Agenda

MSc SS Thesis Presentation

Direction-of-Arrival Estimation using an Unsynchronized Array of Acoustic Vector Sensors

Bart Coonen

Direction-Of-Arrival (DOA) estimation of acoustic signals is of great interest in various applications including battlefield acoustic and noise localization. Acoustic sensors are employed in an array configuration to estimate DOAs based on the time differences of arrival DOAs. However, the acoustic sensors in the network have all their own Data AcQuisition (DAQ) unit with independent clocks than, it might not be possible to perfectly synchronize the network which affects the performance of the time differences of arrival reliably.  

In this thesis  we consider the issue of clock synchronization errors in a network where Acoustic Vector Sensors (AVSs) are used. AVSs are shown to be advantageous in terms of direction finding compared to conventional Acoustic Pressure Sensors (APSs) due to their directional particle velocity measurement capability. Initiallity the measurement model for AVSs is presented. After that the behavior of the clocks is incorporated in the measurement model of the full array setup. Subsequently, the effects of the clocks on the MVDR DOA estimation method is discussed.

  The model with clock errors is used in the development of three new DOA-estimation methods. The first two techniques are eigenstructure methods that are capable of finding the DOAs regardless of the accuracy of the synchronization. However, to find the DOAs with high accuracy in a real-time application these methods are not due to their high computational cost. Alternatively, the third proposed algorithm takes the DOA estimate from previous methods with low accuracy as its input. The algorithm estimates the DOA in an iterative fashion with high accuracy based on these estimates with low accuracy.  

Finally, measurements are conducted in a controlled environment in order to show that these methods are usable in practical situations.

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Overview of MSc SS Thesis Presentation

Agenda

Microelectronics Colloquium

Sten Vollebregt, Massimo Mastrangeli, Daniele Cavallo

Tenure track colloquium

Daniele Cavallo (TS group); wideband phased arrays for future wireless communication terminals, Massimo Mastrangeli (ECTM Group); Towards smart organs-on-chip, Sten Vollebregt (ECTM group) Emerging electronic materials: from lab to fab

Signal Processing Seminar

Krishnaprasad Nambur Ramamohan

Signal processing algorithms for acoustic vector sensors

Symposium MRI for Low-Resource Setting

Steven Schiff, Johnes Obungoloch

Sustainable Low-Field MRI Technology for Point of Care Diagnostics in Low-Income Countries

Kick-off meeting of the project "A sustainable MRI system to diagnose hydrocephalus in Uganda"

Signal Processing Seminar

Peter Gerstoft

Machine learning in physical sciences

Machine learning (ML) is booming thanks to efforts promoted by Google. However, ML also has use in physical sciences. I start with a general overview of ML for supervised/unsupervised learning. Then I will focus on my applications of ML in array processing in seismology and ocean acoustics. This will include source localization using neural networks or graph processing. Final example is using ML-based tomography to obtain high-resolution subsurface geophysical structure in Long Beach, CA, from seismic noise recorded on a 5200-element array. This method exploits the dense sampling obtained by ambient noise processing on large arrays by learning a dictionary of local, or small-scale, geophysical features directly from the data.

Signal Processing Seminar

Aydin Rajabzadeh

manufacturing defect detection