{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2015:VG5RN3YO6E5GUTWWUTM4FNXWKF","short_pith_number":"pith:VG5RN3YO","schema_version":"1.0","canonical_sha256":"a9bb16ef0ef13a6a4ed6a4d9c2b6f6516db88982a306793f74ef06ece52d35e6","source":{"kind":"arxiv","id":"1503.01898","version":1},"attestation_state":"computed","paper":{"title":"Joint Detection and Super-Resolution Estimation of Multipath Signal Parameters Using Incremental Automatic Relevance Determination","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.AP","authors_text":"Dmitriy Shutin, Nicolas Schneckenburger","submitted_at":"2015-03-06T10:17:28Z","abstract_excerpt":"The presented work investigates a sparse Bayesian incremental automatic relevance determination (IARD) algorithm in the context of multipath parameter estimation in a super-resolution regime. The corresponding estimation problem is highly nonlinear and, in general, requires an estimation of the number of multipath components. In the IARD approach individual multipath components are processed sequentially, which permits a tractable convergence analysis of the corresponding inference expressions. This leads to a simple condition, termed here a pruning condition, that determines if a multipath co"},"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":"1503.01898","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.AP","submitted_at":"2015-03-06T10:17:28Z","cross_cats_sorted":[],"title_canon_sha256":"77874c13938b06cae1e0c23eeb9074a04cae87f5f2670e50931515bd8d2b6ec4","abstract_canon_sha256":"501ea27e8f83a86ed95bb3de21484babe6bb2b2c69734575b86608d42130f346"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:25:27.322584Z","signature_b64":"CQthetVI11DZt9YPUhBy0fxk692Rr2NbpHNMKO7UUVg4iECj7WTpxfCnIDK9Swlu6BxT67DR0n5AKMkc9IZ6BA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a9bb16ef0ef13a6a4ed6a4d9c2b6f6516db88982a306793f74ef06ece52d35e6","last_reissued_at":"2026-05-18T02:25:27.322145Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:25:27.322145Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Joint Detection and Super-Resolution Estimation of Multipath Signal Parameters Using Incremental Automatic Relevance Determination","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.AP","authors_text":"Dmitriy Shutin, Nicolas Schneckenburger","submitted_at":"2015-03-06T10:17:28Z","abstract_excerpt":"The presented work investigates a sparse Bayesian incremental automatic relevance determination (IARD) algorithm in the context of multipath parameter estimation in a super-resolution regime. The corresponding estimation problem is highly nonlinear and, in general, requires an estimation of the number of multipath components. In the IARD approach individual multipath components are processed sequentially, which permits a tractable convergence analysis of the corresponding inference expressions. This leads to a simple condition, termed here a pruning condition, that determines if a multipath co"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1503.01898","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":"1503.01898","created_at":"2026-05-18T02:25:27.322207+00:00"},{"alias_kind":"arxiv_version","alias_value":"1503.01898v1","created_at":"2026-05-18T02:25:27.322207+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1503.01898","created_at":"2026-05-18T02:25:27.322207+00:00"},{"alias_kind":"pith_short_12","alias_value":"VG5RN3YO6E5G","created_at":"2026-05-18T12:29:44.643036+00:00"},{"alias_kind":"pith_short_16","alias_value":"VG5RN3YO6E5GUTWW","created_at":"2026-05-18T12:29:44.643036+00:00"},{"alias_kind":"pith_short_8","alias_value":"VG5RN3YO","created_at":"2026-05-18T12:29:44.643036+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/VG5RN3YO6E5GUTWWUTM4FNXWKF","json":"https://pith.science/pith/VG5RN3YO6E5GUTWWUTM4FNXWKF.json","graph_json":"https://pith.science/api/pith-number/VG5RN3YO6E5GUTWWUTM4FNXWKF/graph.json","events_json":"https://pith.science/api/pith-number/VG5RN3YO6E5GUTWWUTM4FNXWKF/events.json","paper":"https://pith.science/paper/VG5RN3YO"},"agent_actions":{"view_html":"https://pith.science/pith/VG5RN3YO6E5GUTWWUTM4FNXWKF","download_json":"https://pith.science/pith/VG5RN3YO6E5GUTWWUTM4FNXWKF.json","view_paper":"https://pith.science/paper/VG5RN3YO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1503.01898&json=true","fetch_graph":"https://pith.science/api/pith-number/VG5RN3YO6E5GUTWWUTM4FNXWKF/graph.json","fetch_events":"https://pith.science/api/pith-number/VG5RN3YO6E5GUTWWUTM4FNXWKF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VG5RN3YO6E5GUTWWUTM4FNXWKF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VG5RN3YO6E5GUTWWUTM4FNXWKF/action/storage_attestation","attest_author":"https://pith.science/pith/VG5RN3YO6E5GUTWWUTM4FNXWKF/action/author_attestation","sign_citation":"https://pith.science/pith/VG5RN3YO6E5GUTWWUTM4FNXWKF/action/citation_signature","submit_replication":"https://pith.science/pith/VG5RN3YO6E5GUTWWUTM4FNXWKF/action/replication_record"}},"created_at":"2026-05-18T02:25:27.322207+00:00","updated_at":"2026-05-18T02:25:27.322207+00:00"}