
Fitting Conics to Noisy Data Using Stochastic Linearization,


Proceedings of the 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2011), San Francisco, California, USA, September, 2011.



Author : Marcus Baum, Uwe D. Hanebeck
Title : Fitting Conics to Noisy Data Using Stochastic Linearization
In : Proceedings of the 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2011)
Date : September 2011


Abstract

Fitting conic sections, e.g., ellipses or circles, to
noisy data points is a fundamental sensor data processing
problem, which frequently arises in robotics. In this paper, we
introduce a new procedure for deriving a recursive Gaussian
state estimator for fitting conics to data corrupted by additive
Gaussian noise. For this purpose, the original exact implicit
measurement equation is reformulated with the help of suitable
approximations as an explicit measurement equation corrupted
by multiplicative noise. Based on stochastic linearization, an
efficient Gaussian state estimator is derived for the explicit
measurement equation. The performance of the new approach
is evaluated by means of a typical ellipse fitting scenario.

• Sensor data fusion
• System and estimation theory

Teaching

• Tutor and coordinator of the lecture Lokalisierung mobiler Agenten (summer term 2009, 2010 and 2011)
• Supervising tutor of the laboratory Praktikum: Forschungsprojekt "Intelligente Sensor-Aktor-Systeme" (winter term 2007/2008 to summer term 2009 )

Study/Diploma Thesis Subjects

in German

Activities

• International Conference on Information Fusion 2011, Chicago, IL, USA
  • TPC member
  • Co-organizer of the Special Session "Extended Object and Group Tracking"
• International Conference on Information Fusion 2010, Edinburgh, UK
  • TPC member
  • Co-organizer of the Special Session "Extended Object and Group Tracking"
• International Conference on Information Fusion 2009, Seattle, Washington, USA
  • Co-organizer of the Special Session "Extended Object and Group Tracking"

All Publications

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

Author : Marcus Baum, Florian Faion, Uwe D. Hanebeck
Title : Modeling Extended Targets as Multiplicative Noise (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 : Marcus Baum, Peter Willett, Uwe D. Hanebeck
Title : Calculating Exact MMOSPA estimates for Particle Densities (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 : Florian Faion, Marcus Baum, Uwe D. Hanebeck
Title : Tracking 3D Shapes in Noisy Point Clouds with Random Hypersurface Models (preliminary title)
In : Proceedings of the 15th International Conference on Information Fusion (Fusion 2012) (to appear)
Date : July 2012

Signal Processing, Sensor Fusion, and Target Recognition XXI (Proceedings of SPIE), Baltimore, Maryland, USA, April, 2012.

Author : Yvonne Fischer, Marcus Baum, Fabian Flohr, Uwe Hanebeck, Jürgen Beyerer
Title : Evaluation of Tracking Methods for Maritime Surveillance
In : Signal Processing, Sensor Fusion, and Target Recognition XXI (Proceedings of SPIE)
Date : April 2012

Abstract

In this article we present an evaluation of different target tracking methods based on various simulated scenarios
in the maritime domain. We implemented well known algorithms (JIPDA, Linear Multi Target PDA, Linear Joint PDA, Monte Carlo Markov
Chain Data Association) and integrated them into a data fusion architecture. The algorithms have been compared based on extensions
of the Optimal Subpattern Assignment metric. Also further performance measures are used to get a single score for each algorithm.
As no single algorithm is equally well fitted to all tested scenarios, our results show which algorithms fits best
for specific scenarios.

IEEE Aerospace and Electronic Systems Magazine, accepted March 2012 (to appear).

Author : Marcus Baum, Uwe D. Hanebeck
Title : Simultaneous Tracking and Shape Estimation of Extended Targets
In : IEEE Aerospace and Electronic Systems Magazine
Date : accepted March 2012 (to appear)

IEEE Transactions on Aerospace and Electronic Systems, accepted August 2011 (to appear).

Author : Marcus Baum, Uwe D. Hanebeck
Title : A Novel Approach for Extended Object Tracking based on Combined Set-Theoretic and Stochastic Fusion
In : IEEE Transactions on Aerospace and Electronic Systems
Date : accepted August 2011 (to appear)

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 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2011), San Francisco, California, USA, September, 2011.

Author : Marcus Baum, Uwe D. Hanebeck
Title : Fitting Conics to Noisy Data Using Stochastic Linearization
In : Proceedings of the 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2011)
Date : September 2011

Abstract

Fitting conic sections, e.g., ellipses or circles, to
noisy data points is a fundamental sensor data processing
problem, which frequently arises in robotics. In this paper, we
introduce a new procedure for deriving a recursive Gaussian
state estimator for fitting conics to data corrupted by additive
Gaussian noise. For this purpose, the original exact implicit
measurement equation is reformulated with the help of suitable
approximations as an explicit measurement equation corrupted
by multiplicative noise. Based on stochastic linearization, an
efficient Gaussian state estimator is derived for the explicit
measurement equation. The performance of the new approach
is evaluated by means of a typical ellipse fitting scenario.

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, Uwe D. Hanebeck
Title : Shape Tracking of Extended Objects and Group Targets with Star-Convex RHMs
In : Proceedings of the 14th International Conference on Information Fusion (Fusion 2011)
Date : July 2011

Abstract

This paper is about tracking an extended object or a group target, which gives rise to a varying number of measurements from different measurement sources.
For this purpose, the shape of the target is tracked in addition to its kinematics. The target extent is modeled with a new approach called Random Hypersurface Model (RHM)
that assumes varying measurement sources to lie on scaled versions of the shape boundaries. In this paper, a star-convex RHM is introduced
for tracking star-convex shape approximations of targets. Bayesian inference for star-convex RHM is performed by means of a Gaussian-assumed state estimator
allowing for an efficient recursive closed-form measurement update. Simulations demonstrate the performance of this approach for typical extended object and group tracking scenarios.

Winner Best Student Paper Award

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

Author : Marcus Baum, Uwe D. Hanebeck
Title : Using Symmetric State Transformations for Multi-Target Tracking
In : Proceedings of the 14th International Conference on Information Fusion (Fusion 2011)
Date : July 2011

Abstract

This paper is about the use of symmetric state transformations for multi-target tracking. First, a novel method for obtaining point estimates for multi-target states is proposed.
The basic idea is to apply a symmetric state transformation to the original state in order to compute a minimum mean-square-error (MMSE) estimate in a transformed state.
By this means, the known shortcomings of MMSE estimates for multi-target states can be avoided. Second, a new multi-target tracking method based on state transformations is suggested,
which entirely performs the time and measurement update in a transformed space and thus, avoids the explicit calculation of data association hypotheses and removes the target identity
from the estimation problem. The performance of the new approach is evaluated by means of tracking two crossing targets.

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 IEEE ISIF Workshop on Sensor Data Fusion: Trends, Solutions, Applications (SDF 2010), Leipzig, Germany, October, 2010.

Author : Marcus Baum, Michael Feldmann, Dietrich Fränken, Uwe D. Hanebeck, Wolfgang Koch
Title : Extended Object and Group Tracking: A Comparison of Random Matrices and Random Hypersurface Models
In : Proceedings of the IEEE ISIF Workshop on Sensor Data Fusion: Trends, Solutions, Applications (SDF 2010)
Date : October 2010

Abstract

Based on previous work of the authors, this paper provides a comparison
of two different tracking methodologies for extended objects and
group targets, where the true shape of the extent is approximated by
an ellipsoid. Although both methods exploit usual sensor data, i.e.,
position measurements of varying scattering centers, the distinctions
are a consequence of the different modeling of the extent as a symmetric
positive definite random matrix on the one hand and an elliptic random
hypersurface model on the other. Besides analyzing the fundamental
assumptions and a comparison of the properties of these tracking methods,
simulation results are presented based on a static tracking environment

to highlight especially the differences in the update step for the extension estimate.

Proceedings of the 2010 IEEE International Conference on Virtual Environments, Human-Computer Interfaces and Measurement Systems (VECIMS 2010), Taranto, Italy, September, 2010.

Author : Ioana Gheta, Marcus Baum, Andrey Belkin, Jürgen Beyerer, Uwe D. Hanebeck
Title : Three Pillar Information Management System for Modeling the Environment of Autonomous Systems
In : Proceedings of the 2010 IEEE International Conference on Virtual Environments, Human-Computer Interfaces and Measurement Systems (VECIMS 2010)
Date : September 2010

Abstract

This contribution is about an information management and storage system for modeling the
environment of autonomous systems. The three pillars of the system consist of prior
knowledge, environment model and sensory information. The main pillar is the environment model,
which supplies the autonomous system with relevant information about its current environment.
For this purpose, an abstract representation of the real world is created, where instances
with attributes and relations serve as virtual substitutes of entities (persons and objects)
of the real world. The environment model is created based on sensory information about
the real world. The gathered sensory information is typically uncertain in a stochastic
sense and is represented in the environment model by means of Degree-of-Belief (DoB) distributions.
The prior knowledge contains all relevant background knowledge (e.g., concepts organized in ontologies)
for creating and maintaining the environment model. The concept of the three pillar information system
has previously been published. Therefore this contribution focuses on further central properties
of the system. Furthermore, the development status and possible applications as well as evaluation
scenarios are discussed.

Proceedings of the 25th IEEE/ACM International Conference on Automated Software Engineering (ASE 2010), Antwerp, Belgium, September, 2010.

Author : Reiner Hähnle, Marcus Baum, Richard Bubel, Marcel Rothe
Title : A Visual Interactive Debugger Based on Symbolic Execution
In : Proceedings of the 25th IEEE/ACM International Conference on Automated Software Engineering (ASE 2010)
Date : September 2010

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 : Marcus Baum, Uwe D. Hanebeck
Title : Association-free Tracking of Two Closely Spaced Targets
In : Proceedings of the 2010 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2010)
Date : September 2010

Abstract

This paper introduces a new concept for tracking closely spaced targets in Cartesian
space based on position measurements corrupted with additive Gaussian noise.
The basic idea is to select a special state representation that eliminates the target identity
and avoids the explicit evaluation of association probabilities.
One major advantage of this approach is that the resulting likelihood function for this special problem is unimodal.
Hence, it is especially suitable for closely spaced targets.
The resulting estimation problem can be tackled with a standard nonlinear estimator.
In this work, we focus on two targets in two-dimensional Cartesian space.
The Cartesian coordinates of the targets are represented by means of extreme values, i.e.,
minima and maxima. Simulation results demonstrate the feasibility of the new approach.

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 : Marcus Baum, Uwe D. Hanebeck
Title : Tracking a Minimum Bounding Rectangle based on Extreme Value Theory
In : Proceedings of the 2010 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2010)
Date : September 2010

Abstract

In this paper, a novel Bayesian estimator for the minimum bounding axis-aligned rectangle of a point set based on noisy measurements is
derived. Each given measurement stems from an unknown point and is corrupted with additive Gaussian noise.
Extreme value theory is applied in order to derive a linear measurement equation for the problem.
The new estimator is applied to the problem of group target and extended object tracking.
Instead of estimating each single group member or point feature explicitly, the basic idea is to track a summarizing shape, namely the minimum bounding rectangle, of the
group. Simulation results demonstrate the feasibility of the estimator.

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 : Marcus Baum, Ioana Gheta, Andrey Belkin, Jürgen Beyerer, Uwe D. Hanebeck
Title : Data Association in a World Model for Autonomous Systems
In : Proceedings of the 2010 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2010)
Date : September 2010

Abstract

This contribution introduces a three pillar information storage and management
system for modeling the environment of autonomous systems. The main
characteristics is the separation of prior knowledge, environment model and
sensor information. In the center of the system is the environment model, which
provides the autonomous system with information about the current state of the
environment. It consists of instances with attributes and relations as virtual
substitutes of entities (persons and objects) of the real world.
Important features are the representation of uncertain information by means
of Degree-of-Belief (DoB) distributions,
the information exchange between the three pillars as well as creation,
deletion and update of instances, attributes and relations in the environment
model. In this work, a Bayesian method for fusing new observations to the
environment model is introduced. For this purpose, a Bayesian data association
method is derived. The main question answered here is the
observation-to-instance mapping
and the decision mechanisms for creating a new instance or
updating already existing instances in the environment model.

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

Author : Marcus Baum, Vesa Klumpp, Uwe D. Hanebeck
Title : A Novel Bayesian Method for Fitting a Circle to Noisy Points
In : Proceedings of the 13th International Conference on Information Fusion (Fusion 2010)
Date : July 2010

Abstract

This paper introduces a novel recursive Bayesian
estimator for the center and radius of a circle based on
noisy points. Each given point is assumed to be a noisy measurement
of an unknown true point on the circle that is corrupted with known
isotropic Gaussian noise. In contrast to existing approaches, the
novel method does not make assumptions about the true points on
the circle, where the measurements stem from. Closed-form expressions
for the measurement update step are derived. Simulations show that
the novel method outperforms standard Bayesian approaches for
circle fitting.

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 9th IEEE International Symposium on Signal Processing and Information Technology (ISSPIT 2009), Ajman, United Arab Emirates, December, 2009.

Author : Marcus Baum, Uwe D. Hanebeck
Title : Random Hypersurface Models for Extended Object Tracking
In : Proceedings of the 9th IEEE International Symposium on Signal Processing and Information Technology (ISSPIT 2009)
Date : December 2009

Abstract

Target tracking algorithms usually assume that the received measurements
stem from a point source. However, in many scenarios this assumption
is not feasible so that measurements may stem from different locations,
named measurement sources, on the target surface. Then, it is necessary
to incorporate the target extent into the estimation procedure in
order to obtain robust and precise estimation results. This paper
introduces the novel concept of Random Hypersurface Models for extended
targets. A Random Hypersurface Model assumes that each measurement
source is an element of a randomly generated hypersurface. The applicability
of this approach is demonstrated by means of an elliptic target shape.
In this case, a Random Hypersurface Model specifies the random (relative)
Mahalanobis distance of a measurement source to the center of the
target object. As a consequence, good estimation results can be obtained
even if the true target shape significantly differs from the modeled
shape. Additionally, Random Hypersurface Models are computationally
tractable with standard nonlinear stochastic state estimators.

4th German Workshop on Sensor Data Fusion: Trends, Solutions, Applications (SDF 2009), 39th Annual Conference of the Gesellschaft für Informatik e.V. (GI), Lübeck, Germany, October, 2009.

Author : Marcus Baum, Uwe D. Hanebeck
Title : Tracking an Extended Object Modeled as an Axis-Aligned Rectangle
In : 4th German Workshop on Sensor Data Fusion: Trends, Solutions, Applications (SDF 2009), 39th Annual Conference of the Gesellschaft für Informatik e.V. (GI)
Date : October 2009

Abstract

In many tracking applications, the extent of the target object is neglected
and it is assumed that the received measurements stem from a point source. However,
modern sensors are able to supply several measurements from different scattering cen-
ters on the target object due to their high-resolution capability. As a consequence, it
becomes necessary to incorporate the target extent into the estimation procedure. This
paper introduces a new method for tracking the smallest enclosing rectangle of an ex-
tended object with an unknown shape. At each time step, a finite set of noisy position
measurements that stem from arbitrary, unknown measurement sources on the target
surface may be available. In contrast to common approaches, the presented approach
does not have to make any statistical assumptions on the measurement sources.

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

Author : Marcus Baum, Uwe D. Hanebeck
Title : Extended Object Tracking based on Combined Set-Theoretic and Stochastic Fusion
In : Proceedings of the 12th International Conference on Information Fusion (Fusion 2009)
Date : July 2009

Abstract

In this paper, a novel approach for tracking
extended objects is presented. The target object is
modeled as a circular disc such that the center and
extent of the target object can be estimated. At each
time step, a finite set of position measurements that
are corrupted with stochastic noise may be available.
Each position measurement stems from an unknown measurement
source on the extended object. In contrast to existing
approaches, no statistical assumptions about the distribution
of the measurement sources on the extended object are made.
As a consequence, it is necessary to deal with stochastic
and set-valued uncertainties. For this purpose, a novel
combined stochastic and set-theoretic estimator that employs
random hyperboloids to express the uncertainties about the
true circular disc is derived.