基于遗传算法的OFDM雷达脉冲设计
Design of OFDM radar pulses using genetic algorithm based techniques
摘要：
进化算法在求解凸优化问题中的优点已得到广泛认可。本文提出了一种基于遗传算法（GA）优化的波形设计框架，以改善依赖于正交频分复用（OFDM）结构的雷达脉冲特性。我们的优化技术的重点是找到最佳的相位码序列的OFDM信号。由于我们考虑了两种不同的雷达处理解决方案，因此使用了几个最优性标准，这些解决方案要求单目标或多目标优化。当所谓的峰均包络功率比（PMEPR）单目标的最小化处理，我们比较我们的研究结果与现有的方法，并强调我们的方法的优点。在双目标优化的范围内，我们首先解决PMEPR和峰值旁瓣电平比（PSLR），并表明我们的方法的基础上的非支配排序遗传算法-II（NSGA-II）提供了设计解决方案，而不是随机设置的相位码有显着的改善。然后，我们研究另一个感兴趣的案例，其中目标函数是旁瓣电平的两个度量，即PSLR和集成旁瓣电平比（ISLR），并建议修改NSGA-II以包含对PMEPR的约束。在最后一部分中，我们通过一个案例研究说明了我们的编码解决方案如何能够最小化单目标PMEPR，同时启用目标检测增强策略，当SNR度量将被选择用于检测框架时。
英文摘要：
The merit of evolutionary algorithms (EA) to solve convex optimization problems is widely acknowledged. In this paper, a genetic algorithm (GA) optimization based waveform design framework is used to improve the features of radar pulses relying on the orthogonal frequency division multiplexing (OFDM) structure. Our optimization techniques focus on finding optimal phase code sequences for the OFDM signal. Several optimality criteria are used since we consider two different radar processing solutions which call either for single or multiple-objective optimizations. When minimization of the so-called peak-to-mean envelope power ratio (PMEPR) single-objective is tackled, we compare our findings with existing methods and emphasize on the merit of our approach. In the scope of the two-objective optimization, we first address PMEPR and peak-to-sidelobe level ratio (PSLR) and show that our approach based on the non-dominated sorting genetic algorithm-II (NSGA-II) provides design solutions with noticeable improvements as opposed to random sets of phase codes. We then look at another case of interest where the objective functions are two measures of the sidelobe level, namely PSLR and the integrated-sidelobe level ratio (ISLR) and propose to modify the NSGA-II to include a constrain on the PMEPR instead. In the last part, we illustrate via a case study how our encoding solution makes it possible to minimize the single objective PMEPR while enabling a target detection enhancement strategy, when the SNR metric would be chosen for the detection framework.
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