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Wideband source localization using a distributed acoustic vector-sensor array

机译:使用分布式声矢量传感器阵列进行宽带源定位

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We derive fast wideband algorithms, based on measurements of the acoustic intensity, for determining the bearings of a target using an acoustic vector sensor (AVS) situated in free space or on a reflecting boundary. We also obtain a lower bound on the mean-square angular error (MSAE) of such estimates. We then develop general closed-form weighted least-squares (WLS) and reweighted least-squares algorithms that compute the three-dimensional (3-D) location of a target whose bearing to a number of dispersed locations has been measured. We devise a scheme for adaptively choosing the weights for the WLS routine when measures of accuracy for the bearing estimates, such as the lower bound on the MSAE, are available. In addition, a measure of the potential estimation accuracy of a distributed system is developed based on a two-stage application of the Cramer-Rao bound. These 3-D results are quite independent of how bearing estimates are obtained. Naturally, the two parts of the paper are tied together by examining how well distributed arrays of AVSs located on the ground, seabed, and in free space can determine the 3-D position of a target The results are relevant to the localization of underwater and airborne sources using freely drifting, moored, or ground sensors. Numerical simulations illustrate the effectiveness of our estimators and the new potential performance measure.
机译:我们基于声强的测量结果得出快速宽带算法,以使用位于自由空间或反射边界上的声矢量传感器(AVS)确定目标方位。我们还获得了此类估计的均方角误差(MSAE)的下限。然后,我们开发通用的封闭式加权最小二乘(WLS)和重新加权最小二乘算法,这些算法可计算目标的三维(3-D)位置,该位置已测量了对多个分散位置的影响。我们设计了一种方案,用于在方位估计的准确性度量(例如MSAE的下限)可用时,为WLS例程自适应选择权重。另外,基于Cramer-Rao边界的两阶段应用,开发了一种分布式系统的潜在估计精度的度量。这些3-D结果与获得方位估计的方式完全无关。自然地,通过检查位于地面,海床和自由空间中的AVS阵列的分布情况如何,可以确定目标的3D位置,从而将本文的两部分结合在一起。结果与水下物体的定位有关使用自由漂移,系泊或地面传感器的空中源。数值模拟说明了我们的估计器的有效性以及新的潜在性能指标。

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