Stack Type Detection Using Few-Shot Learning

Henry Lin, K. George
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Abstract

Wireless digital communication has become so saturated that it is harder for radar receivers to distinguish noise from desired signals, essential for tracking applications like air traffic control towers, defense systems, and communication towers. This is where signal detection is a vital capability of radar systems. Signal detection is the ability to detect signals from noise, and often these signals will be interleaved with noise and other signals. Noise can be alleviated by using filtering techniques, windowing, and transforms, which then can be used by a deinterleaving algorithm to isolate signals. Standard deinterleaving methods isolate signals using deterministic methods; however, more state-of-the-art methods may approach these problems using machine learning or artificial intelligence. Often these methods require copious amounts of data, which can vary from a few hundred to thousands. This might not always be possible in certain situations where privacy limits the amount of available data. This is where Few-Shot Learning (FSL) is utilized for training models on small datasets. This paper proposes a system that can generate interleaved signals and deinterleave them with the help of an FSL model. Various FSL models will be used to compare and determine the optimal configuration of the proposed system.
使用Few-Shot学习的堆栈类型检测
无线数字通信已经变得如此饱和,以至于雷达接收器很难区分噪声和期望的信号,这对于跟踪应用程序(如空中交通管制塔、防御系统和通信塔)至关重要。这就是信号检测是雷达系统至关重要的能力。信号检测是从噪声中检测信号的能力,而这些信号往往会与噪声和其他信号交织在一起。噪声可以通过使用滤波技术、加窗和变换来减轻,然后可以通过去交错算法来隔离信号。标准去交错方法使用确定性方法隔离信号;然而,更先进的方法可能会使用机器学习或人工智能来解决这些问题。这些方法通常需要大量的数据,从几百到几千不等。在隐私限制可用数据量的某些情况下,这可能并不总是可行的。这就是利用Few-Shot Learning (FSL)在小数据集上训练模型的地方。本文提出了一种利用FSL模型产生交错信号并去交错信号的系统。将使用各种FSL模型来比较和确定拟议系统的最佳配置。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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