This paper proposes an efficient algorithm for robust sensor placement with the purpose of recovering a source signal from noisy measurements. To model uncertainty on the spatially-variant sensors gain and on the spatially correlated noise, we assume that both are realizations of Gaussian processes. Since the signal to noise ratio (SNR) is also uncertain in this context, to achieve a robust signal extraction, we propose a new placement criterion based on the maximization of the probability that the SNR exceeds a given threshold. This criterion can be easily evaluated using the Gaussian process assumption. Moreover, to reduce the computational complexity of the joint maximization of the criterion with respect to all sensor positions, we suggest a sequential maximization approach, where the sensor positions are chosen one at a time. Finally, we present numerical results showing the superior robustness of the proposed approach when compared to standard sensor placement criteria aimed at interpolating the spatial gain and to a recently proposed criterion aimed at maximizing the average SNR.

中文翻译：

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用于信号提取的稳健传感器放置


本文提出了一种用于鲁棒传感器放置的有效算法，其目的是从噪声测量中恢复源信号。为了对空间变化的传感器增益和空间相关噪声的不确定性进行建模，我们假设两者都是高斯过程的实现。由于信噪比 (SNR) 在这种情况下也是不确定的，为了实现鲁棒的信号提取，我们提出了一种基于 SNR 超过给定阈值的概率最大化的新放置标准。使用高斯过程假设可以轻松评估该标准。此外，为了降低针对所有传感器位置的标准联合最大化的计算复杂性，我们建议采用一种顺序最大化方法，其中一次选择一个传感器位置。最后，我们提出的数值结果表明，与旨在插值空间增益的标准传感器放置标准和最近提出的旨在最大化平均信噪比的标准相比，所提出的方法具有卓越的鲁棒性。