Real-time, Adaptive Radiological Anomaly Detection and Isotope Identification Using Non-negative Matrix Factorization

TL;DR

This paper introduces a real-time adaptive NMF algorithm for radiological anomaly detection and isotope identification that updates its background model to handle environmental changes, improving robustness and performance.

Contribution

The authors developed a novel, periodically updating NMF-based algorithm that adapts to environmental changes, enhancing detection accuracy over traditional static models.

Findings

Outperforms existing NMF methods on simulated datasets.

Maintains high detection sensitivity in real-world scenarios.

Reduces false alarms by adapting to environmental variability.

Abstract

Spectroscopic anomaly detection and isotope identification algorithms are integral components in nuclear nonproliferation applications such as search operations. The task is especially challenging in the case of mobile detector systems due to the fact that the observed gamma-ray background changes more than for a static detector system, and a pretrained background model can easily find itself out of domain. The result is that algorithms may exceed their intended false alarm rate, or sacrifice detection sensitivity in order to maintain the desired false alarm rate. Non-negative matrix factorization (NMF) has been shown to be a powerful tool for spectral anomaly detection and identification, but, like many similar algorithms that rely on data-driven background models, in its conventional implementation it is unable to update in real time to account for environmental changes that affect the background spectroscopic signature. We have developed a novel NMF-based algorithm that periodically updates its background model to accommodate changing environmental conditions. The Adaptive NMF algorithm involves fewer assumptions about its environment, making it more generalizable than existing NMF-based methods while maintaining or exceeding detection performance on simulated and real-world datasets.