Abstract
Farfield and nearfield microphone arrays are proposed for noise source identification (NSI) and sound field visualization (SFV). Farfield acoustic imaging algorithms including the delay and sum (DAS) algorithm, the minimum variance distortionless response (MVDR) algorithm and the multiple signal classification (MUSIC) algorithm are employed to estimate direction of arrival (DOA). Results show that the MUSIC algorithm can attain the highest resolution of localizing sound sources positions. In the nearefield array signal processing, one formulation derived from discretizing the simple layer potential is termed the indirect equivalent source model (ESM)-based nearfield acoustical holography (NAH), while another formulation derived from discretizing the Kirchhoff-Helmholtz integral equation is termed the direct ESM-based NAH. In the use of ESM NAH, the choice of parameters including retract distance and average area of element is of vital importance. These parameters are optimized, with the aid of the golden section search and parabolic interpolation (GSS-PI) algorithm and the simulated annealing (SA) algorithm, for the direct and indirect ESM formulations. Instead of directly solving the inverse problem, the forward problem is solved in a recursive manner akin to the approach adopted by using recursive Wiener filtering. The approaches proposed are based on a state-space formulation employing the Kalman filter-based state observer and particle filter-based state estimator. The state observer and estimator are adaptive in nature and capable of tracking dynamic variation of sound field, even in the presence of noises and perturbations. Optimum weighting coefficients and inverse filters for microphone arrays can be accomplished, with the aid of a systematic methodology of mathematical programming. Both farfield and nearfield array problems are formulated in terms of compressive sampling (CS) and convex optimization (CVX) formalisms. CVX is applied to beamformer design, pressure field reconstruction, source separation and modal analysis with satisfactory performance in both nearfield and farfield microphone arrays. Design of optimal beamformers that withstand system errors such as channel mismatch, sensor position error, and pointing error has been a key issue in real-world applications of arrays. This thesis also examines the effects of system errors on beamformer performance from a statistical perspective. In practical applications where only patch array with scarce sensors are available, the ESM-based interpolation (ESM-IP), under-determined ESM (UD-ESM), direct basis function model (D-BFM) and BFM-based interpolation (BFM-IP) are proposed to reconstruct source velocity with sound filed interpolation. These methods were compared with the direct ESM (D-ESM) method. In the BFM-based NAH, basis functions including planar and spherical wave functions are used. CS is exploited in the BFM-IP and the D-BFM in light of CVX method. It is desirable to enhance the image resolution based on a sparse array configuration. As indicated by the simulation and experiment results, the proposed technique proved effective in identifying sources of many practical examples, including wooden box experiment, noncontact modal analysis of plate and machine tools.