Communications Engineering - Project Description
Velocity estimation for sequences of sparse images
Particle Image Velocimetry (PIV) and Particle Tracking Velocimetry (PTV) are important imaging techniques for flow characterization. They are applied in many different areas from the optimization of combustion processes to propulsion systems of ships. Particles injected into the liquid flow have to be detected and tracked by using high speed CCD or CMOS cameras with high spatial resolution. Meanwhile, three-dimensional motions of several thousand particles can be detected and tracked with a spatial resolution of 0.1 pixels allowing the characterization of instationary and turbulent flows. The basic limitation of PIV and other multi-dimensional imaging techniques is the lack of real-time processing for a high temporal resolution (frame rates above 100Hz). Hence, imaging techniques cannot be applied for process measurement technology. Furthermore, the required data rates for frame rates in the kHz range are extremely high so that the limited size of state-of-the-art RAMs restricts the process observation time to only a few seconds which is not sufficient for a meaningful analysis. Therefore, this project pursues two different approaches.
On one hand, a multidimensional estimation of the motion vector field has to be developed by using stochastically sampling of the image date. The motion vector field is directly estimated from two or more images without analyzing their individual features or particles. Traditional approaches consider complete images and using phase correlation, cross-correlation (PIV) or the filtering with periodic spatial structures (spatial filtering technique). In this project, these approaches have to be generalized to stochastically sampled images. The real-time solution comes at the expense of a reduced data rate.
On the other hand, the recording duration shall be extended by extreme subsampling while keeping the spatial resolution in a subsequent offline processing high. Compared to the resolution of the camera sensor only a few particles are to be detected. Hence, the signal to be detected is sparse and techniques like compressed sensing or spectral estimation can be applied. They shall be extended and improved for the problem at hand and compared with state-of-the-art techniques.
German National Science Foundation (DFG), 2013 - 2015
Untersuchungen zur optischen Tiefpassfilterung für die Partikelpositionsschätzung bei Unterabtastung des Sensors und mittels spektraler Analyse. Proc. of the 23. Fachtagung Lasermethoden in der Strömungsmesstechnik (GALA) 2015, Dresden, Germany, 2015
Motion Estimation for Particle Images in a Finite Rate of Innovation Framework. 10th International ITG Conference on Systems, Communications and Coding (SCC'2015), Hamburg, Germany, 2015
Exploiting the Cramér-Rao Bound for Optimised Sampling and Quantisation of FRI Signals. Proc. of the 48th Asilomar Conference on Signals, Systems and Computers 2014, pp. 1468-1472, Pacific Grove, CA, USA, 2014
ISBN: 978-1-4799-8295-0, doi:10.1109/ACSSC.2014.7094706
Schätzung der Partikelposition mittels spektraler Analyse aus reduzierten Datenmengen. Proc. of the 22. Fachtagung Lasermethoden in der Strömungsmesstechnik (GALA) 2014, Karlsruhe, Germany, 2014
Cramér-Rao Bound for Sampling & Reconstruction of FRI Signals. Proc. of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2014, pp. 1808-1812, Florence, Italy, 2014
Aliasing-Tolerant Sub-Nyquist Sampling of FRI Signals. Proc. of the IEEE International Conference on Communications (ICC) 2013, pp. 4957 - 4961, Budapest, Hungary, 2013
ISSN: 1550-3607, doi:10.1109/ICC.2013.6655364
Robustness of Aliasing-Tolerant Sub-Nyquist Sampling with Application to Particle Localisation. Proc. of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 5435 - 5439, Vancouver, Canada, 2013
ISSN: 1520-6149, doi:10.1109/ICASSP.2013.6638702
High Resolution Particle Detection via Spectral Estimation. 9th International ITG Conference on Systems, Communications and Coding (SCC'2013), Munich, Germany, 2013
Extension of SoS Sampling Kernels for 2-D FRI Problems. In: Electronics Letters, vol. 48, no. 9 (4/2012), pp. 527-528
ISSN: 0013-5194, doi:10.1049/el.2012.0690