Radio Frequency Machine Learning (RFML) techniques for the classification tasks of Specific Emitter Identification (SEI) and Automatic Modulation Classification (AMC) have seen rapid improvements in recent years. The applications of SEI, a technique used to associate a received signal to an emitter, and AMC, a technique for determining the modulation scheme present within a transmission, are necessary for a variety of defense applications such as early warning systems and emitter tracking. Existing works studying SEI and AMC have sought to perform and improve classification through the use of various different machine learning (ML) model architectures. In ideal conditions, these efforts have shown strong classification results, however, when robust real-world data is applied to these models, performance notably decreases. Further efforts, therefore, are required to understand why each of these models fails in adverse conditions. With this understanding, robust architectures that are able to maintain performance in the presence of various data conditions can be created. The work presented in this thesis seeks to improve upon SEI and AMC models by furthering the understanding of how certain model architectures fail under varying data conditions, then applies Transfer Learning (TL) and Ensemble Learning techniques in an effort to mitigate discovered failures and improve the applicability of trained models to various types of data. Each of the approaches presented in this work utilize real-world datasets, collected in a way that emulate a variety of possible real life use conditions of RFML systems. Results show that existing AMC approaches are fairly robust to varying data conditions, while SEI approaches suffer a significant degradation in performance under conditions that differ than that used to train a given model. Further, TL and ensemble techniques can be utilized to improve the robustness of RFML models. This thesis helps isolate the rate and features of those SEI degradations, hopefully setting a foundation for future improvements. / Master of Science / Radio Frequency (RF) signals are produced by many different emitters encountered on a daily basis, including phones, networks, radar, and radios. These signals are used to transfer information from an emitter to a receiver, and contain a plethora of information that need be protected for defense practices in the RF domain. On the other hand, the information contained in these signals can be intercepted and utilized to discover information about potentially malicious transmissions. Two practices to determine information about received signals include Specific Emitter Identification (SEI), which relates an emitter to a received signal, and Automatic Modulation Classification (AMC), which determines the modulation scheme in which a signal is transmitted. A signal is made up of information, expressed in bits, and a modulation scheme is the method used to map those bits to express information. In recent years, Machine Learning (ML) techniques have been applied to SEI and AMC in an effort to improve the efficiency and accuracy results of classification. These ML approaches have shown high accuracy results when applied to data that is collected in the same environment as that used for training. When applied to data with different variables, however, model accuracy notably drops. This performance decrease motivates the need to discover more variables that negatively impact model performance, and further to create models that do not suffer from the same weaknesses. This work examines four different real-world variables that are common in deployed radio frequency machine learning (RFML) usage environments, and using the information learned about model failures, implements two approaches to create models that are more robust to variances in data. This work finds that model performance varies when exposed to variations in temperature, signal-to-noise ratio (SNR), training data quantity, and receiver hardware. Further, this work finds that Transfer Learning (TL) and Ensemble Learning can be used to create models that mitigate these discovered weaknesses.

The purpose of this thesis is to provide insight into the potential benefits that Radio Frequency Identification (RFID) technology may provide USSOCOM and other commands in the Global War on Terror. This thesis will explain the basic concept behind RFID, and cite some of the current day applications of today that are revolutionizing the civilian sector. More importantly, this thesis will introduce conceptual security applications that could benefit USSOCOM today, highlighting the possible successes and downfalls that these applications might include. / US Army (USA) author

Gases at satisfactory pressures fluoresce in the presence of radio frequency radiation (6). Such fluorescent gases have been used to probe fields of radio frequency oscillation and their emission spectra have been recorded and studied. Ions with multiple charges also exist in these gases, (6). In 1941 Oliver (12) observed the fluorescence of an isobutane- isobutene gaseous mix flowing to a pump through a glass tube which was wrapped by a spiral antenna of a sevenmegacycle transmitter. A white deposit was noticed at a bend in the tubing on the pump side of the fluorescing section of the gas (12, p. 8). In 1957 Blacknall (3) studied the fluorescence and reaction products of propylene in the antenna region of sevenmega- cycle radiation, The oscillator employed by Blacknall was an ARC-5/T-22 military surplus transmitter of range 7.00 to 9.10 megacycles, which he operated at 7.00 megacycles. Blacknall observed a drop in pressure and the formation of a brown deposit in the region of the coil. Blacknall did not report an analysis of this product. In 1959 Armstrong (1) repeated Blacknall's experiments and modified Blacknall's apparatus into an improved design. He also performed an analysis on Blacknall's product. Blacknall used a vertical open-end mercurial manometer with which to measure pressure in his system and as a result introduced mercury vapor into his system. Armstrong tried to minimize the amount of mercury introduced by covering his manometric mercury with a layer of octyl sebacate, "octoil". Armstrong used a spiral-would antenna wrapped around his reaction vessel and reported the formation of spiral brown rings coincident with the copper wire of the antenna. There was a white product deposited in a spiral interlaced with the spiral of dark brown material. No definite identification was reported by Armstrong other than reporting, his solid material as isotactic polypropylene. Other work on electrodeless discharge of organic chemicals has been done in 1960 by Durward Smith and William Smith (13), in 1961 by Jimmie McCarty (10), and in 1963-64 by David Flinn (4).

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The recent demand for wireless transceivers has created a flurry of research into nontraditional receiver architectures. The homodyne receiver, because of its high degree of integration, low complexity and low power consumption, has surfaced a desirable alternative to the well-known heterodyne receiver. However, distortions such as gain and phase imbalance severely degrade the performance of the homodyne receiver. These imbalances, which are caused by impairments of the employed analog devices, are intensified because quadrature demodulation is performed at very high frequencies with a weak input signal. Thus, there exists a great need for low complexity techniques to compensate for these imbalances. In this thesis, we present a new, simplified method for the estimation and the correction of the gain and phase imbalances in a homodyne receiver. The estimation process is based upon carrier re-injection during idle periods of the mobile unit and thus requires only few additional analog components. This approach will be shown to yield tight estimates of the gain and the phase error. Additionally, the correction is performed in the digital domain and thus can be implemented on a digital signal processor. The effectiveness of this method is demonstrated via simulations of an IS-54 transceiver. IS-54 is the North American TDMA standard for dual-mode cellular systems. / Graduation date: 1998

Thesis (M.S.)--Montana State University--Bozeman, 2006. / Typescript. Chairperson, Graduate Committee: James P. Becker. Includes bibliographical references (leaves 120-121).

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Thesis (M.S.)--Worcester Polytechnic Institute. / Keywords: thermal effects, MEMS switches, RF switches. Includes bibliographical references (p. 123-127).

Title from PDF of title page (University of Missouri--Columbia, viewed on Feb. 19, 2008). The entire thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file; a non-technical public abstract appears in the public.pdf file. Dr. Luis Occeña, Thesis Supervisor. Includes bibliographical references.

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