Abstract
Arrays with sparse and random microphone deployment are known to be capable of delivering high quality far-field images without grating lobes. Numerical simulations are undertaken in this thesis to optimize the microphone deployment. Global optimization techniques including the Monte Carlo (MC) algorithm, the Simulated Annealing (SA) algorithm and the Intra-Block Monte Carlo (IBMC) algorithms are exploited to find the optimal microphone deployment efficiently. As predicted by the conventional wisdom, the results reveal that randomized deployment is required to avoid grating lobes. The combined use of the SA and the IBMC algorithms enables efficient search for satisfactory deployment with excellent beam pattern and relatively uniform distribution of microphones. In Direction of arrival (DOA) estimation, the planar wave sources are assumed to be spherical wave sources in this thesis. Far-field acoustic imaging algorithms including the delay and sum (DAS) algorithm, the time reversal (TR) algorithm, the single input multiple output equivalent source inverse filtering (SIMO-ESIF) algorithm, the Minimum Variance Distortionless Response (MVDR) algorithm and the Multiple Signal Classification (MUSIC) algorithm are employed to estimate DOA. Results show that the MUSIC algorithm can attain the highest resolution of localizing sound sources positions.