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- Marcus Baum, Benjamin Noack, Uwe D. Hanebeck
- 2010 13th International Conference on Information…
- 2010

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… (More)

- Benjamin Noack, Vesa Klumpp, Dietrich Brunn, Uwe D. Hanebeck
- 2008 11th International Conference on Information…
- 2008

This paper presents a theoretical framework for Bayesian estimation in the case of imprecisely known probability density functions. The lack of knowledge about the true density functions is represented by sets of densities. A formal Bayesian estimator for these sets is introduced, which is intractable for infinite sets. To obtain a tractable filter,… (More)

- Marc Reinhardt, Benjamin Noack, Uwe D. Hanebeck
- 2012 IEEE International Conference on Multisensor…
- 2012

This paper deals with distributed information processing in sensor networks. We propose the Hypothesizing Distributed Kalman Filter that incorporates an assumption of the global measurement model into the distributed estimation process. The procedure is based on the Distributed Kalman Filter and inherits its optimality when the assumption about the global… (More)

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… (More)

- Joris Sijs, Leon Kester, Benjamin Noack
- 17th International Conference on Information…
- 2014

To reduce the amount of data transfer in networked systems measurements are usually taken only when an event occurs rather than periodically in time. However, a fundamental assessment on the response of estimation algorithms receiving event sampled measurements is not available. This research presents such an analysis when new measurements are sampled at… (More)

- Benjamin Noack, Florian Pfaff, Uwe D. Hanebeck
- 2012 15th International Conference on Information…
- 2012

In state estimation theory, stochastic and set-membership approaches are generally considered separately from each other. Both concepts have distinct advantages and disadvantages making each one inherently better suited to model different sources of estimation uncertainty. In order to better utilize the potentials of both concepts, the core element of this… (More)

- Marcus Baum, Benjamin Noack, Frederik Beutler, Dominik Itte, Uwe D. Hanebeck
- 14th International Conference on Information…
- 2011

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… (More)

- Marcus Baum, Benjamin Noack, Uwe D. Hanebeck
- CDC-ECE
- 2011

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 socalled… (More)

- Joris Sijs, Benjamin Noack, Uwe D. Hanebeck
- Proceedings of the 16th International Conference…
- 2013

To reduce the amount of data transfer in networked systems, measurements are usually taken only when an event occurs rather than periodically in time. However, this complicates estimation problems considerably as it is not guaranteed that new sensor measurements will be sampled. In order to cope with such event sampled measurements, an existing state… (More)

- Benjamin Noack, Vesa Klumpp, Uwe D. Hanebeck
- 2009 12th International Conference on Information…
- 2009

In practical applications, state estimation requires the consideration of stochastic and systematic errors. If both error types are present, an exact probabilistic description of the state estimate is not possible, so that common Bayesian estimators have to be questioned. This paper introduces a theoretical concept, which allows for incorporating unknown… (More)