Acoustic Target Identification and Localization

声学目标识别和定位

• 批准号: 0434161
• 负责人: Gert Cauwenberghs
• 金额: $ 27.59万
• 依托单位: Johns Hopkins University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-08-01 至 2006-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0434161&HistoricalAwards=false
• 关键词: Acoustic; Target; Identification; Localization

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.

该项目研究信号处理和机器学习算法，用于识别和定位声学场景中的一个或多个目标。这些算法将被映射到适合与微功耗混合信号硬件集成的并行架构上。受生物学启发的梯度流信号表示使用亚波长孔径的微型阵列盲目地分离和定位目标。支持向量机识别局部目标的时频特征。这个为期一年的项目的目标是确定自主传感器可实现的能源效率和集成密度及其在大规模网络中部署的可行性，并使用硬件原型评估该概念。将采用唤醒检测的高级电源管理来降低待机功耗。这项工作还将研究将声学传感器嵌入 CMOS 电路的有效方法，以实现高度集成的定向和智能声学传感器。微型集成和微功耗操作对于为传感器网络中的分布式智能提供自主传感和处理节点至关重要。该项目的成果将推动声学传感技术的发展，用于监视、国土安全，并帮助士兵在数字战场上提高意识。研究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。