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Sensor Compromise Detection in Multiple-Target Tracking Systems.

Juan-Pablo Ramirez-Paredes1, Emily A Doucette2, Jess W Curtis3

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This study introduces a new algorithm to detect compromised sensors in multiple-target tracking systems. The method enhances tracking precision and resource efficiency against adversarial attacks.

Keywords:
cyberphysical systemsestimationsensor networks

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Area of Science:

  • Engineering
  • Computer Science
  • Signal Processing

Background:

  • Multiple-target tracking systems are vulnerable to sensor compromise by adversaries.
  • Adversarial attacks can degrade tracking precision and introduce false targets, wasting resources.

Purpose of the Study:

  • To develop a robust algorithm for detecting compromised or faulty sensors in multiple-target tracking.
  • To address sensor security challenges in both single-sensor and multiple-sensor scenarios.

Main Methods:

  • Utilized finite set statistics (FISST) for sensor data analysis.
  • Applied one-class classifiers for anomaly detection.
  • Employed hypothesis testing with nonparametric techniques for attack identification.

Main Results:

  • The proposed algorithm effectively detects a variety of sensor attacks in multiple-sensor configurations.
  • Demonstrated improved resilience against information loss and precision degradation.

Conclusions:

  • The developed algorithm offers a significant advancement in securing multiple-target tracking systems.
  • Provides a framework for enhancing the reliability and trustworthiness of sensor networks.