A distributed object-oriented multi-mission radar waveform design implementation

V. Amuso, Brent Josefiak
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引用次数: 6

Abstract

This paper 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 the previous work of [1] to develop an application that is capable of analyzing longer more realistic scenarios. 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 preliminary results are presented to validate the performance of the new application against previous work and introduce some results of the multi-mission radar suite.
一个分布式面向对象的多任务雷达波形设计实现
本文进一步发展了遗传算法及其在多任务雷达波形设计中的应用。开发了一个应用程序,其目标是开发一个波形套件,为多目标优化雷达问题找到帕累托最优解。利用强度帕累托进化算法2 (SPEA2),沿着感兴趣的适应度指标优化了一系列雷达参数。此实现建立在先前[1]工作的基础上,以开发能够分析更长时间更现实的场景的应用程序。通过求解合成孔径雷达(SAR)和运动目标指示(MTI)同步任务的帕累托最优阵,进一步推进了前人的研究。这些初步结果是为了验证新应用与先前工作的性能,并介绍多任务雷达套件的一些结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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