{"as_of":"2026-08-09T03:17:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:d74b4c88423da7e132ed05159bf25744204f281bff4f84eaf8fb66ef70efdabe","coverage":[{"denominator":28,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":28,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T21:28:52.743398Z","state":"measured"},{"denominator":28,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":28,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2507.00272/citation-record","integrity":"/paper/2507.00272/integrity","json":"/paper/2507.00272/citation-record.json","paper":"/paper/2507.00272"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:28:58.858952Z","title":"A new approach to linear filtering and prediction problems","venue":null,"work_id":"5440f054-1775-4917-9968-1758576efdbe","year":1960},"citing_paper":{"arxiv_id":"2507.00272","last_updated":"2025-06-30T21:30:58Z","snapshot_observed_at":"2026-08-06T21:17:52.850732Z","submitted_at":"2025-06-30T21:30:58Z","title":"Iteratively Saturated Kalman Filtering","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T21:28:49.037743Z"},"links":{"citing_paper":"/paper/2507.00272"},"observation_digest":"sha256:3637e0263c202a570d0ce46ec9c85c3dbd20e436c2f38d347e198efd64ffd9cf","observation_id":"476de3ff-9fb8-4cef-9794-ec00adb8b178","resolution":{"observed_at":"2026-08-06T21:28:58.998319Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:28:58.641187Z","title":"Efficient decoding with steady-state Kalman filter in neural interface systems","venue":null,"work_id":"fc88b665-99b9-48c4-86d1-a0c1a4343f77","year":2010},"citing_paper":{"arxiv_id":"2507.00272","last_updated":"2025-06-30T21:30:58Z","snapshot_observed_at":"2026-08-06T21:17:52.850732Z","submitted_at":"2025-06-30T21:30:58Z","title":"Iteratively Saturated Kalman Filtering","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T21:28:49.142258Z"},"links":{"citing_paper":"/paper/2507.00272"},"observation_digest":"sha256:f62142e1c05e7f89844918b7c033f69a9ba35ee8270d5dbfe020d43ed4bca7b5","observation_id":"7dd342cf-7b48-45dd-9a5d-888267b71da7","resolution":{"observed_at":"2026-08-06T21:28:58.744855Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:28:58.376051Z","title":"Shocks and frictions in US business cycles: A Bayesian DSGE approach","venue":null,"work_id":"e6c56cc9-0ec7-4049-8204-f8205dc952ae","year":2007},"citing_paper":{"arxiv_id":"2507.00272","last_updated":"2025-06-30T21:30:58Z","snapshot_observed_at":"2026-08-06T21:17:52.850732Z","submitted_at":"2025-06-30T21:30:58Z","title":"Iteratively Saturated Kalman Filtering","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T21:28:49.291308Z"},"links":{"citing_paper":"/paper/2507.00272"},"observation_digest":"sha256:8361e57199015b3b430b996446d210b4c0f08c576110d6587e586ee99dd4add8","observation_id":"1d021101-1034-4379-bc6a-47386cfaa513","resolution":{"observed_at":"2026-08-06T21:28:58.504540Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:28:58.103968Z","title":"Huber,An augmented steady-state Kalman filter to evaluate the likelihood of linear and time: Invariant state-space models, tech","venue":null,"work_id":"76a422df-44e5-487e-a185-37c5ac29fc3a","year":2022},"citing_paper":{"arxiv_id":"2507.00272","last_updated":"2025-06-30T21:30:58Z","snapshot_observed_at":"2026-08-06T21:17:52.850732Z","submitted_at":"2025-06-30T21:30:58Z","title":"Iteratively Saturated Kalman Filtering","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T21:28:49.416927Z"},"links":{"citing_paper":"/paper/2507.00272"},"observation_digest":"sha256:74bba84878afab6b87a5239b42cb1590a6ed69d8fc3a29b0ea12983095423872","observation_id":"7c02f50f-fe1b-48b2-8975-58bfbcd383cf","resolution":{"observed_at":"2026-08-06T21:28:58.231879Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:28:57.889285Z","title":"Robust Bayesian estimation for the linear model and robustifying the Kalman filter","venue":null,"work_id":"4f2701dc-a757-419f-9ffc-80276db099bd","year":1977},"citing_paper":{"arxiv_id":"2507.00272","last_updated":"2025-06-30T21:30:58Z","snapshot_observed_at":"2026-08-06T21:17:52.850732Z","submitted_at":"2025-06-30T21:30:58Z","title":"Iteratively Saturated Kalman Filtering","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T21:28:49.557138Z"},"links":{"citing_paper":"/paper/2507.00272"},"observation_digest":"sha256:c8c86dc580ee492acf4bf58a7a908c2566123c783373b97572af7f7b83c18e31","observation_id":"6a586784-6079-47ee-9b65-2cd74e238f49","resolution":{"observed_at":"2026-08-06T21:28:57.994872Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"biblio/4252678","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:28:53.144043Z","title":"Davidon,Variable metric method for minimization, tech","venue":null,"work_id":"0c8108c1-e58a-4827-937b-46c8ecd386b7","year":1959},"citing_paper":{"arxiv_id":"2507.00272","last_updated":"2025-06-30T21:30:58Z","snapshot_observed_at":"2026-08-06T21:17:52.850732Z","submitted_at":"2025-06-30T21:30:58Z","title":"Iteratively Saturated Kalman Filtering","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T21:28:49.724261Z"},"links":{"citing_paper":"/paper/2507.00272"},"observation_digest":"sha256:b2392c47b9e0fdf7ce922a4c5444bc4bc9fa34e85bfbe591b790065f97c7f043","observation_id":"2cbe8dbc-8c80-49e1-b3b0-2fcd3edb1ddb","resolution":{"observed_at":"2026-08-06T21:28:53.247674Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:28:49.846770Z","title":"A Rapidly Convergent Descent Method for Minimization","venue":null,"work_id":null,"year":1963},"citing_paper":{"arxiv_id":"2507.00272","last_updated":"2025-06-30T21:30:58Z","snapshot_observed_at":"2026-08-06T21:17:52.850732Z","submitted_at":"2025-06-30T21:30:58Z","title":"Iteratively Saturated Kalman Filtering","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T21:28:49.846770Z"},"links":{"citing_paper":"/paper/2507.00272"},"observation_digest":"sha256:595ea1efcdb77c113f0b814892b6d70642a83878dc7b7fe31ae96f57d87bb7fc","observation_id":"2fe082e1-c75d-470a-9b98-36a32a506520","resolution":{"observed_at":"2026-08-06T21:28:49.846770Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:28:57.674453Z","title":"Robust estimation with unknown noise statistics","venue":null,"work_id":"2fc0dd4f-7273-456e-9818-823a16ed8138","year":1999},"citing_paper":{"arxiv_id":"2507.00272","last_updated":"2025-06-30T21:30:58Z","snapshot_observed_at":"2026-08-06T21:17:52.850732Z","submitted_at":"2025-06-30T21:30:58Z","title":"Iteratively Saturated Kalman Filtering","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T21:28:50.011422Z"},"links":{"citing_paper":"/paper/2507.00272"},"observation_digest":"sha256:ecbb2c7d3e85a9dc6bf9dc1358ed64cfc358d9b2b46c066d8c8e1079333057a6","observation_id":"4de9f4d5-7aea-46d2-8e90-05a61444a8da","resolution":{"observed_at":"2026-08-06T21:28:57.780386Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:28:57.496511Z","title":"Robust Kalman filtering based on Mahalanobis distance as outlier judging criterion","venue":null,"work_id":"556276d3-09e8-4bc0-b6d1-b92a324d7dcd","year":2014},"citing_paper":{"arxiv_id":"2507.00272","last_updated":"2025-06-30T21:30:58Z","snapshot_observed_at":"2026-08-06T21:17:52.850732Z","submitted_at":"2025-06-30T21:30:58Z","title":"Iteratively Saturated Kalman Filtering","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T21:28:50.129696Z"},"links":{"citing_paper":"/paper/2507.00272"},"observation_digest":"sha256:6f81e0baf5ab27f2ed57d65efdb72029dc3f62b5f6bda515825a6e7aef0dcbce","observation_id":"0b6b8e9d-171f-4d42-8a46-dd567925a9a9","resolution":{"observed_at":"2026-08-06T21:28:57.569316Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2405.05646","last_updated":"2024-05-28T07:03:49Z","snapshot_observed_at":"2026-07-06T18:12:01.364458Z","submitted_at":"2024-05-09T09:40:56Z","title":"Outlier-robust Kalman Filtering through Generalised Bayes","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.05646","snapshot_observed_at":"2026-08-06T21:28:50.237241Z","title":"Outlier-robust Kalman Filtering through Generalised Bayes","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.00272","last_updated":"2025-06-30T21:30:58Z","snapshot_observed_at":"2026-08-06T21:17:52.850732Z","submitted_at":"2025-06-30T21:30:58Z","title":"Iteratively Saturated Kalman Filtering","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T21:28:50.237241Z"},"links":{"cited_paper":"/paper/2405.05646","citing_paper":"/paper/2507.00272"},"observation_digest":"sha256:393d7df14c2922673a9431ea78ea3bbc4b01ec9637217a35f07240c954351e7b","observation_id":"18b14a92-f474-44bc-b321-bcbe57a4bf17","resolution":{"observed_at":"2026-08-06T21:28:50.237241Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:28:57.264281Z","title":"A Kalman filter for robust outlier detection","venue":null,"work_id":"97677fc4-3c57-48b4-a456-041de3c955dc","year":2007},"citing_paper":{"arxiv_id":"2507.00272","last_updated":"2025-06-30T21:30:58Z","snapshot_observed_at":"2026-08-06T21:17:52.850732Z","submitted_at":"2025-06-30T21:30:58Z","title":"Iteratively Saturated Kalman Filtering","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T21:28:50.406586Z"},"links":{"citing_paper":"/paper/2507.00272"},"observation_digest":"sha256:e13501c75cd927f5d8d29beb1da9cdfac62bdf2466b2dc736f4aecd19f868426","observation_id":"dd7ab3dc-3470-4754-a3b8-dfbe9dfa1067","resolution":{"observed_at":"2026-08-06T21:28:57.398001Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:28:57.047398Z","title":"An outlier-robust Kalman filter","venue":null,"work_id":"35cdc084-f33c-40e9-9e7e-f0f1c9f2eaf1","year":2011},"citing_paper":{"arxiv_id":"2507.00272","last_updated":"2025-06-30T21:30:58Z","snapshot_observed_at":"2026-08-06T21:17:52.850732Z","submitted_at":"2025-06-30T21:30:58Z","title":"Iteratively Saturated Kalman Filtering","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T21:28:50.522047Z"},"links":{"citing_paper":"/paper/2507.00272"},"observation_digest":"sha256:a236ddb605851fd1658f31a7dabd4fdaaeb6213cb48a94bbc9f261b9b0847696","observation_id":"1189e915-f268-4289-b4bc-e293cebbfec7","resolution":{"observed_at":"2026-08-06T21:28:57.122062Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:28:50.637264Z","title":"Robust Estimation of a Location Parameter","venue":null,"work_id":null,"year":1964},"citing_paper":{"arxiv_id":"2507.00272","last_updated":"2025-06-30T21:30:58Z","snapshot_observed_at":"2026-08-06T21:17:52.850732Z","submitted_at":"2025-06-30T21:30:58Z","title":"Iteratively Saturated Kalman Filtering","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T21:28:50.637264Z"},"links":{"citing_paper":"/paper/2507.00272"},"observation_digest":"sha256:4c053f80548c0d4753b3cf84ae14f7775187f6d991f71c5c57868dd0d12e1c62","observation_id":"a1226d0e-d36e-4f8a-ba91-9a99670ca18e","resolution":{"observed_at":"2026-08-06T21:28:50.637264Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:28:56.849207Z","title":"Robust Kalman filter and its application in time series analysis","venue":null,"work_id":"705547b0-345c-45b6-b585-5cdb47b47df9","year":1991},"citing_paper":{"arxiv_id":"2507.00272","last_updated":"2025-06-30T21:30:58Z","snapshot_observed_at":"2026-08-06T21:17:52.850732Z","submitted_at":"2025-06-30T21:30:58Z","title":"Iteratively Saturated Kalman Filtering","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T21:28:50.778282Z"},"links":{"citing_paper":"/paper/2507.00272"},"observation_digest":"sha256:955992e3f2594064daf1923c7b7a2e9998609417cff925b27562f8b3468c47e5","observation_id":"0e5253b3-57ab-4b67-aceb-35c7c57f1189","resolution":{"observed_at":"2026-08-06T21:28:56.972847Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:28:56.568706Z","title":"On robust Kalman filtering","venue":null,"work_id":"fe043a5b-a848-4926-adde-4fc94859b40d","year":1992},"citing_paper":{"arxiv_id":"2507.00272","last_updated":"2025-06-30T21:30:58Z","snapshot_observed_at":"2026-08-06T21:17:52.850732Z","submitted_at":"2025-06-30T21:30:58Z","title":"Iteratively Saturated Kalman Filtering","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T21:28:50.954096Z"},"links":{"citing_paper":"/paper/2507.00272"},"observation_digest":"sha256:c13584636f1056c049ec59d0c100042ac468014e78ab6279ca3c0658cb0dcb00","observation_id":"c096d396-2388-4f7c-b330-23a680029d0a","resolution":{"observed_at":"2026-08-06T21:28:56.695455Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:28:56.319345Z","title":"CVXGEN: A code generator for embedded convex optimization","venue":null,"work_id":"d4acb28b-d919-43d2-b14a-97814368b1e2","year":2012},"citing_paper":{"arxiv_id":"2507.00272","last_updated":"2025-06-30T21:30:58Z","snapshot_observed_at":"2026-08-06T21:17:52.850732Z","submitted_at":"2025-06-30T21:30:58Z","title":"Iteratively Saturated Kalman Filtering","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T21:28:51.081761Z"},"links":{"citing_paper":"/paper/2507.00272"},"observation_digest":"sha256:ffef69a7767defdc0180034dbb8a0b8c3d23223bc5f283baab63fc62abe3beed","observation_id":"430478d7-43d2-42d4-8fb1-926da8954645","resolution":{"observed_at":"2026-08-06T21:28:56.401041Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:28:56.114230Z","title":"Random sample consensus (RANSAC)","venue":null,"work_id":"2c1c2a59-dfff-43ee-a678-a399956e7a71","year":1981},"citing_paper":{"arxiv_id":"2507.00272","last_updated":"2025-06-30T21:30:58Z","snapshot_observed_at":"2026-08-06T21:17:52.850732Z","submitted_at":"2025-06-30T21:30:58Z","title":"Iteratively Saturated Kalman Filtering","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T21:28:51.219716Z"},"links":{"citing_paper":"/paper/2507.00272"},"observation_digest":"sha256:21475a48b40692e200c10e2bbbdae86cf1a982e7210bb8e44bdbab82d8262bdc","observation_id":"5ab517f6-3e32-4541-9dc4-cb76b3189a67","resolution":{"observed_at":"2026-08-06T21:28:56.203766Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:28:55.911471Z","title":"KALMANSAC: Robust filtering by consensus","venue":null,"work_id":"9b1cd83d-66ec-4ec2-85f4-affd50836923","year":2005},"citing_paper":{"arxiv_id":"2507.00272","last_updated":"2025-06-30T21:30:58Z","snapshot_observed_at":"2026-08-06T21:17:52.850732Z","submitted_at":"2025-06-30T21:30:58Z","title":"Iteratively Saturated Kalman Filtering","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T21:28:51.373996Z"},"links":{"citing_paper":"/paper/2507.00272"},"observation_digest":"sha256:ac77c7c55e27be467feb4ebb7df35dc18f3271deb95dd7576d3799c47d09aca8","observation_id":"0c9727fa-dce0-4d8f-9589-066c8573c390","resolution":{"observed_at":"2026-08-06T21:28:55.984837Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:28:55.688810Z","title":"Recursive outlier-robust filtering and smoothing for nonlinear systems using the multivariate Student-t distribution","venue":null,"work_id":"74a16572-f05a-431b-a4dd-f7a9d6c65cef","year":2012},"citing_paper":{"arxiv_id":"2507.00272","last_updated":"2025-06-30T21:30:58Z","snapshot_observed_at":"2026-08-06T21:17:52.850732Z","submitted_at":"2025-06-30T21:30:58Z","title":"Iteratively Saturated Kalman Filtering","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T21:28:51.476436Z"},"links":{"citing_paper":"/paper/2507.00272"},"observation_digest":"sha256:0808d93baf42232c99b4b1940f69f5ee5b8b42368028691c563396d36fd3b657","observation_id":"2edebd2a-d150-4e82-9ebb-0a446987f215","resolution":{"observed_at":"2026-08-06T21:28:55.807271Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:28:55.422995Z","title":"Nonlinear regression Huber–Kalman filtering and fixed-interval smoothing","venue":null,"work_id":"476f16cb-8b71-4780-a375-d448c17028ee","year":2015},"citing_paper":{"arxiv_id":"2507.00272","last_updated":"2025-06-30T21:30:58Z","snapshot_observed_at":"2026-08-06T21:17:52.850732Z","submitted_at":"2025-06-30T21:30:58Z","title":"Iteratively Saturated Kalman Filtering","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T21:28:51.598235Z"},"links":{"citing_paper":"/paper/2507.00272"},"observation_digest":"sha256:84e690df9971b1e5450c835a49cd4bef26229648ded2edf7a4ee0c591b3822d6","observation_id":"97949974-218e-4217-bc8c-ade6b8fb96ba","resolution":{"observed_at":"2026-08-06T21:28:55.551671Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:28:55.130225Z","title":"Robustifying the Kalman filter against measurement outliers: An innovation saturation mechanism","venue":null,"work_id":"f7140959-acdc-4d8b-b568-55a9732442dc","year":2018},"citing_paper":{"arxiv_id":"2507.00272","last_updated":"2025-06-30T21:30:58Z","snapshot_observed_at":"2026-08-06T21:17:52.850732Z","submitted_at":"2025-06-30T21:30:58Z","title":"Iteratively Saturated Kalman Filtering","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-06T21:28:51.766646Z"},"links":{"citing_paper":"/paper/2507.00272"},"observation_digest":"sha256:cb84ae878c85f0b783b06b307f687d5f567920719bce94acea578240f5aece8d","observation_id":"74227318-ffa3-4bf6-bbb8-09a84bfd1bcb","resolution":{"observed_at":"2026-08-06T21:28:55.230807Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:28:54.844062Z","title":"Generalised Bayesian filtering via sequential Monte Carlo","venue":null,"work_id":"c3e1946f-3e07-4c60-b3da-c63b0e63f260","year":2020},"citing_paper":{"arxiv_id":"2507.00272","last_updated":"2025-06-30T21:30:58Z","snapshot_observed_at":"2026-08-06T21:17:52.850732Z","submitted_at":"2025-06-30T21:30:58Z","title":"Iteratively Saturated Kalman Filtering","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-06T21:28:51.911714Z"},"links":{"citing_paper":"/paper/2507.00272"},"observation_digest":"sha256:2735bf30967b4422a7a7ced0bfbb2ccd1926b84919a38ddf82c8892743bc7d79","observation_id":"747dc199-e9d5-4b30-ad98-fba314b74775","resolution":{"observed_at":"2026-08-06T21:28:54.995863Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:28:54.557272Z","title":"Simon, John Wiley & Sons, 2006","venue":null,"work_id":"4c0618f7-bd1b-4dbc-ba37-7b43ebee94e1","year":2006},"citing_paper":{"arxiv_id":"2507.00272","last_updated":"2025-06-30T21:30:58Z","snapshot_observed_at":"2026-08-06T21:17:52.850732Z","submitted_at":"2025-06-30T21:30:58Z","title":"Iteratively Saturated Kalman Filtering","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-06T21:28:52.046008Z"},"links":{"citing_paper":"/paper/2507.00272"},"observation_digest":"sha256:bd1d25ca77167f65ddc008f8931312474dc59987dae64cbf2dafcb55bc86fcbe","observation_id":"a09ca1af-48df-4296-98fd-c7780d08522d","resolution":{"observed_at":"2026-08-06T21:28:54.710522Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:28:54.249121Z","title":"Polyak, Optimization Software, 1987","venue":null,"work_id":"3da41b64-32db-48af-93ba-1c83006222be","year":1987},"citing_paper":{"arxiv_id":"2507.00272","last_updated":"2025-06-30T21:30:58Z","snapshot_observed_at":"2026-08-06T21:17:52.850732Z","submitted_at":"2025-06-30T21:30:58Z","title":"Iteratively Saturated Kalman Filtering","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-06T21:28:52.164049Z"},"links":{"citing_paper":"/paper/2507.00272"},"observation_digest":"sha256:ab6802f7aa9ef45c204cad2bb5f3f878406360b94aef8752a8fb6b110b96c481","observation_id":"c694c7b6-a74e-4914-8144-a08a501922f8","resolution":{"observed_at":"2026-08-06T21:28:54.379692Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2405.12762","last_updated":"2024-05-21T13:15:19Z","snapshot_observed_at":"2026-08-08T05:36:37.901792Z","submitted_at":"2024-05-21T13:15:19Z","title":"Clarabel: An interior-point solver for conic programs with quadratic objectives","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.12762","snapshot_observed_at":"2026-08-06T21:28:52.303119Z","title":"Goulart and Y","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.00272","last_updated":"2025-06-30T21:30:58Z","snapshot_observed_at":"2026-08-06T21:17:52.850732Z","submitted_at":"2025-06-30T21:30:58Z","title":"Iteratively Saturated Kalman Filtering","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-06T21:28:52.303119Z"},"links":{"cited_paper":"/paper/2405.12762","citing_paper":"/paper/2507.00272"},"observation_digest":"sha256:ba9b4f8b2a255593ae3e38d5b2da97d512509baedd1d57332a75e8665f8b43a5","observation_id":"1b815ff9-7bfc-43e4-8da2-e43b4d33fddc","resolution":{"observed_at":"2026-08-06T21:28:52.303119Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:28:53.964537Z","title":"Bequette, Prentice Hall Englewood Cliffs, NJ, 1998","venue":null,"work_id":"b57ecbf3-a75c-4dcb-a4db-a934608d9181","year":1998},"citing_paper":{"arxiv_id":"2507.00272","last_updated":"2025-06-30T21:30:58Z","snapshot_observed_at":"2026-08-06T21:17:52.850732Z","submitted_at":"2025-06-30T21:30:58Z","title":"Iteratively Saturated Kalman Filtering","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-06T21:28:52.454657Z"},"links":{"citing_paper":"/paper/2507.00272"},"observation_digest":"sha256:e174f168422420c2c8f5cebae3808d4eca48c96072da39880d8ae2c29ec35a8c","observation_id":"91f3714a-c785-4d66-89d8-5a1d5329bd64","resolution":{"observed_at":"2026-08-06T21:28:54.076863Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:28:53.670714Z","title":"Seborg, T","venue":null,"work_id":"103aa4e5-1efc-4c0d-871e-ccb004454265","year":2016},"citing_paper":{"arxiv_id":"2507.00272","last_updated":"2025-06-30T21:30:58Z","snapshot_observed_at":"2026-08-06T21:17:52.850732Z","submitted_at":"2025-06-30T21:30:58Z","title":"Iteratively Saturated Kalman Filtering","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-06T21:28:52.613727Z"},"links":{"citing_paper":"/paper/2507.00272"},"observation_digest":"sha256:c633a9dbadc69b027dc59c2a5b3c0eb32103446f66534e6e1cb878c169ae2910","observation_id":"8a945b63-52f8-4f17-b057-da07125eaa55","resolution":{"observed_at":"2026-08-06T21:28:53.789241Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:28:53.388110Z","title":"About the Authors Alan Yang is a Ph.D candidate in Electri- cal Engineering at Stanford University","venue":null,"work_id":"ca62fadb-8c8a-4b95-9197-04717907bc5d","year":2025},"citing_paper":{"arxiv_id":"2507.00272","last_updated":"2025-06-30T21:30:58Z","snapshot_observed_at":"2026-08-06T21:17:52.850732Z","submitted_at":"2025-06-30T21:30:58Z","title":"Iteratively Saturated Kalman Filtering","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-06T21:28:52.743398Z"},"links":{"citing_paper":"/paper/2507.00272"},"observation_digest":"sha256:29d606985f13420d03a1e7cdedf5da79f4fbdc60b42e465a13550396ee02faac","observation_id":"fc4c1c04-2cf5-484c-a5b5-8f99ef39a179","resolution":{"observed_at":"2026-08-06T21:28:53.513690Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2507.00272","last_updated":"2025-06-30T21:30:58Z","latest_version":1,"primary_category":"eess.SY","snapshot_observed_at":"2026-08-06T21:17:52.850732Z","submitted_at":"2025-06-30T21:30:58Z","title":"Iteratively Saturated Kalman Filtering"},"reference_resolution":{"displayed":28,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":4,"verified_exact":1,"verified_fuzzy":23},"total_outbound_references":28},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 28 of 28 outbound references and 0 inbound Pith citation observations for arXiv:2507.00272."}