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A Linear Closed-Form Algorithm for Source Localization From Time-Differences of Arrival

机译:基于到达时间差的线性闭合形式的源定位算法

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摘要

Microphone arrays often operate in the near field, which complicates the problem of determining a source location from time-difference-of-arrival (TDOA) measurements typically derived from generalized cross-correlation functions. Each TDOA satisfies the equation of a hyperboloid in space and methods have been developed to either solve for intersecting hyperboloids or make some approximation to them, keeping source-location determination a nonlinear, somewhat complex problem. We introduce a closed-form, analytic solution for the problem (the GS algorithm). It is so simple that we were surprised that, until very recently, there have been no other solutions similar to ours. The method uses a minimum of five microphones in three dimensions, one more than other solutions, but, for nonsingular layouts of the microphones, it is very fast and accurate. First, the new method is compared to other closed-form methods for accuracy and sensitivity to noise using simulated data. Then, several variants of GS are compared to two other real-time algorithms, LEMSalg and SRP-PHAT, using real, human-talker data from a large array in a noisy room.
机译:麦克风阵列通常在近场中操作,这使根据通常从广义互相关函数得出的到达时间差(TDOA)测量确定源位置的问题变得复杂。每个TDOA都满足空间中的双曲面方程,并且已经开发出方法来解决相交的双曲面或对其进行近似计算,从而将源位置确定保持为非线性的,有些复杂的问题。我们针对该问题引入了一种封闭形式的解析解(GS算法)。如此简单,我们感到惊讶,直到最近,还没有其他类似的解决方案。该方法在三个维度上至少使用五个麦克风,比其他解决方案多一个,但是,对于麦克风的非单一布局,它非常快速且准确。首先,使用模拟数据将新方法与其他闭式方法进行比较,以提高准确性和对噪声的敏感性。然后,在嘈杂的房间中,使用来自大型阵列的真实的人类讲话者数据,将GS的几种变体与其他两种实时算法LEMSalg和SRP-PHAT进行比较。

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