Acoustical beamforming methods allow the creation of high-gain, directed acoustic antennas using non-directional microphones. To determine the location of noise sources and the distribution of spatially distributed sources, microphone arrays using beamforming principles are also utilized in numerous industrial applications. The exact localization is supported by various post-processing algorithms (also known as image-cleaning algorithms).
The aim of this thesis is to examine the application of acoustical beamforming to moving noise sources. The detection, tracking, and acoustical characterization of moving sources involve many challenges. One such example is to take the Doppler effect or acoustic focusing on the moving target into account. As an example application, the thesis aims to detect and track an unmanned helicopter (UAV).
In the thesis I present the theory of acoustical beamforming, its signal processing aspects, and the image-cleaning algorithms for source localization (MUSIC, DAMAS, CLEAN). I examine the operation of beamforming methods with moving noise sources, and use the simulation framework in a self-made Matlab environment for the tests. I implement my own algorithms for tracking detected moving sources using a nonlinear Kalman filter and acoustical focusing on such sources. During the tracking of the source, a frequency tracking procedure optimized for rotor noise is performed. I demonstrate the simulations associated with the sample application with a pre-recorded voice of a quadrocopter. I also adapted the functionality developed in Matlab environment into C++ programming language to reduce the processing time.