Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Integrating Inverse and Forward Modeling for Sparse Temporal Data from Sensor Networks

Julian Vexler, Björn Vieten, Martin Nelke, Stefan Kramer

TL;DR

This paper tackles the challenge of extracting meaningful events from sparse, noisy sensor data by uniting inverse modeling (classifying objects and estimating motion from observations) with forward modeling (generating synthetic data via a knowledge-infused simulator). The CavePerception framework constructs a hypotheses space from both approaches and employs a time-invariant, geometry-based matching procedure to identify the most plausible explanations for observed sensor events. Through real-world validation with magnetometer networks at Frankfurt Airport, the method demonstrates improved classification and motion estimation over standard ML baselines, particularly under data sparsity. The work advances practical sensor-network analytics by enhancing interpretability and robustness, and it outlines a path toward real-time, magnetometer-based traffic surveillance on airport premises.

Abstract

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.

Integrating Inverse and Forward Modeling for Sparse Temporal Data from Sensor Networks

TL;DR

This paper tackles the challenge of extracting meaningful events from sparse, noisy sensor data by uniting inverse modeling (classifying objects and estimating motion from observations) with forward modeling (generating synthetic data via a knowledge-infused simulator). The CavePerception framework constructs a hypotheses space from both approaches and employs a time-invariant, geometry-based matching procedure to identify the most plausible explanations for observed sensor events. Through real-world validation with magnetometer networks at Frankfurt Airport, the method demonstrates improved classification and motion estimation over standard ML baselines, particularly under data sparsity. The work advances practical sensor-network analytics by enhancing interpretability and robustness, and it outlines a path toward real-time, magnetometer-based traffic surveillance on airport premises.

Abstract

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.

Paper Structure

This paper contains 14 sections, 7 equations, 3 figures, 1 table, 2 algorithms.

Table of Contents

  1. Introduction
  2. Related Work
  3. Simulation-Assisted Forward Modeling for Sparse Temporal Sensor Data
  4. Inverse Modeling
  5. Forward Modeling
  6. Inverse and Forward Modeling Matching
  7. Hypotheses Matching
  8. Time-Invariant Hypotheses Reduction
  9. Experimental Results
  10. Group Rank Evaluation
  11. Model Comparison
  12. Conclusion
  13. Acknowledgments.
  14. Disclosure of Interests.

Figures (3)

  • Figure 1: Left: An aircraft leaving a parking area, while trespassing the perpendicular bus line. Right: Sensor field set-up with one bus line along the yellow center guiding line (68 sensors) and one bus line perpendicular to it (53 sensors).
  • Figure 2: An example of a distance matrix $M$. The rows and columns specify temporal distances to the event start, whereas the entries represent the pairwise distance values.
  • Figure 3: Group rank assignment of the true aircraft class. Group rank 1 corresponds to the predicted group of aircraft types or categories with the smallest dissimilarity. Top-Left: ARC. Top-Right: Cat. 17. Bottom: Aircraft type.