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arXiv Machine Learning By Julian P. Merkofer, Vincent van de Schaft, Ruud J. G. van Sloun

Learning Array Signal Topologies as Conditional Neural Manifolds

Read the original on arXiv Machine Learning →

The paper introduces the Conditional Neural Manifold (CNM), a data‑driven replacement for the fixed array manifold used in subspace methods like MUSIC. CNM learns a mapping from source parameters to steering vectors conditioned on observed snapshots, using an encoder to produce a latent scene representation that drives a neural field over the parameter space. By shaping the resulting MUSIC landscape, CNM restores super‑resolution performance under array imperfections, colored noise, correlated sources, near‑field propagation, and resolves the angle‑frequency ambiguity without requiring steering‑vector supervision.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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