通过图约束流匹配实现最优传感器放置
Optimal Sensor Placement via Graph-constrained Flow Matching
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中文总结 AI 辅助
研究图信号处理中最优传感器放置问题，提出将其转化为连续空间生成建模问题，利用离线训练样本让流匹配模型学习分布以直接生成连续传感器坐标，开发相关框架，实验验证了该框架在传感器放置上的有效性。
中文摘要 AI 辅助
最优传感器放置是图信号处理（GSP）中的一个基本问题，即部署有限数量的传感器来重建连续信号场。现有GSP方法依赖离散图上的组合优化，计算成本高且传感器位置限于图顶点。我们将传感器放置重新表述为连续空间生成建模问题。离线GSP优化例程先生成最优传感器配置的训练样本，流匹配（FM）模型从中学习分布。推理时，学习到的速度场直接生成连续传感器坐标，消除在线组合优化。还开发了用于固定锚定传感器部署的置换不变条件生成框架。在实际无线电传播模拟器上的实验证明了所提框架对传感器放置的有效性。
英文摘要
Optimal sensor placement is a fundamental problem in graph signal processing (GSP), where a limited number of sensors are deployed to reconstruct a continuous signal field. Existing GSP methods rely on combinatorial optimization over discretized graphs, resulting in high computational cost and sensor locations restricted to graph vertices. We reformulate sensor placement as a continuous-space generative modeling problem. An offline GSP optimization routine first generates training samples of optimal sensor configurations, from which a flow matching (FM) model learns their distribution. At inference, the learned velocity field directly generates continuous sensor coordinates, eliminating online combinatorial optimization. We further develop a permutation-invariant conditional generation framework for deployment with fixed anchor sensors. Experiments on a realistic radio propagation simulator demonstrate the effectiveness of the proposed framework for sensor placement.
