Low-Complexity Neural Pursuit for Line Spectral Estimation

Abstract

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.