AI 中文总结
本文研究近场定位问题，核心方法是采用群体与进化计算技术，该技术适用于近场定位复杂优化环境，相比传统方法具有优势，有望在近场定位中发挥重要作用。
AI 中文摘要
近年来，随着向更高频率和超大孔径阵列的发展，近场定位备受关注，这扩大了近场区域并引入许多源，意味着天线阵列可用于角度和距离定位。虽已开发多种定位方法，但各有局限。本文聚焦群体与进化计算（SEC）技术，其在近场定位复杂优化环境中很适用，比传统方法如基于网格的子空间方法和深度学习方法有优势。
英文摘要
Near-field localization has attracted significant attention in recent years due to the move toward higher frequencies and extremely large aperture arrays, which expand the near-field region and bring many sources into it. This implies that antenna arrays can be used to localize not only in angle but also in range. Although a wide range of localization methods has been developed, each comes with limitations that may hinder practical deployment. This article focuses on a class of techniques that has received relatively little attention in the prior literature despite its strong potential for accurate and efficient location estimation: swarm and evolutionary computation (SEC). These methods are well-suited to the complex optimization landscape of near-field localization and can offer important advantages over conventional approaches such as grid-based subspace methods and deep learning approaches.
