{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:VXKKMWGLW7R4ZFYURUR7JW2EVJ","short_pith_number":"pith:VXKKMWGL","schema_version":"1.0","canonical_sha256":"add4a658cbb7e3cc97148d23f4db44aa7fe13703714439c99e893fadff844c61","source":{"kind":"arxiv","id":"1603.06349","version":1},"attestation_state":"computed","paper":{"title":"Distributed Multi-Sensor Fusion Using Generalized Multi-Bernoulli Densities","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SY"],"primary_cat":"stat.ME","authors_text":"Lingjiang Kong, Meng Jiang, Reza Hoseinnezhad, Wei Yi","submitted_at":"2016-03-21T08:14:48Z","abstract_excerpt":"The paper addresses distributed multi-target tracking in the framework of generalized Covariance Intersection (GCI) over multistatic radar system. The proposed method is based on the unlabeled version of generalized labeled multi-Bernoulli (GLMB) family by discarding the labels, referred as generalized multi-Bernoulli (GMB) family. However, it doesn't permit closed form solution for GCI fusion with GMB family. To solve this challenging problem, firstly, we propose an efficient approximation to the GMB family which preserves both the probability hypothesis density (PHD) and cardinality distribu"},"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":"1603.06349","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2016-03-21T08:14:48Z","cross_cats_sorted":["cs.SY"],"title_canon_sha256":"22abe1246a6c1fd040f5a95fd64b058431d1252f8a6a03cb19227bb4d247e155","abstract_canon_sha256":"68671020d75e529b6ad661ea7f65ae39db93f1537399e34f723bb46fac1474f2"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:18:48.699478Z","signature_b64":"Mq1CWsDTKfsyCJH7u/Ot0/oWG5xaNmja51IeNwlu8dgk797a3DkREVG/WnZZuhicqu2wI//kJPJKDtzOIeGSCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"add4a658cbb7e3cc97148d23f4db44aa7fe13703714439c99e893fadff844c61","last_reissued_at":"2026-05-18T01:18:48.698796Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:18:48.698796Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Distributed Multi-Sensor Fusion Using Generalized Multi-Bernoulli Densities","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SY"],"primary_cat":"stat.ME","authors_text":"Lingjiang Kong, Meng Jiang, Reza Hoseinnezhad, Wei Yi","submitted_at":"2016-03-21T08:14:48Z","abstract_excerpt":"The paper addresses distributed multi-target tracking in the framework of generalized Covariance Intersection (GCI) over multistatic radar system. The proposed method is based on the unlabeled version of generalized labeled multi-Bernoulli (GLMB) family by discarding the labels, referred as generalized multi-Bernoulli (GMB) family. However, it doesn't permit closed form solution for GCI fusion with GMB family. To solve this challenging problem, firstly, we propose an efficient approximation to the GMB family which preserves both the probability hypothesis density (PHD) and cardinality distribu"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1603.06349","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":"1603.06349","created_at":"2026-05-18T01:18:48.698913+00:00"},{"alias_kind":"arxiv_version","alias_value":"1603.06349v1","created_at":"2026-05-18T01:18:48.698913+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1603.06349","created_at":"2026-05-18T01:18:48.698913+00:00"},{"alias_kind":"pith_short_12","alias_value":"VXKKMWGLW7R4","created_at":"2026-05-18T12:30:48.956258+00:00"},{"alias_kind":"pith_short_16","alias_value":"VXKKMWGLW7R4ZFYU","created_at":"2026-05-18T12:30:48.956258+00:00"},{"alias_kind":"pith_short_8","alias_value":"VXKKMWGL","created_at":"2026-05-18T12:30:48.956258+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/VXKKMWGLW7R4ZFYURUR7JW2EVJ","json":"https://pith.science/pith/VXKKMWGLW7R4ZFYURUR7JW2EVJ.json","graph_json":"https://pith.science/api/pith-number/VXKKMWGLW7R4ZFYURUR7JW2EVJ/graph.json","events_json":"https://pith.science/api/pith-number/VXKKMWGLW7R4ZFYURUR7JW2EVJ/events.json","paper":"https://pith.science/paper/VXKKMWGL"},"agent_actions":{"view_html":"https://pith.science/pith/VXKKMWGLW7R4ZFYURUR7JW2EVJ","download_json":"https://pith.science/pith/VXKKMWGLW7R4ZFYURUR7JW2EVJ.json","view_paper":"https://pith.science/paper/VXKKMWGL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1603.06349&json=true","fetch_graph":"https://pith.science/api/pith-number/VXKKMWGLW7R4ZFYURUR7JW2EVJ/graph.json","fetch_events":"https://pith.science/api/pith-number/VXKKMWGLW7R4ZFYURUR7JW2EVJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VXKKMWGLW7R4ZFYURUR7JW2EVJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VXKKMWGLW7R4ZFYURUR7JW2EVJ/action/storage_attestation","attest_author":"https://pith.science/pith/VXKKMWGLW7R4ZFYURUR7JW2EVJ/action/author_attestation","sign_citation":"https://pith.science/pith/VXKKMWGLW7R4ZFYURUR7JW2EVJ/action/citation_signature","submit_replication":"https://pith.science/pith/VXKKMWGLW7R4ZFYURUR7JW2EVJ/action/replication_record"}},"created_at":"2026-05-18T01:18:48.698913+00:00","updated_at":"2026-05-18T01:18:48.698913+00:00"}