Detection and recognition of R/F devices based on their unintended electromagnetic emissions using stochastic and computational intelligence methods.

摘要

Radio Frequency (RF) devices produce some amount of Unintended Electromagnetic Emissions (UEEs). UEEs are generally unique to a device and can be thought of as a signature of the device. This property of uniqueness of UEEs can be used to detect and identify the device producing the emission. The problem with UEEs is that they are very low in power and are often buried deep inside the noise band which makes them difficult to detect. There are two types of UEE detection methods. The first one is called stimulated detection method where the UEEs of a device are enhanced using external stimulation signal and the detection is made based on the analysis of the enhanced stimulated signal. This method, however, is resource intensive as the generation, transmission, and reception of the stimulation signal requires hardware components. The second UEE detection method is called passive detection method where the UEE signals are not tampered with and are analyzed in its original raw form. Since the UEEs are weak in strength, the challenge with passive detection method is to measure and analyze UEEs in a noisy environment.;In order to detect and recognize RF devices through the UEE, the first step is to measure the leakage of electric signal that is emitted outside of the RF devices as UEEs. UEE samples are collected from two RF devices at three different distances of 3 feet, 6 feet and 10 feet and also for noise in a similar environment. The three methods explored in this research are Principal Components Analysis (PCA), Hidden Markov Model (HMM), and Support Vector Machine (SVM). This research studies the performance of these three algorithms for passive detection of UEEs and compares it with the performance of Neural Network (NN). The explored methods gives significant better results than existing methods and can be used as an alternative for the costly and resource intensive stimulated detection methods. One of the major application of UEE is in the detection of Improvised Explosive Devices (IEDs). Effective IED detection system for military operation should accurately perform the task of detection, localization, and direction of malicious devices. This research contributes to the detection and recognition of IED detection system by proposing models based on stochastic and computational intelligence methods. These methods proved to have promise if it can be implemented in real life with more applied research.