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.

翻译：通过社交媒体和聊天平台，文本和语音等自然语言数据已变得易于获取。本文利用以自然语言表达的人类观测信息，解决了物理系统的状态估计问题，其中人类充当感知代理。为此，我们提出了一种语言辅助粒子滤波器（LAPF），该粒子滤波框架通过自然语言处理结构化人类观测，并将其纳入状态估计的更新步骤。最后，将LAPF应用于灌溉渠道的水位估计问题，并验证了其有效性。