Department of Information Technology

Signal Processing

Currently the following people in our group perform research in this area:

Senior Researcher:

Reserchers:

PhD Students:

Some PhD theses from the group.

Our projects in this area have been supported by the European Research Council (ERC), the Swedish Foundation for Strategic Research (SSF), the Swedish Research Council (VR), Celsius/Saab, and the Swedish Foundation for International Cooperation in Research and Higher Education (STINT).

A Topical Textbook:

Spectral Analysis of Signals by Peter Stoica and Randolph Moses.

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Current research topics:

Some further Signal Processing projects are described on the page of the System Identification group.

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Past research topics:

A selection of our many projects from the past:

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International collaborations:

  • Massachusetts Institute of Technology, Electrical Engineering and Computer Science, Lincoln Laboratory
  • K.U. Leuven, Belgium: ESAT-SISTA
  • University of California, Los Angeles, USA
  • University of Newcastle, Callaghan, Australia: School of Electrical Engineering and Computer Science
  • University of Florida, USA: Spectral Analysis Lab
  • Bell Laboratories, Alcatel-Lucent, NJ, USA: Mathematical Sciences Research Center
  • Royal Institute of Technology, Sweden: School of Electrical Engineering
  • GE Healthcare Technologies, Milwaukee WI, USA
  • Karlstad University, Sweden: Dept. of Electrical Engineering
  • King's College London, London, UK
  • Aalborg University, Denmark: Dept. of Communication Technology
  • Waverider, Toronto, Canada
  • Stanford University, California, USA: Dept of Mathematics
  • Ohio State University: Dept of Electrical and Computer Engineering
  • Cornell University, Ithaca, NY, USA: School of Electrical and Computer Engineering
  • California Institute of Technology, Pasadena, CA, USA

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Some achievements:

  • Best student paper award at EUSIPCO 2011 was given to the paper:
    Signal Processing Algorithms for Removing Banding Artifacts in MRI. Marcus Björk, Erik Gudmundson, Joëlle Barral, and Peter Stoica. In Proceedings of the 19th European Signal Processing Conference (EUSIPCO-2011), European Signals Processing Conference, pp 1000-1004, 2011. (fulltext:print).
  • Several students completed their PhD theses in this research area: Erik Gudmundson, Yngve Selén, Niclas Sandgren, Richard Abrahamsson, Mats Cedervall, Girish Ganesan, Andreas Jakobsson, Erik G. Larsson, Magnus Mossberg, Joakim Sorelius, Tomas Sundin, and Per Ã?hgren.
  • The following selected textbooks on this topic have been published
    • T. Söderström and P. Stoica: System Identification, Prentice Hall International, London, UK, 1989 -- paperback edition, 1994 -- Polish edition 1998;
    • P. Stoica and R. Moses: Spectral Analysis of Signals, Prentice Hall, Upper Saddle River, NJ, 2005.
    • J. Li and P. Stoica (Eds): MIMO Radar Signal Processing, J Wiley&Sons, USA, 2009.
    • J Li and P Stoica (Eds): Robust Adaptive Beamforming, J Wiley&Sons, USA, 2006.
    • G. Giannakis, Y. Hua, P. Stoica and L. Tong (eds): Signal Processing Advances in Wireless and Mobile Communications. Vol I: Trends in Channel Estimation and Equalization; Vol II: Trends in Single- and Multi-User Systems. Prentice-Hall, NJ, 2001.
    • E. G. Larsson and P. Stoica: Space-Time Block Coding for Wireless Communications. Cambridge University Press, Cambridge, UK, May 2003 -- Chinese Edition 2006.
    • Y. Wang, J. Li and P. Stoica: Spectral Analysis of Signals: This Missing Data Case. Morgan & Claypool Publishers, USA, 2005.
    • J. Li, P. Stoica and Z. Wang: Robust Capon Beamforming. In Robust Adaptive Beamforming (J. Li and P. Stoica, Eds). J Wiley & Sons, USA, 2006.
    • A. Scaglione and P. Stoica: Linear Precoding for MIMO Channels. In Space-Time Wireless Systems: From Array Processing to MIMO Communications. Cambridge University Press, Cambridge, UK, 2006.
  • Some 15 papers have been published every year in leading international journals.
  • One researcher of the group has been elected IEEE Fellow for contributions to the area, awarded some best paper prizes, and has been serving in several editorial positions for leading journals.
  • One researcher of the group was given the 1996 Technical Achievement Award of the IEEE Signal Processing Society, the 2000 IEEE Millenium Medal, the 2002 Eurasip Individual Technical Achievement Award, the 2005 IEE Achievement Medal and the 2006 Society Award of the IEEE Signal Processing Society.
  • A Senior Individual Grant was awarded for the period 1998-2003 by the Swedish Foundation for Strategic Research (SSF) for supporting our research in the Signals and Systems Modelling area. Also an advanced grant was awarded for the period 2008-2012 from the European Research Council, for joint reseach with KTH.

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Selected publications:

Scalable and Passive Wireless Network Clock Synchronization in LOS Environments. Dave Zachariah, Satyam Dwivedi, Peter Handel, and Peter Stoica. In IEEE Transactions on Wireless Communications, volume 16, number 6, pp 3536-3546, 2017. (DOI).
Prediction Performance After Learning in Gaussian Process Regression. Johan Wågberg, Dave Zachariah, Thomas B. Schön, and Peter Stoica. In Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, Proceedings of Machine Learning Research, pp 1264-1272, PMLR, 2017. (Full text, Reference, fulltext:postprint).
Training signal design for correlated massive MIMO channel estimation. Mojtaba Soltanalian, Mohammad Mahdi Naghsh, Nafiseh Shariati, Peter Stoica, and Babak Hassibi. In IEEE Transactions on Wireless Communications, volume 16, number 2, pp 1135-1143, 2017. (DOI).
Locating the Few: Sparsity-aware waveform design for active radar. Heng Hu, Mojtaba Soltanalian, Peter Stoica, and Xiaohua Zhu. In IEEE Transactions on Signal Processing, volume 65, number 3, pp 651-662, 2017. (DOI).
Recursive nonlinear system identification using latent variables. Per Mattsson, Dave Zachariah, and Peter Stoica. In 25th European Research Network System Identification Workshop, 2016.
Prediction performance after learning in Gaussian process regression. Johan Wågberg, Dave Zachariah, Thomas B. Schön, and Peter Stoica. In 25th European Research Network System Identification Workshop, 2016.
Online prediction of spatial fields for radio-frequency communication. Dave Zachariah, Niklas Jaldén, and Peter Stoica. In Proc. 24th European Signal Processing Conference, European Signal Processing Conference, pp 1252-1256, IEEE, Piscataway, NJ, 2016. (DOI).
NOVIFAST: A fast non-linear least squares method for accurate and precise estimation of T1 from SPGR signals.. Gabriel Ramos-Llordén, Arnold Jan den Dekker, Marcus Björk, Marleen Verhoye, and Jan Sijbers. In , 2016.
One-bit compressive sampling with time-varying thresholds for sparse parameter estimation. Christopher Gianelli, Luzhou Xu, Jian Li, and Peter Stoica. In Proc. 9th Sensor Array and Multichannel Signal Processing Workshop, IEEE, Piscataway, NJ, 2016. (DOI).
Recursive identification method for piecewise ARX models: A sparse estimation approach. Per Mattsson, Dave Zachariah, and Peter Stoica. In IEEE Transactions on Signal Processing, volume 64, number 19, pp 5082-5093, 2016. (DOI).
Efficient sum-rate maximization for medium-scale MIMO AF-relay networks. Mohammad Mahdi Naghsh, Mojtaba Soltanalian, Peter Stoica, Maryam Masjedi, and Björn Ottersten. In IEEE Transactions on Wireless Communications, volume 15, number 9, pp 6400-6411, 2016. (DOI).
Rate optimization for massive MIMO relay networks: A minorization-maximization approach. Mohammad Mahdi Naghsh, Mojtaba Soltanalian, Peter Stoica, Maryam Masjedi, and Björn Ottersten. In Proc. 41st International Conference on Acoustics, Speech, and Signal Processing, pp 3611-3615, IEEE, Piscataway, NJ, 2016. (DOI).
Vandermonde decomposition of multilevel Toeplitz matrices with application to multidimensional super-resolution. Zai Yang, Lihua Xie, and Peter Stoica. In IEEE Transactions on Information Theory, volume 62, number 6, pp 3685-3701, 2016. (DOI).
Identification using Convexification and Recursion. Liang Dai. Ph.D. thesis, Uppsala Dissertations from the Faculty of Science and Technology nr 123, Acta Universitatis Upsaliensis, Uppsala, 2016. (fulltext, preview image).
Estimating the order of sinusoidal models using the adaptively penalized likelihood approach: Large sample consistency properties. Khushboo Surana, Sharmishtha Mitra, Amit Mitra, and Peter Stoica. In Signal Processing, volume 128, pp 204-211, 2016. (DOI).

Updated  2016-11-14 11:44:22 by Marcus Björk.