Infrasound Signal Classification

次声信号分类

• 批准号: 0433392
• 负责人: John Olson
• 金额: $ 14.75万
• 依托单位: University of Alaska Fairbanks Campus
• 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: 2004-08-15 至 2011-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0433392&HistoricalAwards=false
• 关键词: Infrasound; Signal; Classification

The local infrasound environment in Alaska is rich in both natural and human-generated sources. Identifying the source and localizing it is a challenge with many different applications. The Infrasound group at UAF has operated arrays in Alaska and Antarctica for several years. The primary arrays are comprised of eight microphones arranged to obtain wide-band operation with no spatial aliasing over the frequency band of interest. These arrays are operated and maintained by the UAF group as part of the International Monitoring System (IMS) of the United Nations Comprehensive Nuclear Test-Ban Treaty Office (CTBTO). A second array of four microphones is operated by the UAF group as a research array. The group has used this array to test methods of wind-noise reduction, microphone performance and array performance under varying array geometries. As part of the process of identifying infrasound signal sources a prototype locator algorithm was developed that allows estimation of the range to the signal source for locations of the signals source of up to 10 to 15 times the array diameter. The locator algorithm has been used extensively in cases where signal-to-noise levels are high and it appears to be robust. Investigators will continue to study the method and investigate its behavior under a range of signal-to-noise conditions, as well as develop an estimator of the variance in the estimates of range and azimuth. The description and classification of signal sources will improve the ability to discriminate against known natural and man-made sources in signal detection operations. The development of an algorithm to estimate source range to accompany the traditional azimuth estimates will also benefit the general area of source localization using acoustic methods. This algorithm should be transferable to any detection program using arrays of sensors.

阿拉斯加当地的次声环境既有丰富的自然来源，也有人为来源。对于许多不同的应用程序，识别来源并将其本地化是一项挑战。UAF的次声小组已经在阿拉斯加和南极洲操作阵列多年。主阵列由8个麦克风组成，这些麦克风被布置成在感兴趣的频带上获得没有空间混叠的宽带操作。这些阵列由UAF小组作为联合国全面禁止核试验条约办公室(禁核试条约办公室)国际监测系统的一部分进行操作和维护。第二个由四个麦克风组成的阵列由UAF集团作为研究阵列进行操作。该小组已经使用这种阵列来测试风噪降低方法、麦克风性能以及在不同阵列几何结构下的阵列性能。作为识别次声信号源过程的一部分，开发了一种原型定位器算法，该算法允许估计信号源的距离，以确定信号源的位置，最大可达阵列直径的10至15倍。定位器算法已被广泛应用于信噪比较高且表现出健壮性的情况。调查人员将继续研究该方法，并调查其在一系列信噪比条件下的行为，以及开发距离和方位估计中方差的估计器。信号源的描述和分类将提高在信号检测作业中区分已知的自然和人造信号源的能力。伴随着传统方位角估计的声源距离估计算法的发展，也将有利于利用声学方法进行声源定位的一般领域。这种算法应该可以移植到任何使用传感器阵列的检测程序中。