Infrasound Signal Classification

阿拉斯加当地的次声环境有丰富的自然和人为源。 对于许多不同的应用程序来说，识别源并对其进行本地化是一个挑战。 UAF的次声组在阿拉斯加和南极洲运行了几年。主阵列由八个麦克风组成，这些麦克风被布置成在感兴趣的频带上没有空间混叠的情况下获得宽带操作。这些阵列由UAF集团运营和维护，作为联合国全面禁止核试验条约办公室（CTBTO）国际监测系统（IMS）的一部分。 第二个由四个麦克风组成的阵列由UAF集团作为研究阵列运营。该小组使用该阵列测试风噪声降低方法，麦克风性能和不同阵列几何形状下的阵列性能。 作为确定次声信号源过程的一部分，开发了一种原型定位算法，该算法允许估计到信号源的距离，以确定高达阵列直径10至15倍的信号源位置。定位器算法已被广泛使用的情况下，信号噪声水平很高，它似乎是强大的。研究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角估计方差的估计值。 信号源的描述和分类将提高在信号检测操作中区分已知的自然源和人造源的能力。发展一种估计源距离的算法来伴随传统的方位估计，也将有益于使用声学方法的源定位的一般领域。该算法应可转移到任何检测程序使用传感器阵列。

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.