AI 中文总结
本文提出条件神经流形（CNM），以观测条件映射替代固定阵列流形，通过无监督学习MUSIC景观，在模型失配下恢复DoA估计分辨率并解决角度-频率模糊。
AI 中文摘要
子空间方法如多信号分类（MUSIC）通过利用阵列流形与测量噪声子空间之间的正交性实现超分辨率到达方向（DoA）估计。因此，其精度依赖于假定的流形，并在模型失配下退化，而无法从空间流形中识别的参数则无法恢复。在本工作中，我们提出条件神经流形（CNM），它用从源参数到导向矢量的观测条件映射替代固定流形。编码器将快照映射到潜在场景表示，该表示对参数空间上的零初始化神经场进行条件化。该流形在没有导向矢量监督的情况下，通过塑造最终的MUSIC景观来学习。由于校正作用于流形而非估计器，因此可被其他基于流形的方法直接使用而无需修改。CNM在阵列缺陷、有色噪声、相关源和近场传播下恢复分辨率，并解决了名义空间流形固有的角度-频率模糊性。
英文摘要
Subspace methods such as multiple signal classification (MUSIC) achieve super-resolution direction of arrival (DoA) estimation by exploiting the orthogonality between the array manifold and the noise subspace of the measurements. Their accuracy therefore depends on the assumed manifold and degrades under model mismatch, while parameters not identifiable from the spatial manifold cannot be recovered. In this work, we propose the conditional neural manifold (CNM), which replaces the fixed manifold with an observation-conditioned mapping from source parameters to steering vectors. An encoder maps the snapshots to a latent scene representation that conditions a zero-initialized neural field over the parameter space. The manifold is learned without steering-vector supervision by shaping the resulting MUSIC landscape. Since the correction acts on the manifold rather than on the estimator, it can be used by other manifold-based methods without modification. The CNM restores resolution under array imperfections, colored noise, correlated sources, and near-field propagation, and resolves the angle-frequency ambiguity inherent to the nominal spatial manifold.
CommentsSubmitted to ICASSP 2027
