论文标题
脉冲压缩雷达的相位代码发现：一种遗传算法方法
Phase Code Discovery for Pulse Compression Radar: A Genetic Algorithm Approach
论文作者
论文摘要
长期以来，发现具有所需属性的序列一直是一种有趣的智力追求。在脉冲压缩雷达（PCR）中，发现具有较低上膜的自相关的相位代码对于良好的估计性能至关重要。但是，相位代码的设计在数学上是非平凡的，因为序列的多个自发自相关属性对于表征而言是棘手的。在本文中，我们提出了一种遗传算法（GA）方法，以发现使用错配滤波器（MMF）接收器的PCR的新相位代码。发达的GA（称为Gaseq）发现了比最新技术更好的相位代码。以59的代码长度，GASEQ发现的序列达到了50.84的信噪比（SCR），而最著名的序列的SCR为45.16。此外，GASEQ的效率和可扩展性使我们能够以更长的代码长度进行搜索阶段代码，从而阻止了现有的基于深度学习的方法。以100的代码长度，Gaseq发现的最佳相位代码的SCR为63.23。
Discovering sequences with desired properties has long been an interesting intellectual pursuit. In pulse compression radar (PCR), discovering phase codes with low aperiodic autocorrelations is essential for a good estimation performance. The design of phase code, however, is mathematically non-trivial as the aperiodic autocorrelation properties of a sequence are intractable to characterize. In this paper, we put forth a genetic algorithm (GA) approach to discover new phase codes for PCR with the mismatched filter (MMF) receiver. The developed GA, dubbed GASeq, discovers better phase codes than the state of the art. At a code length of 59, the sequence discovered by GASeq achieves a signal-to-clutter ratio (SCR) of 50.84, while the best-known sequence has an SCR of 45.16. In addition, the efficiency and scalability of GASeq enable us to search phase codes with a longer code length, which thwarts existing deep learning-based approaches. At a code length of 100, the best phase code discovered by GASeq exhibit an SCR of 63.23.
