{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:YS6Z2PYWDAYOO4OUAYPGF5M7M6","short_pith_number":"pith:YS6Z2PYW","schema_version":"1.0","canonical_sha256":"c4bd9d3f161830e771d4061e62f59f67bd4f9fa0a116179117960dfa2ff8a1d0","source":{"kind":"arxiv","id":"1704.06922","version":1},"attestation_state":"computed","paper":{"title":"Off-the-grid Two-Dimensional Line Spectral Estimation With Prior Information","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.IT"],"primary_cat":"cs.IT","authors_text":"Farzan Haddadi, Hamid Fathi, Iman Valiulahi, Sajad Daei","submitted_at":"2017-04-23T13:53:49Z","abstract_excerpt":"In this paper, we provide a method to recover off-the-grid frequencies of a signal in two-dimensional (2-D) line spectral estimation. Most of the literature in this field focuses on the case in which the only information is spectral sparsity in a continuous domain and does not consider prior information. However, in many applications such as radar and sonar, one has extra information about the spectrum of the signal of interest. The common way of accommodating prior information is to use weighted atomic norm minimization. We present a new semidefinite program using the theory of positive trigo"},"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":"1704.06922","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2017-04-23T13:53:49Z","cross_cats_sorted":["math.IT"],"title_canon_sha256":"a57cf6d0f98735ba3c1569fb05b460af2d5cd0ca150578b08ea42a196f311bf5","abstract_canon_sha256":"07ade5cb38840e22f15c9c6fa521de5ef238073bde813cb23d750e17580ca6a7"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:45:55.334511Z","signature_b64":"gcyixFb5MER3eS7vGgJKoUa4BYoYuT8oJ8Uwixd4zNeMCJmUHqFiIPQpLjv1pPRTAOraRx45/5dLGZKZ5L55Aw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c4bd9d3f161830e771d4061e62f59f67bd4f9fa0a116179117960dfa2ff8a1d0","last_reissued_at":"2026-05-18T00:45:55.333880Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:45:55.333880Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Off-the-grid Two-Dimensional Line Spectral Estimation With Prior Information","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.IT"],"primary_cat":"cs.IT","authors_text":"Farzan Haddadi, Hamid Fathi, Iman Valiulahi, Sajad Daei","submitted_at":"2017-04-23T13:53:49Z","abstract_excerpt":"In this paper, we provide a method to recover off-the-grid frequencies of a signal in two-dimensional (2-D) line spectral estimation. Most of the literature in this field focuses on the case in which the only information is spectral sparsity in a continuous domain and does not consider prior information. However, in many applications such as radar and sonar, one has extra information about the spectrum of the signal of interest. The common way of accommodating prior information is to use weighted atomic norm minimization. We present a new semidefinite program using the theory of positive trigo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1704.06922","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"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":"1704.06922","created_at":"2026-05-18T00:45:55.333978+00:00"},{"alias_kind":"arxiv_version","alias_value":"1704.06922v1","created_at":"2026-05-18T00:45:55.333978+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1704.06922","created_at":"2026-05-18T00:45:55.333978+00:00"},{"alias_kind":"pith_short_12","alias_value":"YS6Z2PYWDAYO","created_at":"2026-05-18T12:31:56.362134+00:00"},{"alias_kind":"pith_short_16","alias_value":"YS6Z2PYWDAYOO4OU","created_at":"2026-05-18T12:31:56.362134+00:00"},{"alias_kind":"pith_short_8","alias_value":"YS6Z2PYW","created_at":"2026-05-18T12:31:56.362134+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/YS6Z2PYWDAYOO4OUAYPGF5M7M6","json":"https://pith.science/pith/YS6Z2PYWDAYOO4OUAYPGF5M7M6.json","graph_json":"https://pith.science/api/pith-number/YS6Z2PYWDAYOO4OUAYPGF5M7M6/graph.json","events_json":"https://pith.science/api/pith-number/YS6Z2PYWDAYOO4OUAYPGF5M7M6/events.json","paper":"https://pith.science/paper/YS6Z2PYW"},"agent_actions":{"view_html":"https://pith.science/pith/YS6Z2PYWDAYOO4OUAYPGF5M7M6","download_json":"https://pith.science/pith/YS6Z2PYWDAYOO4OUAYPGF5M7M6.json","view_paper":"https://pith.science/paper/YS6Z2PYW","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1704.06922&json=true","fetch_graph":"https://pith.science/api/pith-number/YS6Z2PYWDAYOO4OUAYPGF5M7M6/graph.json","fetch_events":"https://pith.science/api/pith-number/YS6Z2PYWDAYOO4OUAYPGF5M7M6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YS6Z2PYWDAYOO4OUAYPGF5M7M6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YS6Z2PYWDAYOO4OUAYPGF5M7M6/action/storage_attestation","attest_author":"https://pith.science/pith/YS6Z2PYWDAYOO4OUAYPGF5M7M6/action/author_attestation","sign_citation":"https://pith.science/pith/YS6Z2PYWDAYOO4OUAYPGF5M7M6/action/citation_signature","submit_replication":"https://pith.science/pith/YS6Z2PYWDAYOO4OUAYPGF5M7M6/action/replication_record"}},"created_at":"2026-05-18T00:45:55.333978+00:00","updated_at":"2026-05-18T00:45:55.333978+00:00"}