将强度Pareto进化算法2 (SPEA2)应用于同步多任务波形设计

Vincent J. Amuso, Jason Enslin
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引用次数: 42

摘要

本文进一步发展了进化计算,特别是遗传算法(GAs)在同步传输正交波形设计中的应用。该应用程序的目标是为同时执行多个雷达任务的单一平台雷达系统确定一套“最佳”波形(在Pareto意义上)。波形组是通过应用由Zitzler, Laumanns & Theile(2002)开发的强度Pareto进化算法2 (SPEA2)来确定的,以找到成功实现一系列特定于各种雷达任务的目标的波形参数。要优化的目标是由感兴趣的特定任务决定的。SPEA2算法将这些目标函数映射到实际雷达性能参数,以确定如何在Pareto最优意义下使用单个雷达系统同时执行多个雷达任务,如GMTI, AMTI, SAR等。给出了一个按比例缩小的多任务多目标函数场景的初步结果。
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The Strength Pareto Evolutionary Algorithm 2 (SPEA2) applied to simultaneous multi- mission waveform design
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 (2002) 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.
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