分布式多输出高斯过程，实现高效协同感知与可靠不确定性估计
Consensus-based Recursive Multi-Output Gaussian Process
- 基于共享基向量递归推断+邻居间信息共识
- 每步计算开销有界，保持输出间相关性
- 适合多智能体传感场景，支持大规模部署
多输出高斯过程能对向量场进行带不确定性的合理学习，但因其计算密集且依赖中心化架构，难以应用于大规模、分布式和流式场景。本文提出一种基于共识的递归多输出高斯过程（CRMGP）框架，结合共享基向量的递归推断与邻近节点间的信息共识更新。该方法支持并行、完全分布式的学习，每步计算开销有界，同时保留输出间的相关性与校准后的不确定性。在合成风场和真实LiDAR数据上的实验表明，CRMGP实现了具有竞争力的预测性能和可靠的不确定性校准，为多智能体感知应用提供了可扩展的集中式高斯过程替代方案。
原文摘要 · Abstract (English)
Multi-output Gaussian Processes provide principled uncertainty-aware learning of vector-valued fields but are difficult to deploy in large-scale, distributed, and streaming settings due to their computational and centralized nature. This paper proposes a Consensus-based Recursive Multi-Output Gaussian Process (CRMGP) framework that combines recursive inference on shared basis vectors with neighbour-to-neighbour information-consensus updates. The resulting method supports parallel, fully distributed learning with bounded per-step computation while preserving inter-output correlations and calibrated uncertainty. Experiments on synthetic wind fields and real LiDAR data demonstrate that CRMGP achieves competitive predictive performance and reliable uncertainty calibration, offering a scalable alternative to centralized Gaussian process models for multi-agent sensing applications.
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