Improved detection of chemical threats by sensor data fusion

Journal Title: Security and Defence Quarterly - Year 2022, Vol 37, Issue 1

Abstract

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.

Authors and Affiliations

Norbert Kopp, Helge Koch-Eschweiler

Keywords

Related Articles

Arctic – the region of dissonant international interests

The enduring global warming has opened new views on the exploitation of the Artic. The opportunity to open new shipping routes, huge resource reserves, and fishery are of interest to many entities; however experts's opin...

Illegal immigration as a threat to the safety of the Republic of Poland

Uncontrolled migration is one of the threats to the state, and as illegal immigration, it is a leading threat to international security. From the point of view of state security, illegal immigration is classified as “req...

The Influence of strategic culture on shaping security policy

The aim of the article is to present the role that strategic culture plays in creating and shaping security of the future. Taking account of the purpose of this paper, the main research problem took the form of the follo...

Values and norms of behaviour in the life of cadets

The aim of this paper is to analyse the meaning, role and place of values in the life of a soldier. Furthermore, the article explores the issues of soldiers’ ethical standards and types of behaviour which can be found in...

Social structure determinants of Polish military manpower

This paper sets out to identify the key elements of Polish social structure that are critical to the manpower of the Polish Armed Forces. So far, there has been little discussion, in either the literature or ongoing publ...

Download PDF file
  • EP ID EP705787
  • DOI https://doi.org/10.35467/sdq/144296
  • Views 138
  • Downloads 0

How To Cite

Norbert Kopp, Helge Koch-Eschweiler (2022). Improved detection of chemical threats by sensor data fusion. Security and Defence Quarterly, 37(1), -. https://europub.co.uk/articles/-A-705787