MEMS-Based Acoustic Array Technology for Real-Time Monitoring

0097636Nishida这项研究工作的目标是开发一个基于MEMS的声学阵列，利用声学，MEMS设计和制造，自适应信号处理，DSP硬件开发的分布式传感和控制的专业知识。该技术提供了将定向麦克风阵列技术经济地扩展到数百个麦克风的潜力，同时改善了数据处理和便携性。潜在的影响包括用于通信设备的定向麦克风，以及用于减少噪音的工业屏蔽。本研究将探讨麦克风的设计，波束形成算法，和DSP硬件所需的实现基于MEMS的声学阵列。麦克风的设计包括建模，制造和表征的双背板力反馈电容MEMS麦克风作为阵列元件，以避免开环电容麦克风的静电吸合不稳定性，同时产生更宽的带宽，动态范围，和更大的信噪比。由于存在强声学干扰信号，对波束形成算法的要求包括可用于同时干扰抑制的自适应方法。Capon波束形成器是最有效的自适应波束形成方法之一，在雷达和通信领域得到了广泛的应用。本研究的内容包括基于MEMS声阵列的真实的实时自适应声成像算法的实现。这些算法包括：1.任意辐射方向图声源的自适应波束形成算法，2.存在干扰时的导向矢量估计算法，3.具有间隙或缺失数据信息的自适应波束成形，以及4.递归实现移动源的自适应算法。这产生了传统上以非实时方式处理的大量计算问题。 我

0097636NishidaThe objective of this research effort is to develop a MEMS-based acoustic array using expertise in acoustics, MEMS design and fabrication, adaptive signal processing, and DSP hardware development for distributed sensing and control. The technology offers the potential to economically scale directional microphone array technology to hundreds of microphones while improving data handling and portability. The potential impact includes directional microphones for communication devices to industrial screening for noise abatement. This research will explore the microphone design, beamforming algorithms, and DSP hardware required to realize a MEMS-based acoustic array. The microphone design involves the modeling, fabrication, and characterization of a dual back plate force-feedback capacitive MEMS microphone as the array element to avoid the electrostatic pull-in instability of open-loop capactive microphones while yielding a wider bandwidth, dynamic range, and larger signal-to-noise ratio. Due to the presence of strong acoustic interference signals, requirements for the beamforming algorithm include adaptive approaches that can be used for simultaneous interference suppression. One of the most effective adaptive beamforming approaches is the Capon beamformer which has been widely used in many applications in radar and communications. This research includes the implementation of the real time adaptive acoustic imaging algorithms with MEMS-based acoustic arrays. These algorithms include: 1. adaptive beamforming algorithms for acoustic sources with arbitrary radiation patterns, 2. steering vector estimation algorithms in the presence of interference, 3. adaptive beamforming with gapped or missing data information, and 4. recursive implementations of the adaptive algorithms for moving sources. This creates a substantial computation problem that has traditionally been handled in a non-real time manner. I