Multi-Mission Radar Waveform Design via a Distributed SPEA2 Genetic Algorithm

This thesis furthers the development of Genetic Algorithms (GAs) and their application to the design of multi-mission radar waveforms. An application was developed with the goal of developing a waveform suite that finds the Pareto optimal solutions to a multi-objective optimization radar problem. Utilizing the Strength Pareto Evolutionary Algorithm 2 (SPEA2) a series of radar parameters are optimized along the fitness metrics of interest. This implementation builds upon previous work to develop an application that is capable of analyzing longer more realistic scenarios by using a distributed grid computer to spread the computational load across multiple CPUs. It also advances the previous research by solving for the Pareto optimal front of a simultaneous Synthetic Aperture Radar (SAR) and Moving Target Indication (MTI) mission. These results are presented to validate the performance of the new distributed application against previous work and introduce results of larger more realistic scenarios for a multi-mission radar suite.