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Source localization using widely separated special microphone arrays: Study of the applicability of higher order statistics

机译:使用广泛分离的特殊麦克风阵列进行源定位:高阶统计的适用性研究

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This paper presents an application implying higher order statistics in source localization. Such techniques have been first introduced in the early 1990's. Some interesting properties of higher order statistics are exploited in zone survey applications. A large global array constituted by a set of moving sub-arrays (soldiers wearing a Head Equipment (HE) equipped with one or more microphones, a GPS receiver and a magnetometer) allows the localization of the menaces near a survey zone. Studying a large global array, with second order statistics high resolution localization techniques, the local informations are not significant enough in order to have an influence on the estimation of the position of the acoustic sources (for instance, the orientation of the HE has no influence on the estimation of the position of the sources). We expect that the use of higher order statistics and their properties concerning especially the virtual equivalent network (VEN) will improve the significance of these local information and thus, allow to have a better estimation of the source position. A first study has been based on simulations, and then, some results with real data, collected in October 2003, are presented.
机译:本文提出了一种在源本地化中隐含高阶统计信息的应用。这种技术最早是在1990年代初引入的。在区域测量应用程序中利用了一些高阶统计的有趣特性。由一组移动子阵列(装备有一个或多个麦克风,GPS接收器和磁力计的头戴式头盔(HE)的士兵)组成的大型全局阵列可以将威胁定位在调查区域附近。在研究大型全局阵列时,使用二阶统计量高分辨率的定位技术,本地信息不足以影响声源位置的估计(例如,HE的方向没有影响)关于源位置的估计)。我们希望使用高阶统计信息及其属性(尤其是虚拟等效网络(VEN))将提高这些本地信息的重要性,从而可以更好地估计源位置。最初的研究是基于模拟的,然后介绍了一些在2003年10月收集的带有真实数据的结果。

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