Development, Optimization, and Validation of Unintended Radiated Emissions Processing System for Threat Identification

There is an on-going need for faster, more accurate, and easier to implement threat identification systems for concealed electronics, to thwart terrorism and espionage attempts. Common electronics are used in the design of improvised explosive devices (IEDs), targeting military, and civilian populations, while concealed recording devices steal proprietary/confidential data, compromising both government and industrial resources. This paper discusses a novel, nonintrusive, repeatable, reliable, expandable, and simple-to-implement detection and identification (D&I) system for identifying threats by using unintended radiated emissions (URE). This paper differs from current URE proposals, by avoiding energy radiation. The proposed process also addresses big data problems involved in capturing and building databases of URE characteristics. Issue interpretation is utilized with URE data to distinguish between threat/nonthreat devices using multiple criteria decision analysis and other decision-making techniques to determine model and mode of hidden devices. System optimization, verified by testing, is used to improve the speed and accuracy of the identification algorithm. The system is developed with data from 130 devices and validated with 33 separate devices, which are representative of IED and espionage threats. Due to slow progress in the URE D&I field, the properties and potential of a more effective D&I system, compared to current methods, will be of interest to the explosive ordnance disposal, security services, electronic systems manufacturing, automated inventory, mobile application development communities, and potentially others as well. For a field that is not well documented, this paper provides a clear, concise, and detailed model for future research and applications of URE D&I.