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State-of-the-art CBRNe detection systems are predominantly available as standalone detectors, rarely offering the potential of networking and data fusion. This paper presents a novel CBRNe detection and identification system based on the network of heterogeneous sensor nodes. The system uses a novel data fusion algorithm combining data from the sensors, advanced machine-learning and modelling algorithms to significantly reduce false alarm rates. The situational awareness tools and training compounds supplement the system to provide innovative real capabilities for CBRNe practitioners.
Szklarski et al. (Fri,) studied this question.