Research

Marek's research is focused within the area of ground sensor networks - data fusion from multiple sensors, algorithms and techniques for localisation, calibration and alignment of ground sensors. Recently his interest is mainly in application of Bayesian method to sparse parameter estimation as well as Monte Carlo methods. His supervisor is Fredrik Gustafsson.

Theses

• [T1] M. Syldatk, On Calibration of Ground Sensor Networks Licentiate of Engineeting Thesis, Linköping University, Linköping, September 2013. [pdf]

Journal papers

• [J*] M. Syldatk, F. Gustafsson, Expectation Maximization Algorithm for Simultaneous Tracking and Sparse Calibration of Sensor Networks Submitted to IEEE Transactions on Signal Processing (under review)

Conference Papers

• [C2] M. Syldatk, F. Gustafsson, Simultaneous Tracking and Sparse Calibration in Ground Sensor Networks using Evidence Approximation Proceedings of the 38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Vancouver, Canada, May 2013. (Paper was nominated to the ICASSP2013 Machine Learning for Signal Processing (MLSP) Best Student Paper Candidates) [pdf]
  
  
• [C1] M. Syldatk, F. Gustafsson, E. Sviestins, Expectation Maximization Algorithm for Calibration of Ground Sensor Networks using a Road Constrained Particle Filter Proceedings of the 15th International Conference on Information Fusion, Singapore, July 2012. [pdf]