{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:WPBGXAL5JYZTPUQSI3OUOCO2U3","short_pith_number":"pith:WPBGXAL5","schema_version":"1.0","canonical_sha256":"b3c26b817d4e3337d21246dd4709daa6cecd7e6b88b7ed5c647febc7d33cd5d0","source":{"kind":"arxiv","id":"1702.00884","version":1},"attestation_state":"computed","paper":{"title":"Adaptive Adjustment of Noise Covariance in Kalman Filter for Dynamic State Estimation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.SY","authors_text":"Ning Zhou, Shahrokh Akhlaghi, Zhenyu Huang","submitted_at":"2017-02-03T01:21:35Z","abstract_excerpt":"Accurate estimation of the dynamic states of a synchronous machine (e.g., rotor s angle and speed) is essential in monitoring and controlling transient stability of a power system. It is well known that the covariance matrixes of process noise (Q) and measurement noise (R) have a significant impact on the Kalman filter s performance in estimating dynamic states. The conventional ad-hoc approaches for estimating the covariance matrixes are not adequate in achieving the best filtering performance. To address this problem, this paper proposes an adaptive filtering approach to adaptively estimate "},"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":"1702.00884","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SY","submitted_at":"2017-02-03T01:21:35Z","cross_cats_sorted":[],"title_canon_sha256":"de6b685c45c02be51657a6824d0f9b4e4917b41d43335df2250451e42dea571e","abstract_canon_sha256":"1ab67536ce9118fa29f7737c76740df3768e48c66c186e6bf77066f897c5084b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:51:29.519909Z","signature_b64":"qr2FdQI95k/uIF8clYRv3gYOSRmil6+pN0RDdibT2giuObiJOOBCkeU6FM6vHfEhxeQ3U9VLEgMBIPeBCHRJAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b3c26b817d4e3337d21246dd4709daa6cecd7e6b88b7ed5c647febc7d33cd5d0","last_reissued_at":"2026-05-18T00:51:29.519448Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:51:29.519448Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Adaptive Adjustment of Noise Covariance in Kalman Filter for Dynamic State Estimation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.SY","authors_text":"Ning Zhou, Shahrokh Akhlaghi, Zhenyu Huang","submitted_at":"2017-02-03T01:21:35Z","abstract_excerpt":"Accurate estimation of the dynamic states of a synchronous machine (e.g., rotor s angle and speed) is essential in monitoring and controlling transient stability of a power system. It is well known that the covariance matrixes of process noise (Q) and measurement noise (R) have a significant impact on the Kalman filter s performance in estimating dynamic states. The conventional ad-hoc approaches for estimating the covariance matrixes are not adequate in achieving the best filtering performance. To address this problem, this paper proposes an adaptive filtering approach to adaptively estimate "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1702.00884","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":"1702.00884","created_at":"2026-05-18T00:51:29.519531+00:00"},{"alias_kind":"arxiv_version","alias_value":"1702.00884v1","created_at":"2026-05-18T00:51:29.519531+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1702.00884","created_at":"2026-05-18T00:51:29.519531+00:00"},{"alias_kind":"pith_short_12","alias_value":"WPBGXAL5JYZT","created_at":"2026-05-18T12:31:53.515858+00:00"},{"alias_kind":"pith_short_16","alias_value":"WPBGXAL5JYZTPUQS","created_at":"2026-05-18T12:31:53.515858+00:00"},{"alias_kind":"pith_short_8","alias_value":"WPBGXAL5","created_at":"2026-05-18T12:31:53.515858+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.28107","citing_title":"Neural Aided Kalman Filtering for UAV State Estimation in Degraded Sensing Environments","ref_index":28,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/WPBGXAL5JYZTPUQSI3OUOCO2U3","json":"https://pith.science/pith/WPBGXAL5JYZTPUQSI3OUOCO2U3.json","graph_json":"https://pith.science/api/pith-number/WPBGXAL5JYZTPUQSI3OUOCO2U3/graph.json","events_json":"https://pith.science/api/pith-number/WPBGXAL5JYZTPUQSI3OUOCO2U3/events.json","paper":"https://pith.science/paper/WPBGXAL5"},"agent_actions":{"view_html":"https://pith.science/pith/WPBGXAL5JYZTPUQSI3OUOCO2U3","download_json":"https://pith.science/pith/WPBGXAL5JYZTPUQSI3OUOCO2U3.json","view_paper":"https://pith.science/paper/WPBGXAL5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1702.00884&json=true","fetch_graph":"https://pith.science/api/pith-number/WPBGXAL5JYZTPUQSI3OUOCO2U3/graph.json","fetch_events":"https://pith.science/api/pith-number/WPBGXAL5JYZTPUQSI3OUOCO2U3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/WPBGXAL5JYZTPUQSI3OUOCO2U3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/WPBGXAL5JYZTPUQSI3OUOCO2U3/action/storage_attestation","attest_author":"https://pith.science/pith/WPBGXAL5JYZTPUQSI3OUOCO2U3/action/author_attestation","sign_citation":"https://pith.science/pith/WPBGXAL5JYZTPUQSI3OUOCO2U3/action/citation_signature","submit_replication":"https://pith.science/pith/WPBGXAL5JYZTPUQSI3OUOCO2U3/action/replication_record"}},"created_at":"2026-05-18T00:51:29.519531+00:00","updated_at":"2026-05-18T00:51:29.519531+00:00"}