AI 中文总结
本文提出利用三维数字孪生生成合成信道冲激响应数据，训练序列神经网络实现设备无关定位，并主张环境条件化的局部模型，实验验证了其潜力。
AI 中文摘要
我们提出了一种方法论，将环境的三维数字孪生（DT）作为大规模无线电感知发展的主要推动力。数字孪生充当世界模型，向射线追踪引擎提供几何、材料以及发射器/接收器位置，该引擎为大量合理场景（移动的人和物体、布局变体、季节/天气条件等）生成带时间索引的信道冲激响应（CIR）。从这些合成序列中，我们训练一个序列神经网络，将CIR时间序列映射到空间占用估计，从而实现无需仪器化目标的设备无关定位（DFL）。我们认为，感知最好被视为一个环境条件化的学习问题：与其寻求单一的全局模型，我们主张训练或微调针对特定场地数字孪生专门化的局部模型。作为首个实验，我们引入了一种新颖的状态空间模型架构，并在多种房间几何形状上进行训练和评估。所获得的定位性能证明了该方法的潜力。
英文摘要
We present a methodology that places a 3D digital twin (DT) of the environment as the main enabler behind the development of radio sensing at scale. The DT acts as a world model, providing geometry, materials, and transmitter/receiver placements to a ray-tracing engine that generates time-indexed channel impulse responses (CIRs) for large numbers of plausible scenes (moving people and objects, layout variants, seasonal/weather conditions, etc). From these synthetic sequences, we train a sequential neural network that maps CIR time series to spatial occupancy estimates, enabling device-free localization (DFL) without instrumented targets. We posit that sensing is best approached as an environment-conditioned learning problem: rather than seeking a single global model, we advocate training or fine-tuning local models specialized to a site-specific DT. As a first experiment, we introduce a novel State Space Model architecture, trained and evaluated across multiple room geometries. The localization performances obtained demonstrate the potential of the approach.
CommentsAccepted at EUSIPCO 2026
