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首页> 外文期刊>The Journal of the Acoustical Society of America >Particle velocity estimation based on a two-microphone array and Kalman filter
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Particle velocity estimation based on a two-microphone array and Kalman filter

机译:基于两麦克风阵列和卡尔曼滤波器的粒子速度估计

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摘要

A traditional method to measure particle velocity is based on the finite difference (FD) approximation of pressure gradient by using a pair of well matched pressure microphones. This approach is known to be sensitive to sensor noise and mismatch. Recently, a double hot-wire sensor termed Microflown became available in light of micro-electro-mechanical system technology. This sensor eliminates the robustness issue of the conventional FD-based methods. In this paper, an alternative two-microphone approach termed the u-sensor is developed from the perspective of robust adaptive filtering. With two ordinary microphones, the proposed u-sensor does not require novel fabrication technology. In the method, plane wave and spherical wave models are employed in the formulation of a Kalman filter with process and measurement noise taken into account. Both numerical and experimental investigations were undertaken to validate the proposed u-sensor technique. The results have shown that the proposed approach attained better performance than the FD method, and comparable performance to a Microflown sensor.
机译:一种传统的测量粒子速度的方法是通过使用一对匹配良好的压力传声器,基于压力梯度的有限差分(FD)近似值。已知这种方法对传感器噪声和失配敏感。最近,根据微机电系统技术,一种名为Microflown的双热线传感器变得可用。该传感器消除了传统基于FD的方法的鲁棒性问题。在本文中,从鲁棒性自适应滤波的角度出发,开发了另一种称为u传感器的两麦克风方法。使用两个普通的麦克风,建议的u传感器不需要新颖的制造技术。在该方法中,考虑了过程和测量噪声,在设计卡尔曼滤波器时采用了平面波和球面波模型。进行了数值和实验研究,以验证所提出的u传感器技术。结果表明,所提出的方法比FD方法具有更好的性能,并且与Microflown传感器具有可比的性能。

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