{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:SBO4XAEIH5W4TQDIYZDPN2QUW6","short_pith_number":"pith:SBO4XAEI","schema_version":"1.0","canonical_sha256":"905dcb80883f6dc9c068c646f6ea14b79ecf3521abb9e702dc63b4324b9f8d74","source":{"kind":"arxiv","id":"2306.02271","version":2},"attestation_state":"computed","paper":{"title":"SubspaceNet: Deep Learning-Aided Subspace Methods for DoA Estimation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"eess.SP","authors_text":"Dor H. Shmuel, Guy Revach, Julian P. Merkofer, Nir Shlezinger, Ruud J. G. van Sloun","submitted_at":"2023-06-04T06:30:13Z","abstract_excerpt":"Direction of arrival (DoA) estimation is a fundamental task in array processing. A popular family of DoA estimation algorithms are subspace methods, which operate by dividing the measurements into distinct signal and noise subspaces. Subspace methods, such as Multiple Signal Classification (MUSIC) and Root-MUSIC, rely on several restrictive assumptions, including narrowband non-coherent sources and fully calibrated arrays, and their performance is considerably degraded when these do not hold. In this work we propose SubspaceNet; a data-driven DoA estimator which learns how to divide the observ"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2306.02271","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SP","submitted_at":"2023-06-04T06:30:13Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"30e9e2666c2d213e30f9fbd42113c3771c719105ee5e988173f45b54a63457fc","abstract_canon_sha256":"a12aff4f8a487c024f7f82210657d7ff65969d339bcf114696db4cb04bb39e7e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:42:29.378844Z","signature_b64":"RPU/T9DzAe909KLqy58Cl9XaWtLvsBNCbczhhspXk1/mHOFGDQ2wMkfiJgAeCuLbJ84WFrrTmvVFn2dnBJcDCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"905dcb80883f6dc9c068c646f6ea14b79ecf3521abb9e702dc63b4324b9f8d74","last_reissued_at":"2026-07-05T08:42:29.378293Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:42:29.378293Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"SubspaceNet: Deep Learning-Aided Subspace Methods for DoA Estimation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"eess.SP","authors_text":"Dor H. Shmuel, Guy Revach, Julian P. Merkofer, Nir Shlezinger, Ruud J. G. van Sloun","submitted_at":"2023-06-04T06:30:13Z","abstract_excerpt":"Direction of arrival (DoA) estimation is a fundamental task in array processing. A popular family of DoA estimation algorithms are subspace methods, which operate by dividing the measurements into distinct signal and noise subspaces. Subspace methods, such as Multiple Signal Classification (MUSIC) and Root-MUSIC, rely on several restrictive assumptions, including narrowband non-coherent sources and fully calibrated arrays, and their performance is considerably degraded when these do not hold. In this work we propose SubspaceNet; a data-driven DoA estimator which learns how to divide the observ"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2306.02271","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2306.02271/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2306.02271","created_at":"2026-07-05T08:42:29.378359+00:00"},{"alias_kind":"arxiv_version","alias_value":"2306.02271v2","created_at":"2026-07-05T08:42:29.378359+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2306.02271","created_at":"2026-07-05T08:42:29.378359+00:00"},{"alias_kind":"pith_short_12","alias_value":"SBO4XAEIH5W4","created_at":"2026-07-05T08:42:29.378359+00:00"},{"alias_kind":"pith_short_16","alias_value":"SBO4XAEIH5W4TQDI","created_at":"2026-07-05T08:42:29.378359+00:00"},{"alias_kind":"pith_short_8","alias_value":"SBO4XAEI","created_at":"2026-07-05T08:42:29.378359+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/SBO4XAEIH5W4TQDIYZDPN2QUW6","json":"https://pith.science/pith/SBO4XAEIH5W4TQDIYZDPN2QUW6.json","graph_json":"https://pith.science/api/pith-number/SBO4XAEIH5W4TQDIYZDPN2QUW6/graph.json","events_json":"https://pith.science/api/pith-number/SBO4XAEIH5W4TQDIYZDPN2QUW6/events.json","paper":"https://pith.science/paper/SBO4XAEI"},"agent_actions":{"view_html":"https://pith.science/pith/SBO4XAEIH5W4TQDIYZDPN2QUW6","download_json":"https://pith.science/pith/SBO4XAEIH5W4TQDIYZDPN2QUW6.json","view_paper":"https://pith.science/paper/SBO4XAEI","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2306.02271&json=true","fetch_graph":"https://pith.science/api/pith-number/SBO4XAEIH5W4TQDIYZDPN2QUW6/graph.json","fetch_events":"https://pith.science/api/pith-number/SBO4XAEIH5W4TQDIYZDPN2QUW6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SBO4XAEIH5W4TQDIYZDPN2QUW6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SBO4XAEIH5W4TQDIYZDPN2QUW6/action/storage_attestation","attest_author":"https://pith.science/pith/SBO4XAEIH5W4TQDIYZDPN2QUW6/action/author_attestation","sign_citation":"https://pith.science/pith/SBO4XAEIH5W4TQDIYZDPN2QUW6/action/citation_signature","submit_replication":"https://pith.science/pith/SBO4XAEIH5W4TQDIYZDPN2QUW6/action/replication_record"}},"created_at":"2026-07-05T08:42:29.378359+00:00","updated_at":"2026-07-05T08:42:29.378359+00:00"}