Microwave neural networks and fuzzy classifiers for ES system

This thesis introduces techniques to build novel ES systems. The main contributions are the microwave phase neuron and the fuzzy classifiers. Unlike most of the work available in literature, which present future ES by the sight of traditional methods, this thesis discusses two novel proposals. The first chapters formalise some theoretical points, while the last chapters describe and analyse the proposed novel designs. Chapter 1 introduces Electronic Warfare (EW). It also analyses the electromagnetic environment faced by a naval platform and the traditional ES systems. Chapter 2 makes use of fuzzy theory to provide a formal theoretical study of signal classification in EW. Chapter 3 analyses the heuristics applying fuzzy logics, fuzzy numbers and fuzzy aggregation connectives. Chapter 4 presents the microwave phase neurons. It describes the basic mathematical formulation and the evolution of this concept from its early stages. It also presents the results obtained from simulation of several phase-neuron topologies. The phase neuron is a completely new artificial neural network paradigm. Chapter 5 models fuzzy inference engines. It indicates how these systems work in several different situations and analyse the results of several simulations. It investigates data-fusion techniques and the demands of automatic target recognitors (ATR). This chapter introduces the fuzzy classifiers and the fuzzy identification filters (FIF). Each FIF combines the outputs of the several classifiers to calculate the degree of belief of each possible outcome. This new architecture is another main contribution of this work. Chapter 6 presents the work being presently conducted with microwave classifiers. The results from the simulation of some possible system architectures are commented. Chapter 7 presents the final conclusions and provides suggestions for further research.