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The Strength Pareto Evolutionary Algorithm 2 (SPEA2) Applied to Simultaneous Multi-mission Waveform Design

机译:强度帕累托进化算法2(SPEA2)应用于同时多任务波形设计

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This paper furthers the development of the application of Evolutionary Computation, specifically Genetic Algorithms (GAs) to the design of simultaneously transmitted orthogonal waveforms. The goal of the application is to determine a suite of "optimal" waveforms (in the Pareto sense) for a single platform radar system performing multiple radar missions simultaneously. The waveform suite is determined by applying the Strength Pareto Evolutionary Algorithm 2 (SPEA2) developed by Zitzler, Laumanns & Theile [1] to find waveform parameters that successfully realize a set of objectives particular to a variety of radar missions. The objectives to optimize are dictated by the particular missions of interest. The mapping of these objective functions to actual radar performance parameters is used in the SPEA2 algorithm to determine how best to simultaneously perform multiple radar missions such as GMTI, AMTI, SAR etc. using a single radar system in a Pareto optimal sense. Preliminary results are presented for a scaled down multi-mission multi-objective function scenario.
机译:本文传统的发展进化计算的应用,特别是遗传算法(气体)到同时传输正交波形的设计。该应用程序的目标是确定一个同时执行多个雷达任务的单个平台雷达系统的“最佳”波形(在Pareto Sense中)。波形套件是通过施加由Zitzler,Laumanns&Contile [1]开发的强度帕圈进化进化算法2(SPEA2)来找到成功实现一组目标的波形参数,该目标是各种雷达任务。优化的目标是由特定的兴趣特派团决定。这些目标函数对实际雷达性能参数的映射用于SPEA2算法,以确定如何最好地使用帕累托最佳意义上的单个雷达系统等多个雷达特派团等MGTI,AMTI,SAR等。初步结果显示为缩放的缩小多项任务多目标函数方案。

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