Particle-Filter-Enabled Real-Time Sensor Fault Detection Without a Model of Faults
Matthew A. Wright, Roberto Horowitz

TL;DR
This paper introduces a particle filter-based method for real-time sensor fault detection that does not require prior knowledge of fault models, effectively identifying faulty measurements and enhancing state estimation accuracy.
Contribution
The authors propose a novel fault detection approach leveraging particle filters that operates without any predefined fault models, improving robustness in sensor validation.
Findings
Correctly identifies nearly 90% of faulty measurements
Achieves only a 3% increase in estimation error compared to perfect detection
Demonstrates effectiveness in nonlinear vehicle traffic models
Abstract
We are experiencing an explosion in the amount of sensors measuring our activities and the world around us. These sensors are spread throughout the built environment and can help us perform state estimation and control of related systems, but they are often built and/or maintained by third parties or system users. As a result, by outsourcing system measurement to third parties, the controller must accept their measurements without being able to directly verify the sensors' correct operation. Instead, detection and rejection of measurements from faulty sensors must be done with the raw data only. Towards this goal, we present a method of detecting possibly faulty behavior of sensors. The method does not require that the control designer have any model of faulty sensor behavior. As we discuss, it turns out that the widely-used particle filter state estimation algorithm provides the…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
