Improved detection of chemical threats by sensor data fusion

• Autorzy: Kopp Norbert Ludwig , Koch-Eschweiler Helge

Identyfikatory

DOI

10.35467/sdq/144296

• Języki publikacji: EN

Abstrakty

EN

This paper presents some aspects of sensor data fusion that were derived from the EU-SENSE project of the European Commission (Horizon 2020, Grant Agreement No 787031). The aim of EU-SENSE was to develop a novel network of sensors for CBRNe applications through the exploitation of chemical detector technologies, advanced machine-learning and modelling algorithms. The high-level objectives of the project include improving the detection capabilities of the novel network of chemical sensors through the use of machine learning algorithms and reducing the impact of environmental noise. The focus in this paper is on the detection and data fusion aspects as well as the machine learning approaches that were used as part of the project. Detection (in the sense of detectto-warn) is a classification task and improvement of detection requires enhancing the discriminatory power of the classifier, that is reducing false alarms, false positives, and false negatives. This was achieved by a two-step procedure, that is a sensitive distance-based anomaly/change detection followed by downstream classification, identification and concentration estimation. Bayesian networks proved to be useful when fusing information from multiple sensors. For validation purposes, experimental data was gathered during the project and the developed approaches were applied successfully. Despite the development of several new, helpful tools within the project, the domain of chemical detection remains challenging, particularly regarding provisioning of the necessary prior-knowledge. It might make sense from a coverage point of view to look into integration of stand-off detection techniques into a sensor network, including data fusion too.

Słowa kluczowe

EN

Bayesian inference; sensor data fusion; anomaly detection; chemical detection

• Wydawca: Akademia Sztuki Wojennej
• Czasopismo: Security and Defence Quarterly
• Rocznik: 2022
• Tom: Vol. 37, No. 1
• Strony: 70--93
• Opis fizyczny: Bibliogr. 7 poz., rys., tab.

Twórcy

autor

Kopp Norbert Ludwig

• System Analysis, technisch-mathematische studiengesellschaft mbH, Germany

• https://orcid.org/0000-0001-8226-7240

autor

Koch-Eschweiler Helge

• System Analysis, technisch-mathematische studiengesellschaft mbH, Germany

Bibliografia

• 1. Jensen, F.V. and Nielsen, T.D. (2007) Bayesian networks and decision graphs. New York, NY: Springer.
• 2. Mehrotra, K.G., Mohan, C.K. and Huang, H. (2017) Anomaly detection principles and algorithms. New York, NY: Springer.
• 3. Mitchell, H. (2007) Multi-sensor data fusion: An introduction. New York, NY: Springer.
• 4. Ó Ruanaidh, J.J. and Fitzgerald, W. (1996) Numerical Baesian methods applied to signal processing. New York, NY: Springer.
• 5. Robins, P. and Paul, T. (2005a) ‘Non-linear Bayesian CBRN source term estimation’, in 7th international conference on information fusion. Philadelphia, PA: IEEE, p. 8. doi: 10.1109/ICIF.2005.1591980.
• 6. Robins, P., Rapley, V. and Thomas, P. (2005b) ‘A probabilistic chemical sensor model for data fusion’, in 7th International conference on information fusion. Philadelphia, PA: IEEE, p. 7. doi: 10.1109/ICIF.2005.1591982.
• 7. Sferopoulos, R. (2009) A review of chemical warfare agent (CWA) detector technologies and commercial-off-the-shelf items. Victoria, Australia: Human Protection and Performance Division, Defence Science and Technology Organisation (DSTO).