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.

本研究针对传感器网络中稀疏、噪声及可能不完整的数据分析问题，提出了CavePerception框架。通过将机器学习与物理建模相结合，该框架在假设空间内提高了数据的可解释性。实验证明，该方法能够改善对复杂传感器驱动事件的理解和预测。

结合逆向建模和正向建模以分析传感器网络中的稀疏时间数据