{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:RC2V5UYYVHXX4MMWUQEPLREVWU","short_pith_number":"pith:RC2V5UYY","schema_version":"1.0","canonical_sha256":"88b55ed318a9ef7e3196a408f5c495b5076d1244b91f49cddcbae88ec38ee83e","source":{"kind":"arxiv","id":"1710.02808","version":2},"attestation_state":"computed","paper":{"title":"Optimal Estimation of Sensor Biases for Asynchronous Multi-Sensor Data Fusion","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.IT"],"primary_cat":"cs.IT","authors_text":"Hongwei Liu, Junkun Yan, Wenqiang Pu, Ya-Feng Liu, Zhi-Quan Luo","submitted_at":"2017-10-08T09:23:08Z","abstract_excerpt":"An important step in a multi-sensor surveillance system is to estimate sensor biases from their noisy asynchronous measurements. This estimation problem is computationally challenging due to the highly nonlinear transformation between the global and local coordinate systems as well as the measurement asynchrony from different sensors. In this paper, we propose a novel nonlinear least squares (LS) formulation for the problem by assuming the existence of a reference target moving with an (unknown) constant velocity. We also propose an efficient block coordinate decent (BCD) optimization algorith"},"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":"1710.02808","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2017-10-08T09:23:08Z","cross_cats_sorted":["math.IT"],"title_canon_sha256":"0ec7cb3d1484df5f93240f1057559a218b6fad426ea854271a7b41314aec8c03","abstract_canon_sha256":"4d3bfacfae660c62e54d0b7f307c553cb56efa0f896971439ec73ceed5ffdc72"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:15:42.013915Z","signature_b64":"cYe7rax4tWHVF0vG5vR0Ceh9jD5N/sGuCnc8rVFMQ+k05/1q2S6MmdFi5h82/6lEdaUS98/MUFzH5mP2hwe7DA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"88b55ed318a9ef7e3196a408f5c495b5076d1244b91f49cddcbae88ec38ee83e","last_reissued_at":"2026-05-18T00:15:42.013369Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:15:42.013369Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Optimal Estimation of Sensor Biases for Asynchronous Multi-Sensor Data Fusion","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.IT"],"primary_cat":"cs.IT","authors_text":"Hongwei Liu, Junkun Yan, Wenqiang Pu, Ya-Feng Liu, Zhi-Quan Luo","submitted_at":"2017-10-08T09:23:08Z","abstract_excerpt":"An important step in a multi-sensor surveillance system is to estimate sensor biases from their noisy asynchronous measurements. This estimation problem is computationally challenging due to the highly nonlinear transformation between the global and local coordinate systems as well as the measurement asynchrony from different sensors. In this paper, we propose a novel nonlinear least squares (LS) formulation for the problem by assuming the existence of a reference target moving with an (unknown) constant velocity. We also propose an efficient block coordinate decent (BCD) optimization algorith"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1710.02808","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":""},"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":"1710.02808","created_at":"2026-05-18T00:15:42.013458+00:00"},{"alias_kind":"arxiv_version","alias_value":"1710.02808v2","created_at":"2026-05-18T00:15:42.013458+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1710.02808","created_at":"2026-05-18T00:15:42.013458+00:00"},{"alias_kind":"pith_short_12","alias_value":"RC2V5UYYVHXX","created_at":"2026-05-18T12:31:39.905425+00:00"},{"alias_kind":"pith_short_16","alias_value":"RC2V5UYYVHXX4MMW","created_at":"2026-05-18T12:31:39.905425+00:00"},{"alias_kind":"pith_short_8","alias_value":"RC2V5UYY","created_at":"2026-05-18T12:31:39.905425+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/RC2V5UYYVHXX4MMWUQEPLREVWU","json":"https://pith.science/pith/RC2V5UYYVHXX4MMWUQEPLREVWU.json","graph_json":"https://pith.science/api/pith-number/RC2V5UYYVHXX4MMWUQEPLREVWU/graph.json","events_json":"https://pith.science/api/pith-number/RC2V5UYYVHXX4MMWUQEPLREVWU/events.json","paper":"https://pith.science/paper/RC2V5UYY"},"agent_actions":{"view_html":"https://pith.science/pith/RC2V5UYYVHXX4MMWUQEPLREVWU","download_json":"https://pith.science/pith/RC2V5UYYVHXX4MMWUQEPLREVWU.json","view_paper":"https://pith.science/paper/RC2V5UYY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1710.02808&json=true","fetch_graph":"https://pith.science/api/pith-number/RC2V5UYYVHXX4MMWUQEPLREVWU/graph.json","fetch_events":"https://pith.science/api/pith-number/RC2V5UYYVHXX4MMWUQEPLREVWU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RC2V5UYYVHXX4MMWUQEPLREVWU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RC2V5UYYVHXX4MMWUQEPLREVWU/action/storage_attestation","attest_author":"https://pith.science/pith/RC2V5UYYVHXX4MMWUQEPLREVWU/action/author_attestation","sign_citation":"https://pith.science/pith/RC2V5UYYVHXX4MMWUQEPLREVWU/action/citation_signature","submit_replication":"https://pith.science/pith/RC2V5UYYVHXX4MMWUQEPLREVWU/action/replication_record"}},"created_at":"2026-05-18T00:15:42.013458+00:00","updated_at":"2026-05-18T00:15:42.013458+00:00"}