低复杂度神经追踪用于线谱估计
Low-Complexity Neural Pursuit for Line Spectral Estimation
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中文总结 AI 辅助
本文提出NeuPLSE框架，融合模型驱动与数据驱动，通过残差分析联合实现模型阶数检测与频谱参数估计，支持1D到MD扩展，在ISAC场景中实现高精度估计并显著降低计算复杂度。
中文摘要 AI 辅助
本文提出了一种高精度且低复杂度的框架，称为神经追踪线谱估计（NeuPLSE），用于从单次快照中进行离网线谱估计。该框架系统地将模型驱动的信号结构与数据驱动的适应性相结合，实现了连续域频谱表示以及自适应参数学习。在此框架内，通过残差分析联合实现模型阶数检测和频谱参数估计，并协调使用全局和广义分量级残差信息以确保鲁棒且准确的估计性能。此外，NeuPLSE支持从一维（1D）到多维（MD）场景的统一扩展，同时保持低计算复杂度。在代表性集成感知与通信（ISAC）场景中的大量仿真表明，NeuPLSE实现了准确的模型阶数检测和高精度频谱估计。在稀疏路径设置中，它接近Cramér-Rao下界，而在密集路径设置中，它以显著低于最先进方法的计算复杂度实现了具有竞争力的重建性能。
英文摘要
In this paper, we propose a high-precision yet low-complexity framework, termed Neural Pursuit for Line Spectral Estimation (NeuPLSE), for off-grid line spectral estimation from a single snapshot. The proposed framework systematically integrates model-driven signal structures with data-driven adaptability, enabling continuous-domain spectral representation together with adaptive parameter learning. Within this framework, model order detection and spectral parameter estimation are jointly achieved through residual analysis, with coordinated use of global and generalized component-wise residual information to ensure robust and accurate estimation performance. Moreover, NeuPLSE supports a unified extension from one-dimensional (1D) to multi-dimensional (MD) scenarios while maintaining low computational complexity. Extensive simulations in representative integrated sensing and communication (ISAC) scenarios demonstrate that NeuPLSE achieves accurate model order detection and high-precision spectral estimation. In sparse-path settings, it approaches the Cramér-Rao lower bound, while in dense-path settings, it attains competitive reconstruction performance with significantly lower computational complexity than state-of-the-art methods.
