1. IEEE Signal Processing Magazine
2. Signal Processing Digital Library*
3. Inside Signal Processing Newsletter
4. SPS Resource Center
5. Career advancement & recognition
6. Discounts on conferences and publications
7. Professional networking
8. Communities for students, young professionals, and women
9. Volunteer opportunities
10. Coming soon! PDH/CEU credits
Click here to learn more.
10 years of news and resources for members of the IEEE Signal Processing Society
In this series, we introduce a scientist, who makes use of signal processing techniques for his research findings, by means of an interview. This month, we are happy to introduce Dr. Florian Meyer from Vienna University of Technology, whose research interests mainly focus on message passing algorithms for statistical inference.
1. What are your research interests in the signal processing field?
My research interests focus on statistical signal processing methods related to wireless sensor networks, networks of unmanned autonomous vehicles, localization and tracking, information-seeking control, message passing algorithms, and finite set statistics.
2. Could you briefly introduce your research?
The main theme of my current research is the use of belief propagation (BP) algorithms, which are graph-based signal processing techniques for Bayesian inference, in high-dimensional localization and tracking problems. The BP methodology is powerful because it is very general and provides highly efficient and scalable algorithms. Also, BP-based inference algorithms can be developed quite easily: once a Bayesian estimation problem and a corresponding statistical model are formulated, obtaining an efficient algorithm is essentially a turn-the-crank procedure. Interestingly, many well-known algorithms for navigation and tracking – such as the Kalman filter, the particle filter, and the joint probabilistic data association filter – can be viewed as special instances of BP. Another useful property of BP is that in certain cooperative scenarios involving decentralized wireless sensor networks, the BP approach almost automatically yields a distributed algorithm, that is, the computations are distributed among the sensors and communication is required only between neighboring sensors.
3. In your opinion, what was the most impressive result published in IEEE SPS journals and conferences within the last 12 months?
A recent result that impressed me very much is a new technique for multiobject tracking presented by J. L. Williams in his paper, "An efficient, variational approximation of the best fitting multi-Bernoulli filter" (IEEE Transactions on Signal Processing, vol. 63, no. 1, pp. 258–273, Jan. 2015). Multi-Bernoulli filters are important techniques for multiobject tracking that account for the fact that, typically, the number of objects to be tracked is unknown and time-varying and the objects are unlabeled. The conventional multi-Bernoulli filter is based on certain approximations involving probability generating functionals (PGFs). Since the PGF is a rather nonintuitive mathematical tool, it is difficult to understand how these approximations affect the tracking performance of the resulting filter. By contrast, the alternative multi-Bernoulli filter proposed by J. L. Williams is based on a projection of the true multiobject probability density onto the space of multi-Bernoulli densities with a fixed number of objects. This projection amounts to a minimization of the multiobject Kullback-Leibler divergence and avoids nontransparent approximations in the PGF domain. It is demonstrated in the paper that the new multi-Bernoulli filter significantly outperforms existing tracking techniques in challenging scenarios with a large number of objects located close to each other.
4. Could you introduce an important state-of-the-art research issue (or technology) in this field?
In my opinion, signal processing on graphs combined with Monte Carlo techniques such as importance sampling is a powerful state-of-the art methdodology for Bayesian inference. It is fairly general, very intuitive, and capable of dealing with high-dimensional, nonlinear, and non-Gaussian inference problems.
5. In which way have you been connected first with IEEE SPS (university, conference, etc…)?
I connected to IEEE SPS at the suggestion of my PhD thesis advisor, Prof. Franz Hlawatsch. I have been an IEEE SPS student member and, later, member since March 2012.
6. In which way did you know the IEEE SPS e-NewsLetter?
I started receiving and reading the e-Newsletter when I became a student member of the IEEE SPS society.
Florian Meyer received the Dipl.-Ing. (M.Sc.) and Ph.D. degrees in electrical engineering from Vienna University of Technology, Vienna, Austria in 2011 and 2015, respectively. Since 2011, he has been a Research and Teaching Assistant with the Institute of Telecommunications, Vienna University of Technology. From 2010 to 2014, he also worked as a telecommunications engineer for Schiebel Elektronische Geräte Gmbh, a manufacturer of unmanned aerial vehicles. He was a visiting scholar with the Department of Signals and Systems, Chalmers University of Technology, Gothenburg, Sweden in 2013 and with the NATO STO Centre of Maritime Research and Experimentation (CMRE), La Spezia, Italy in 2014 and 2015. In March 2016 he will join CMRE as Research Scientist.
© Copyright 2020 IEEE – All rights reserved. Use of this website signifies your agreement to the IEEE Terms and Conditions.
A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.