A Primer on Evolutionary Frameworks for Near-Field Multi-Source Localization

TL;DR

This paper presents new evolutionary frameworks for near-field multi-source localization that operate directly on continuous models, overcoming limitations of traditional grid-based and deep learning methods, and support arbitrary array geometries without labeled data.

Contribution

Introduction of two novel evolutionary localization frameworks, NEMO-DE and NEEF-DE, that work on continuous models and are compatible with various optimizers, enhancing near-field source localization accuracy and flexibility.

Findings

NEMO-DE effectively estimates multiple sources with spatial separation.

NEEF-DE handles large power imbalances among sources.

Frameworks outperform traditional grid-based methods in various configurations.

Abstract

This paper introduces a novel class of model-driven evolutionary frameworks for near-field multi-source localization, addressing the major limitations of grid-based subspace methods such as MUSIC and data-dependent deep learning approaches. To this end, we develop two complementary evolutionary localization frameworks that operate directly on the continuous spherical-wave signal model and support arbitrary array geometries without requiring labeled data, discretized angle--range grids, or architectural constraints. The first framework, termed NEar-field MultimOdal DE (NEMO-DE) associates each individual in the evolutionary population to a single source and optimizes a residual least-squares objective in a sequential manner, updating the data residual and enforcing spatial separation to estimate multiple source locations. To overcome the limitation of NEMO-DE under large power imbalances among the sources, we propose the second framework, named NEar-field Eigen-subspace Fitting DE (NEEF-DE), which jointly encodes all source locations and minimizes a subspace-fitting criterion that aligns a model-based array response subspace with the received signal subspace. Although the proposed frameworks are algorithm-agnostic and compatible with various evolutionary optimizers, differential evolution (DE) is adopted in this work as a representative search strategy due to its simplicity, robustness, and strong empirical performance. We provide extensive numerical experiments to evaluate the performance of the proposed frameworks under different system configurations. This work establishes evolutionary computation as a powerful and flexible paradigm for model-based near-field localization, paving the way for future innovations in this domain.