用自然语言提升物理系统状态估计精度
Language-Aided State Estimation
- 将人类语言观测转为结构化信息融入粒子滤波
- 在灌溉渠水位估计中实现更准确的状态预测
- 适合需要人机协同感知的智能系统场景
社交媒体和聊天平台上的自然语言数据（如文本、语音）日益丰富。本文利用人类通过自然语言表达的观察，解决物理系统状态估计问题，其中人类充当感知代理。为此，提出一种语言辅助粒子滤波器（LAPF），该框架通过自然语言处理技术对人类观测进行结构化，并将其融入状态估计的更新步骤。最终，将LAPF应用于灌溉渠水位估计问题，验证了其有效性。
原文摘要 · Abstract (English)
Natural language data, such as text and speech, have become readily available through social networking services and chat platforms. By leveraging human observations expressed in natural language, this paper addresses the problem of state estimation for physical systems, in which humans act as sensing agents. To this end, we propose a Language-Aided Particle Filter (LAPF), a particle filter framework that structures human observations via natural language processing and incorporates them into the update step of the state estimation. Finally, the LAPF is applied to the water level estimation problem in an irrigation canal and its effectiveness is demonstrated.
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