发表机构
ONERA; Université Paris-Saclay; Mines Paris; PSL University（法国航空航天研究院; 巴黎萨克雷大学; 巴黎矿业学院; 巴黎文理研究大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出用图神经网络编码器将异质采样的组织化信号集合嵌入固定大小向量空间，实现采样不变表示，并通过合成复值射频信号实验验证了波形分离判别能力。
AI 中文摘要
传感器网络和雷达可以将信号作为组织化集合来传递，例如有序信号、描述网格内距离单元的信号或被视为图节点的信号。在此类集合中，单个信号可能具有不同的采样参数。本文研究组织化信号集合的神经网络编码器。在本工作的背景下，此类编码器的目的是将异质采样的信号集合投影到任意固定大小的向量空间中。该新表示空间的设计使得信号集合能够作为去除采样差异的向量进行处理，从而允许在无信号处理约束的情况下进行任意拓扑感知处理。在此旨在减少异质采样参数影响的表示空间中，通过考虑信号集合的判别潜力（重点关注波形分离）来评估信号集合表示的相关性。所进行的编码和嵌入判别实验完全依赖于合成的复值射频信号。
英文摘要
Sensor networks and radars can deliver signals as organized sets, e.g. ordered signals, signals describing range cells within a grid or signals perceived as graph nodes. Within such sets, individual signals may be characterized by distinct sampling parameters. This paper investigates organized signal sets neural network encoders. In the context of this work, the purpose of such encoders is to project heterogeneously sampled signal sets into an arbitrary fixed-size vectors space. This new representation space is designed so that signal sets can be processed as vectors rid of sampling differences to allow for arbitrary topology-aware processing with no signal processing constraints. Within this representation space designed to reduce the influence of heterogeneous sampling parameters, the relevance of signal sets representations is evaluated by considering signal sets discrimination potential with a focus on waveforms separation. The encoding and embeddings discrimination experiments conducted rely exclusively on synthetic complex-valued radiofrequency signals.
