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.

翻译：本文提出了一类新颖的模型驱动进化框架，用于近场多源定位，解决了基于网格的子空间方法（如MUSIC）和数据依赖的深度学习方法的主要局限性。为此，我们开发了两个互补的进化定位框架，它们直接操作于连续的球面波信号模型，并支持任意阵列几何结构，无需标记数据、离散化的角度-距离网格或架构约束。第一个框架称为近场多模态差分进化（NEMO-DE），将进化种群中的每个个体与单个源相关联，并以顺序方式优化残差最小二乘目标，通过更新数据残差并强制空间分离来估计多个源的位置。为克服NEMO-DE在源间功率严重不平衡时的局限性，我们提出了第二个框架，即近场特征子空间拟合差分进化（NEEF-DE），该框架联合编码所有源位置，并最小化一个子空间拟合准则，使基于模型的阵列响应子空间与接收信号子空间对齐。尽管所提出的框架与算法无关，且兼容各种进化优化器，但本文采用差分进化（DE）作为代表性搜索策略，因其简单性、鲁棒性和强大的实证性能。我们提供了广泛的数值实验，以评估所提出框架在不同系统配置下的性能。这项工作确立了进化计算作为基于模型的近场定位的一种强大而灵活的范式，为该领域的未来创新铺平了道路。