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.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/119414 |
| Date | 12 June 2024 |
| Creators | Olds, Brennan Edson |
| Contributors | Electrical and Computer Engineering, Michaels, Alan J., Ruohoniemi, John Michael, Talty, Timothy Joseph |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | ETD, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
Page generated in 0.0136 seconds