{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:Z2ZVNN6VCRLQU4TWCSKK5BMNQV","short_pith_number":"pith:Z2ZVNN6V","schema_version":"1.0","canonical_sha256":"ceb356b7d514570a72761494ae858d857b7a356aca3058cb33b2457dce9312b6","source":{"kind":"arxiv","id":"1912.00450","version":1},"attestation_state":"computed","paper":{"title":"Nonlinear State Estimation using Gaussian Integral","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"eess.SP","authors_text":"Kundan Kumar, Shovan Bhaumik","submitted_at":"2019-12-01T17:25:00Z","abstract_excerpt":"In this letter, a new filtering technique to solve a nonlinear state estimation problem has been developed. It is well known that for a nonlinear system, the prior and posterior probability density functions (pdf) are non-Gaussian in nature. However, in this work, they are assumed as Gaussian and subsequently mean, and covariance of them are calculated. In the proposed method, nonlinear functions of process dynamics and measurement are expressed in a polynomial form with the help of Taylor series expansion. In order to calculate the prior and the posterior mean and covariance, the functions ar"},"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":"1912.00450","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SP","submitted_at":"2019-12-01T17:25:00Z","cross_cats_sorted":[],"title_canon_sha256":"ae8f95dfb9241ce43a2e5f7f69a1ef6389c9739335099d1eac77b6f9497fa10e","abstract_canon_sha256":"518095869de0019fa13b49fb5f02b3fd04bf684125366f5bb4b5b2496ad888dd"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:23:14.101896Z","signature_b64":"Jln4s/2sC+SYjUvuAxZljb8vBXYJ9AuMnLCC9tCudjBovvNyntKIImnN05F1smneRCou4vB7Knq5wDrBewM/CQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ceb356b7d514570a72761494ae858d857b7a356aca3058cb33b2457dce9312b6","last_reissued_at":"2026-07-05T00:23:14.101530Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:23:14.101530Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Nonlinear State Estimation using Gaussian Integral","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"eess.SP","authors_text":"Kundan Kumar, Shovan Bhaumik","submitted_at":"2019-12-01T17:25:00Z","abstract_excerpt":"In this letter, a new filtering technique to solve a nonlinear state estimation problem has been developed. It is well known that for a nonlinear system, the prior and posterior probability density functions (pdf) are non-Gaussian in nature. However, in this work, they are assumed as Gaussian and subsequently mean, and covariance of them are calculated. In the proposed method, nonlinear functions of process dynamics and measurement are expressed in a polynomial form with the help of Taylor series expansion. In order to calculate the prior and the posterior mean and covariance, the functions ar"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1912.00450","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/1912.00450/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":"1912.00450","created_at":"2026-07-05T00:23:14.101591+00:00"},{"alias_kind":"arxiv_version","alias_value":"1912.00450v1","created_at":"2026-07-05T00:23:14.101591+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1912.00450","created_at":"2026-07-05T00:23:14.101591+00:00"},{"alias_kind":"pith_short_12","alias_value":"Z2ZVNN6VCRLQ","created_at":"2026-07-05T00:23:14.101591+00:00"},{"alias_kind":"pith_short_16","alias_value":"Z2ZVNN6VCRLQU4TW","created_at":"2026-07-05T00:23:14.101591+00:00"},{"alias_kind":"pith_short_8","alias_value":"Z2ZVNN6V","created_at":"2026-07-05T00:23:14.101591+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/Z2ZVNN6VCRLQU4TWCSKK5BMNQV","json":"https://pith.science/pith/Z2ZVNN6VCRLQU4TWCSKK5BMNQV.json","graph_json":"https://pith.science/api/pith-number/Z2ZVNN6VCRLQU4TWCSKK5BMNQV/graph.json","events_json":"https://pith.science/api/pith-number/Z2ZVNN6VCRLQU4TWCSKK5BMNQV/events.json","paper":"https://pith.science/paper/Z2ZVNN6V"},"agent_actions":{"view_html":"https://pith.science/pith/Z2ZVNN6VCRLQU4TWCSKK5BMNQV","download_json":"https://pith.science/pith/Z2ZVNN6VCRLQU4TWCSKK5BMNQV.json","view_paper":"https://pith.science/paper/Z2ZVNN6V","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1912.00450&json=true","fetch_graph":"https://pith.science/api/pith-number/Z2ZVNN6VCRLQU4TWCSKK5BMNQV/graph.json","fetch_events":"https://pith.science/api/pith-number/Z2ZVNN6VCRLQU4TWCSKK5BMNQV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/Z2ZVNN6VCRLQU4TWCSKK5BMNQV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/Z2ZVNN6VCRLQU4TWCSKK5BMNQV/action/storage_attestation","attest_author":"https://pith.science/pith/Z2ZVNN6VCRLQU4TWCSKK5BMNQV/action/author_attestation","sign_citation":"https://pith.science/pith/Z2ZVNN6VCRLQU4TWCSKK5BMNQV/action/citation_signature","submit_replication":"https://pith.science/pith/Z2ZVNN6VCRLQU4TWCSKK5BMNQV/action/replication_record"}},"created_at":"2026-07-05T00:23:14.101591+00:00","updated_at":"2026-07-05T00:23:14.101591+00:00"}