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

2026-03-08

AI Summary

This work addresses the limitations of conventional grid-based subspace methods (e.g., MUSIC) and data-driven deep learning approaches for near-field multi-source localization, which often rely on predefined grids, labeled data, or specific network architectures. For the first time, evolutionary computation is introduced to this domain, yielding two grid-free, unsupervised optimization frameworks based on a continuous spherical wave model: NEMO-DE, which minimizes residual error, and NEEF-DE, which employs subspace fitting. Both methods accommodate arbitrary array geometries and directly optimize source locations in the continuous domain, effectively handling challenges such as power imbalance. Extensive experiments demonstrate that the proposed approaches achieve high localization accuracy across diverse configurations, underscoring the flexibility and superiority of evolutionary computation as a model-driven paradigm.

Technology Category

• Search and Optimization: Evolutionary Computation
• Machine Learning: Evolutionary Learning
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

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.

Problem

• near-field localization
• multi-source localization
• evolutionary frameworks
• array signal processing
• subspace methods

Innovation

• evolutionary computation
• near-field localization
• spherical-wave model
• subspace fitting
• differential evolution