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A Novel Self-Calibration Method for Acoustic Vector Sensor

机译:一种新型的矢量传感器自校准方法

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The acoustic vector sensor (AVS) can measure the acoustic pressure field’s spatial gradient, so it has directionality. But its channels may have nonideal gain/phase responses, which will severely degrade its performance in finding source direction. To solve this problem, in this study, a self-calibration algorithm based on all-phase FFT spectrum analysis is proposed. This method is “self-calibrated” because prior knowledge of the training signal’s arrival angle is not required. By measuring signals from different directions, the initial phase can be achieved by taking the all-phase FFT transform to each channel. We use the amplitude of the main spectrum peak of every channel in different direction to formulate an equation; the amplitude gain estimates can be achieved by solving this equation. In order to get better estimation accuracy, bearing difference of different training signals should be larger than a threshold, which is related to SNR. Finally, the reference signal’s direction of arrival can be estimated. This method is easy to implement and has advantage in accuracy and antinoise. The efficacy of this proposed scheme is verified with simulation results.
机译:声矢量传感器(AVS)可以测量声压场的空间梯度,因此具有方向性。但是其通道可能具有非理想的增益/相位响应,这将严重降低其在寻找源方向时的性能。为了解决这个问题,本文提出了一种基于全相位FFT频谱分析的自校准算法。这种方法是“自校准的”,因为不需要事先知道训练信号的到达角度。通过测量来自不同方向的信号,可以通过对每个通道进行全相位FFT转换来实现初始相位。我们使用每个通道在不同方向上的主频谱峰值的幅度来公式化;振幅增益估计可以通过求解该方程来实现。为了获得更好的估计精度,不同训练信号的方位差应大于阈值,这与信噪比有关。最后,可以估计参考信号的到达方向。该方法易于实现,并且在准确性和抗噪声方面具有优势。仿真结果验证了该方案的有效性。

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