{"paper":{"title":"Super-resolution Multi-signal Direction-of-Arrival Estimation by Hankel-structured Sensing and Decomposition","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"Hankel-structured sensing and decomposition yields super-resolution multi-signal direction-of-arrival estimates that are maximum-likelihood optimal under Gaussian or Laplace noise.","cross_cats":["eess.SP"],"primary_cat":"cs.LG","authors_text":"Dimitris A. Pados, Elizabeth S. Bentley, George Sklivanitis, Georgios I. Orfanidis","submitted_at":"2026-04-29T15:25:10Z","abstract_excerpt":"Motivated by sensing modalities in modern autonomous systems that involve hardware-constrained spatial sampling over large arrays with limited coherence time, we develop a novel framework for rapid super-resolution multi-signal direction-of-arrival (DoA) estimation based on Hankel-structured sensing and data matrix decomposition of arbitrary rank, under both the $L_2$ and $L_1$-norm formulation. The resulting $L_2$-norm estimator is shown to be maximum-likelihood optimal in white Gaussian noise. The $L_1$-norm estimator is shown to be maximum-likelihood optimal in independent, identically dist"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"The resulting L2-norm estimator is shown to be maximum-likelihood optimal in white Gaussian noise. The L1-norm estimator is shown to be maximum-likelihood optimal in independent, identically distributed (i.i.d.) isotropic Laplace noise, offering broad robustness to impulsive interference and corrupted measurements commonly encountered in practice.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The received data admits a Hankel structure that permits arbitrary-rank decomposition, and the noise exactly matches the assumed white Gaussian or i.i.d. isotropic Laplace distributions.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"A Hankel-structured sensing and arbitrary-rank decomposition approach yields maximum-likelihood optimal L2 and L1 estimators for multi-signal DoA that outperform recent methods in resolution probability and required SNR.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Hankel-structured sensing and decomposition yields super-resolution multi-signal direction-of-arrival estimates that are maximum-likelihood optimal under Gaussian or Laplace noise.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"399bac2ac63cf7dd7220467c91c7da8401f0cdaf7c724d89be9c4720d6e4c580"},"source":{"id":"2604.26793","kind":"arxiv","version":2},"verdict":{"id":"59ffe887-a8e3-415d-b43a-1edf143b414b","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-07T13:18:30.692220Z","strongest_claim":"The resulting L2-norm estimator is shown to be maximum-likelihood optimal in white Gaussian noise. The L1-norm estimator is shown to be maximum-likelihood optimal in independent, identically distributed (i.i.d.) isotropic Laplace noise, offering broad robustness to impulsive interference and corrupted measurements commonly encountered in practice.","one_line_summary":"A Hankel-structured sensing and arbitrary-rank decomposition approach yields maximum-likelihood optimal L2 and L1 estimators for multi-signal DoA that outperform recent methods in resolution probability and required SNR.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The received data admits a Hankel structure that permits arbitrary-rank decomposition, and the noise exactly matches the assumed white Gaussian or i.i.d. isotropic Laplace distributions.","pith_extraction_headline":"Hankel-structured sensing and decomposition yields super-resolution multi-signal direction-of-arrival estimates that are maximum-likelihood optimal under Gaussian or Laplace noise."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2604.26793/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"doi_compliance","ran_at":"2026-05-19T19:46:05.193901Z","status":"completed","version":"1.0.0","findings_count":0}],"snapshot_sha256":"8c160250d2fb9631047f401b72237ad41e16df068aa949471aac65a57e279e82"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}