Bosch Ignacio, Vergara Luis
Departamento de Comunicaciones, ETSI Telecomunicación, Universidad Politécnica de Valencia, C/Camino de Vera s/n, 46022 Valencia, Spain.
Ultrasonics. 2008 Mar;48(1):56-65. doi: 10.1016/j.ultras.2007.09.003. Epub 2007 Nov 1.
We consider in this paper the problem of automatic detection of ultrasonic echo pulses in a grain noise background. We start by assuming a reference model for grain noise: multivariate correlated Gaussian model having, in general, different variances under every hypothesis. We show that, even for this simple model, there is not practical optimum solution, except if the variances are equal under every hypothesis and the echo pulse satisfies a spectral constraint. Then we consider split-spectrum (SS) suboptimum solutions. Firstly, SS algorithms are formulated following an algebraic approach which is appropriate in an automatic detection framework. Popular minimization and polarity thresholding algorithms are considered under this framework. Then a new detector called normalized SS (NSS) is proposed. The underlying idea is to actually exploit the tuning frequency sensitivity (i.e., variability of the output magnitudes from one SS channel to another), making this measurement independent of the absolute magnitudes. Different experiments with simulated and real data show evidences of the interest of the new method in an automatic detection framework. Derivations of the formulas for fitting the probability of false alarm in every detector are included in the paper.
在本文中，我们考虑了在颗粒噪声背景下自动检测超声回波脉冲的问题。我们首先假设颗粒噪声的一个参考模型：多元相关高斯模型，通常在每个假设下具有不同的方差。我们表明，即使对于这个简单的模型，也不存在实际的最优解，除非在每个假设下方差相等且回波脉冲满足频谱约束。然后我们考虑分裂谱（SS）次优解。首先，按照一种适用于自动检测框架的代数方法来制定SS算法。在这个框架下考虑了流行的最小化和极性阈值算法。然后提出了一种名为归一化SS（NSS）的新检测器。其基本思想是实际利用调谐频率灵敏度（即从一个SS通道到另一个通道输出幅度的变化），使该测量与绝对幅度无关。对模拟数据和真实数据进行的不同实验表明了新方法在自动检测框架中的优势。本文还包括了用于拟合每个检测器中误报概率的公式推导。
