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Learning Array Signal Topologies as Conditional Neural Manifolds — arXiv:2609.18616

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.18616
License (as archived): CC0-1.0
Archived: 2026-10-15T15:08:06.302Z
SHA-256 of the archived text: b896fcf8fa9e82cbcfb7ccb23a44feee6c694171df0bb69702dbf658e6036f11
Stable URL of this page: https://metraipolis.com/documento/dc_bb85e8907da0

How to cite

Learning Array Signal Topologies as Conditional Neural Manifolds — arXiv:2609.18616 — arXiv (en), https://arxiv.org/abs/2609.18616 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_bb85e8907da0 (Metraipolis archive dc_bb85e8907da0, sha256 b896fcf8fa9e82cbcfb7ccb23a44feee6c694171df0bb69702dbf658e6036f11)

BibTeX

@misc{dcbb85e8907da0,
  title = {Learning Array Signal Topologies as Conditional Neural Manifolds — arXiv:2609.18616},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.18616},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_bb85e8907da0; sha256 b896fcf8fa9e82cbcfb7ccb23a44feee6c694171df0bb69702dbf658e6036f11},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.18616. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-17 in cs.LG).
Categorie arXiv: Signal Processing (eess.SP) ; Machine Learning (cs.LG).
Pagina del lavoro: https://arxiv.org/abs/2609.18616 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-15T14:08:19.267Z dalla pagina https://arxiv.org/catchup/cs.LG/2026-09-17?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).