Acoustic Target Identification and Localization

该项目研究用于识别和定位声学场景中的一个或多个目标的信号处理和机器学习算法。这些算法将被映射到一个适合与微功率混合信号硬件集成的并行架构上。一种受生物启发的梯度流信号表示法使用子波长孔径的微型阵列盲地分离和定位目标。支持向量机识别定位目标的时频特征。这个为期一年的项目的目标是确定自主传感器的可实现的能效和集成密度，以及其在大规模网络中部署的可行性，并使用硬件原型对概念进行评估。使用唤醒检测的高级电源管理将用于减少待机电源。这项工作还将研究将声传感器嵌入到cmos电路中的有效方法，以实现高度集成的定向和智能声传感器。为了在传感器网络中为分布式智能提供一个自主的感知和处理节点，微型集成和微功率操作是必不可少的。该项目的成果将推动用于监视、国土安全和帮助士兵在数字战场上意识到的声音传感技术的最先进水平。这一结果还将影响智能助听器和其他辅助听力技术以及人机界面的新发展。

This project investigates signal processing and machine learning algorithms for identifying and localizing one or multiple targets in the acoustic scene. The algorithms will be mapped onto a parallel architecture suitable for integration with micro-power mixed-signal hardware. A biologically inspired gradient flow signal representation blindly separates and localizes targets using a miniature array of sub-wavelength aperture. A support vector machine identifies the time-frequency signatures of the localized targets. The goal of the one-year project is to determine the achievable energy efficiency and integration density of the autonomous sensor and the feasibility of its deployment in a large-scale network, and to evaluate the concept using hardware prototypes. Advanced power management using wake-up detection will be pursued to reduce standby power. The effort will also investigate efficient means of embedding acoustic sensors onto CMOS circuits towards a highly integrated directional and intelligent acoustic sensor. Miniature integration and micro-power operation are essential to provide an autonomous sensing and processing node for distributed intelligence in a sensor network. The outcomes of this project will advance the state of the art in acoustic sensing technology for surveillance, homeland security, and as an aid to the soldier's awareness in the digital battlefield. The results will also impact new developments in intelligent hearing aids and other assistive listening technologies and human-computer interfaces.