Electro-optical systems and neural-inspired processing: A system architecture and technology analysis with applications to law enforcement small arms fire indication systems

Abstract

This dissertation is a transdisciplinary analysis on law-enforcement small arms indication systems. The technology focus is on electro-optical sensors and neural-inspired processing. Dual-color, high-rate, imaging, and high-performance indication systems are all evaluated. A platform-level design decomposition that starts with the customer needs and finishes with the sensor and processor requirements is performed; the requirements are then used for architecture and technology trade studies. To understand sensor solutions, the target phenomenology is studied, a model is developed, and a sensor performance analysis is conducted. To identify processing solutions, a baseline algorithm, using traditional digital signal processing techniques, is identified, its effectiveness is evaluated with stimuli generated from the target/sensor model, and then biological neural network are studied for opportunities to improve the processing. The result is a series of architectures that are evaluated against requirements derived from real-world scenarios. These developed architectures can be later matured into systems that can be used by law-enforcement for surveillance.