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arXiv · 2607.20139

Swarm and Evolutionary Computation for Near-Field Localization

Parisa Ramezani·Seyed Jalaleddin Mousavirad·Mattias O'Nils·Emil Björnson
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Abstract

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.

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Parisa Ramezani, Seyed Jalaleddin Mousavirad, Mattias O'Nils, Emil Björnson. 2026-07-22. Swarm and Evolutionary Computation for Near-Field Localization. https://arxiv.org/abs/2607.20139

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