利用隨機配置陣列發展遠場聲學影像演算法

Abstract

稀疏且隨機配置的麥克風陣列已知可以用來傳遞遠場的影像而不會產生鬼葉瓣的問題。在這篇論文中，數值模擬被用來最佳化麥克風的配置。全域最佳化技術包括蒙地卡羅法、模擬退火法和內部方格蒙地卡羅法被用來有效率地尋找最佳的麥克風配置。如常理所知，模擬結果顯示出要避免鬼葉瓣的出現，隨機配置麥克風是必要的。而結合模擬退火法和蒙地卡羅法的方法可以有效率的找到一個令人滿意的配置，這個配置能得到傑出的波束圖和相對較均勻的麥克風分布。在到達方向的估測中，平面波的聲源被視為球面波。遠場聲學影像的方法包括延遲和相加法、時間反轉法、單進多出等效聲源反逆濾波法、最小變異無失真響應法和多重信號分類法被用來估測聲源位置。結果顯示多重信號分類法在定位噪音源位置上可得到最佳的結果。

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.