Developing a novel network of CBRNe sensors in response to existing capability gaps in current technologies

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.