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.

Library of Congress Subject Headings

Radar--Data processing; Genetic algorithms; Evolutionary computation

Publication Date


Document Type


Department, Program, or Center

Microelectronic Engineering (KGCOE)


Amuso, Vincent


Note: imported from RIT’s Digital Media Library running on DSpace to RIT Scholar Works. Physical copy available through RIT's The Wallace Library at: TK6575 .J67 2012


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