融合正向与反向建模，提升稀疏传感器数据的解释能力
Integrating Inverse and Forward Modeling for Sparse Temporal Data from Sensor Networks
- 在假设空间中结合机器学习与物理模型，构建可解释的数据分析框架
- 在真实机场磁力计数据上验证，成功识别并预测飞机运动轨迹
- 适合需要理解复杂传感事件的智能系统开发者使用
我们提出CavePerception框架，用于分析来自传感器网络的稀疏数据，融合了反向建模与正向建模思想。通过在假设空间中结合机器学习与物理模型，旨在提升对稀疏、噪声大且可能不完整的传感器数据的可解释性。该框架假设二维传感器网络以图结构布局，用于检测特定物体及其运动模式，例如磁力计。已知物体特性及其对传感器的影响，可构建数据生成器，模拟物体在传感器场中的运动产生数据。利用这些仿真数据，框架推断物体在传感器网络中的行为。该方法在真实机场磁力计数据上进行了实验验证，结果表明融合正向与反向建模具有显著价值，使智能系统能更准确地理解和预测复杂的传感器驱动事件。
原文摘要 · Abstract (English)
We present CavePerception, a framework for the analysis of sparse data from sensor networks that incorporates elements of inverse modeling and forward modeling. By integrating machine learning with physical modeling in a hypotheses space, we aim to improve the interpretability of sparse, noisy, and potentially incomplete sensor data. The framework assumes data from a two-dimensional sensor network laid out in a graph structure that detects certain objects, with certain motion patterns. Examples of such sensors are magnetometers. Given knowledge about the objects and the way they act on the sensors, one can develop a data generator that produces data from simulated motions of the objects across the sensor field. The framework uses the simulated data to infer object behaviors across the sensor network. The approach is experimentally tested on real-world data, where magnetometers are used on an airport to detect and identify aircraft motions. Experiments demonstrate the value of integrating inverse and forward modeling, enabling intelligent systems to better understand and predict complex, sensor-driven events.
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