Multi-source contamination mapping on the ground: A novel approach

Abstract

Multi-source contamination mapping is a critical aspect of radiation detection and environmental monitoring. This paper introduces an innovative algorithm for accurate and efficient multi-source contamination mapping. The algorithm comprises two main components: (1) search area identification and (2) source localization using a particle filter. The search area identification involves extrapolating directional measurements to create search areas, employing clustering algorithms to refine these areas, and generating final search areas based on weighted factors. The incorporation of directional measurements significantly enhances performance by reducing parameter search space of the particle filter and thus improving practical application potential. The particle filter-based source localization employs a four-stage process, involving generation of initial guesses, comparison of expected and acquired measurements, employing genetic algorithms for particle selection, and a convergence criterion. The algorithm’s efficacy is demonstrated through four simulated experimental scenarios, each with varying numbers and densities of point radioactive sources. Results illustrate successful search area identification and source localization with an average error of approximately 1 meter and source activity estimation of within 25% of the ground-truth values. Further improvements to the area clustering algorithm, improved mission planning for online applications, and extensive testing of the algorithm are planned in the future. With these improvements, the algorithm presented here could enhance the ability to respond to radioactive contamination incidents swiftly and effectively, ensure timely mapping of contaminated areas, and maintain human safety in the event of an incident.

Keywords

• particle filter
• clustering
• mapping
• contamination