{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2013:VHRB3C3JCIUVDB5UCFJGCDYTNJ","short_pith_number":"pith:VHRB3C3J","canonical_record":{"source":{"id":"1303.2395","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.DS","submitted_at":"2013-03-10T23:20:12Z","cross_cats_sorted":["cs.IT","cs.LG","math.IT","math.PR","stat.ML"],"title_canon_sha256":"2f708d27ff60032cb414e472522c2edb76824636d32d57b1457b4406e62d002d","abstract_canon_sha256":"0d6917b47530892b483a82e269a3f58e511b052621113923eacf6ca4b9dca672"},"schema_version":"1.0"},"canonical_sha256":"a9e21d8b6912295187b41152610f136a79f2ac53349503048feffafd6a5ca912","source":{"kind":"arxiv","id":"1303.2395","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1303.2395","created_at":"2026-05-18T03:31:19Z"},{"alias_kind":"arxiv_version","alias_value":"1303.2395v1","created_at":"2026-05-18T03:31:19Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1303.2395","created_at":"2026-05-18T03:31:19Z"},{"alias_kind":"pith_short_12","alias_value":"VHRB3C3JCIUV","created_at":"2026-05-18T12:28:04Z"},{"alias_kind":"pith_short_16","alias_value":"VHRB3C3JCIUVDB5U","created_at":"2026-05-18T12:28:04Z"},{"alias_kind":"pith_short_8","alias_value":"VHRB3C3J","created_at":"2026-05-18T12:28:04Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2013:VHRB3C3JCIUVDB5UCFJGCDYTNJ","target":"record","payload":{"canonical_record":{"source":{"id":"1303.2395","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.DS","submitted_at":"2013-03-10T23:20:12Z","cross_cats_sorted":["cs.IT","cs.LG","math.IT","math.PR","stat.ML"],"title_canon_sha256":"2f708d27ff60032cb414e472522c2edb76824636d32d57b1457b4406e62d002d","abstract_canon_sha256":"0d6917b47530892b483a82e269a3f58e511b052621113923eacf6ca4b9dca672"},"schema_version":"1.0"},"canonical_sha256":"a9e21d8b6912295187b41152610f136a79f2ac53349503048feffafd6a5ca912","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:31:19.158953Z","signature_b64":"QS6NYR5LQ+O0BeCkXLa0RW8zNVboZGd0RgZU5s50LWSQgUCDhmy7NCEygFk7sMJojfYakcqPn2yJBCArAhwPBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a9e21d8b6912295187b41152610f136a79f2ac53349503048feffafd6a5ca912","last_reissued_at":"2026-05-18T03:31:19.158438Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:31:19.158438Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1303.2395","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T03:31:19Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"6d7JtLczo9GwCJlL2j+Rs+J/7gAZaZ3FMmGHDX3oL10Mcdia+JekX9o6ZFgKZwYvu+XSm9Il6CQjfnPCuk8vBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-05T18:53:21.426095Z"},"content_sha256":"a325c9fc925d569089294c52e94c4e21360b71c9438a327d390c29d9dfce3efc","schema_version":"1.0","event_id":"sha256:a325c9fc925d569089294c52e94c4e21360b71c9438a327d390c29d9dfce3efc"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2013:VHRB3C3JCIUVDB5UCFJGCDYTNJ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"State estimation under non-Gaussian Levy noise: A modified Kalman filtering method","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IT","cs.LG","math.IT","math.PR","stat.ML"],"primary_cat":"math.DS","authors_text":"Jinqiao Duan, Xiangjun Wang, Xiaofan Li, Xu Sun","submitted_at":"2013-03-10T23:20:12Z","abstract_excerpt":"The Kalman filter is extensively used for state estimation for linear systems under Gaussian noise. When non-Gaussian L\\'evy noise is present, the conventional Kalman filter may fail to be effective due to the fact that the non-Gaussian L\\'evy noise may have infinite variance. A modified Kalman filter for linear systems with non-Gaussian L\\'evy noise is devised. It works effectively with reasonable computational cost. Simulation results are presented to illustrate this non-Gaussian filtering method."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1303.2395","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T03:31:19Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"BupFxopN4dxGksKJEubv9ipKBGY03jKT4ZxQYG++P7FEqJEV8ge9+MX9U2HQcLM+6htcprEMNKfXaNPBCtwWCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-05T18:53:21.426460Z"},"content_sha256":"f12efd121349abd1afcafbdfe8f4c3b912f943a6af8107e4bdb0c351a90d6794","schema_version":"1.0","event_id":"sha256:f12efd121349abd1afcafbdfe8f4c3b912f943a6af8107e4bdb0c351a90d6794"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/VHRB3C3JCIUVDB5UCFJGCDYTNJ/bundle.json","state_url":"https://pith.science/pith/VHRB3C3JCIUVDB5UCFJGCDYTNJ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/VHRB3C3JCIUVDB5UCFJGCDYTNJ/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-06-05T18:53:21Z","links":{"resolver":"https://pith.science/pith/VHRB3C3JCIUVDB5UCFJGCDYTNJ","bundle":"https://pith.science/pith/VHRB3C3JCIUVDB5UCFJGCDYTNJ/bundle.json","state":"https://pith.science/pith/VHRB3C3JCIUVDB5UCFJGCDYTNJ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/VHRB3C3JCIUVDB5UCFJGCDYTNJ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2013:VHRB3C3JCIUVDB5UCFJGCDYTNJ","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"0d6917b47530892b483a82e269a3f58e511b052621113923eacf6ca4b9dca672","cross_cats_sorted":["cs.IT","cs.LG","math.IT","math.PR","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.DS","submitted_at":"2013-03-10T23:20:12Z","title_canon_sha256":"2f708d27ff60032cb414e472522c2edb76824636d32d57b1457b4406e62d002d"},"schema_version":"1.0","source":{"id":"1303.2395","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1303.2395","created_at":"2026-05-18T03:31:19Z"},{"alias_kind":"arxiv_version","alias_value":"1303.2395v1","created_at":"2026-05-18T03:31:19Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1303.2395","created_at":"2026-05-18T03:31:19Z"},{"alias_kind":"pith_short_12","alias_value":"VHRB3C3JCIUV","created_at":"2026-05-18T12:28:04Z"},{"alias_kind":"pith_short_16","alias_value":"VHRB3C3JCIUVDB5U","created_at":"2026-05-18T12:28:04Z"},{"alias_kind":"pith_short_8","alias_value":"VHRB3C3J","created_at":"2026-05-18T12:28:04Z"}],"graph_snapshots":[{"event_id":"sha256:f12efd121349abd1afcafbdfe8f4c3b912f943a6af8107e4bdb0c351a90d6794","target":"graph","created_at":"2026-05-18T03:31:19Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"The Kalman filter is extensively used for state estimation for linear systems under Gaussian noise. When non-Gaussian L\\'evy noise is present, the conventional Kalman filter may fail to be effective due to the fact that the non-Gaussian L\\'evy noise may have infinite variance. A modified Kalman filter for linear systems with non-Gaussian L\\'evy noise is devised. It works effectively with reasonable computational cost. Simulation results are presented to illustrate this non-Gaussian filtering method.","authors_text":"Jinqiao Duan, Xiangjun Wang, Xiaofan Li, Xu Sun","cross_cats":["cs.IT","cs.LG","math.IT","math.PR","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.DS","submitted_at":"2013-03-10T23:20:12Z","title":"State estimation under non-Gaussian Levy noise: A modified Kalman filtering method"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1303.2395","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:a325c9fc925d569089294c52e94c4e21360b71c9438a327d390c29d9dfce3efc","target":"record","created_at":"2026-05-18T03:31:19Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"0d6917b47530892b483a82e269a3f58e511b052621113923eacf6ca4b9dca672","cross_cats_sorted":["cs.IT","cs.LG","math.IT","math.PR","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.DS","submitted_at":"2013-03-10T23:20:12Z","title_canon_sha256":"2f708d27ff60032cb414e472522c2edb76824636d32d57b1457b4406e62d002d"},"schema_version":"1.0","source":{"id":"1303.2395","kind":"arxiv","version":1}},"canonical_sha256":"a9e21d8b6912295187b41152610f136a79f2ac53349503048feffafd6a5ca912","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a9e21d8b6912295187b41152610f136a79f2ac53349503048feffafd6a5ca912","first_computed_at":"2026-05-18T03:31:19.158438Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T03:31:19.158438Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"QS6NYR5LQ+O0BeCkXLa0RW8zNVboZGd0RgZU5s50LWSQgUCDhmy7NCEygFk7sMJojfYakcqPn2yJBCArAhwPBg==","signature_status":"signed_v1","signed_at":"2026-05-18T03:31:19.158953Z","signed_message":"canonical_sha256_bytes"},"source_id":"1303.2395","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a325c9fc925d569089294c52e94c4e21360b71c9438a327d390c29d9dfce3efc","sha256:f12efd121349abd1afcafbdfe8f4c3b912f943a6af8107e4bdb0c351a90d6794"],"state_sha256":"8259c1d84eaf2842675036b9b53c564f00fd25bb005cee6f18f09340d786a34e"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"lkKJmeafq+kuKPLEtPPXLaN8ZrFrqQta61tFJHu4ffw/rpNB7ILcH5sX6nFg7+a4H9bHfRzC+4WdbsWWr9dnCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-05T18:53:21.428368Z","bundle_sha256":"74fea7c97c8c1db1dacb565cf5b1c768dd2a08f7b031aba5d8fa3545ef71b520"}}