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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.

In the first project phase, the suitability of the direct estimation of a motion vector field by correlation and spatial filtering techniques and the reconstruction of sparse PTV signals by compressed sensing has been shown. On the one hand, the project extension will focus on methodically new methods. An innovative approach directly estimates the motion vector field by using modified optical concept. Furthermore, priors shall be intensively used to improve the reconstruction of sparse signals, and phase retrieval approaches shall overcome the nonlinear sensor problem. On the other hand, results obtained by simulations shall be experimentally verified. Therefore, an existing experimental system has to be extended by components for implementing an optical filter. The proof of suitability for the examined approaches will be a major step towards a more efficient and powerful PIV / PTV technique.


German National Science Foundation (DFG), 2016 - 2017