Array-based sensing with the goal of signal reconstruction to support downstream processing presents challenges in many domains, including radar and sonar. Existing methods of generating filter weights are stochastically optimal in the beam power response, but do not necessarily result in a better solution for signal reconstruction. Coherent signal-to-noise ratio (SNR) correlates the actual signal to the output of the beamformer and better relates the quality of the reconstructed timeseries. However, coherent SNR requires knowledge of the waveform and so is not available for passive applications. From a large corpus of training data, machine learning approaches learn to estimate solutions improving coherent SNR using data available in situ. This article proposes an approach using machine learning to aid in array signal processing weight generation. A cost function is developed to assess the quality of a set of weights based on coherent SNR responses, and this cost function is used to train a neural network. While this approach works when the stationary assumption holds, this article also considers the case of limited duration, loud dynamic interferers. An expanded architecture trains another neural network, inspired by extreme value distributions, which is then used to select the samples most likely to come from the so-called stationary distribution. This work demonstrates that machine learning has leverage to improve on existing weight generation techniques for the signal reconstruction task.

中文翻译：

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将机器学习应用于自适应阵列信号处理权重生成


旨在信号重建以支持下游处理的基于阵列的传感在许多领域（包括雷达和声纳）提出了挑战。生成滤波器权重的现有方法在波束功率响应中是随机最优的，但不一定会产生更好的信号重建解决方案。相干信噪比 (SNR) 将实际信号与波束形成器的输出相关联，并更好地关联重建时间序列的质量。然而，相干 SNR 需要了解波形，因此不适用于无源应用。从大量的训练数据中，机器学习方法学习使用现场可用的数据来估计提高相干信噪比的解决方案。本文提出了一种使用机器学习来辅助阵列信号处理权重生成的方法。开发成本函数来评估基于相干 SNR 响应的一组权重的质量，并且该成本函数用于训练神经网络。虽然这种方法在平稳假设成立时有效，但本文还考虑了持续时间有限、动态干扰较大的情况。受极值分布的启发，扩展的架构训练另一个神经网络，然后使用该神经网络选择最有可能来自所谓的平稳分布的样本。这项工作表明，机器学习可以改进信号重建任务的现有权重生成技术。