[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-85979-en":3,"doc-seo-85979-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},85979,1374391975076,"Riley","https://ap-avatar.wpscdn.com/avatar/14000253ca4ec9f6853?x-image-process=image/resize,m_fixed,w_180,h_180&k=1783305029341752051",8,"Research & Report","Underwater Dead Reckoning with Deployable Situation-Triggered Covariance Scheduling","Underwater dead reckoning estimates vehicle position without vision and without relying on external positioning anchors. Fixed EKF noise tuning can underperform when motion transitions occur or when sensor reliability changes during turns or straight segments. The paper introduces the Situation-Triggered Calibrated Adaptive Robust Extended Kalman Filter for a BlueROV2: an onboard trigger selects pre-calibrated process-and measurement-noise matrices while keeping state, covariance history, dynamics, and models continuous. Calibration and yaw alignment are done offline using AprilTag-supervised runs and a validation set selects the scheduling policy.","arXiv :2607 . 10597v1 [ cs .RO] 12 Jul 2026  \nUnderwater Dead Reckoning with Deployable Situation-Triggered Covariance Scheduling  \nAkshay Naik, Ramavarapu S. Sreenivas, Dustin Nottage, and Ahmet Soylemezoglu  \nAbstract  \nUnderwater dead reckoning estimates vehicle position when vision is unavailable and external positioning cannot be assumed. A single set of filter parameters can work well in many situations, but fixed tuning may be poorly matched during turns, motion transitions, or periods when sensor measurements are less reliable. This paper presents the Situation-Triggered Calibrated Adaptive Robust Extended Kalman Filter for a BlueROV2 . An onboard probabilistic trigger identifies the current motion situation while one error-state filter runs continuously. When the trigger is confident, the filter changes only to the corresponding pre-calibrated process-and measurement-noise matrices; the state estimate, covariance history, dynamics, and measurement models are not reset or replaced. The trigger, noise profiles, and a one-time Doppler velocity log yaw-alignment correction are calibrated offline using sparse AprilTag-supervised pool runs. A separate validation set selects the scheduling policy, which is then fixed before heldout testing. Across four held-out pool runs, the method reduces label-weighted mean per-run translation root-mean-square error from 0.488m to 0.471m relative to the same filter backbone with one global noise profile, and every held-out run favors the scheduled method. A paired bootstrap over 10-second segments gives a candidate-minus-baseline difference of −0 .017m with a 95% confidence interval of [−0 .024 , −0 .008] m, while orientation error remains essentially unchanged. These results indicate that situation-aware covariance scheduling provides a modest but consistent vision-free dead-reckoning improvement without switching estimators or resetting the filter.  \nIndex Terms  \ndead reckoning, Doppler velocity log, situation-aware estimation, underwater navigation  \nI. INTRODUCTION  \nUnderwater dead reckoning (DR) is constrained by the absence of the measurements that many robotic systems use as anchors. Satellite navigation is unavailable below the surface. Motion-capture systems remain confined to instrumented facilities. Acoustic positioning can provide global information, but it introduces infrastructure, calibration, cost, and failure modes of its own [1], [2] . A low-cost remotely operated vehicle must therefore rely on onboard signals such as inertial measurements, Doppler velocity log (DVL) velocity when bottom lock is healthy, depth and altitude, and commanded motion. These signals support short-term localization, but their integrated errors grow over time. The central question is how much drift can be reduced using only the sensors available at runtime.  \nThat drift does not accumulate uniformly. Hovering near the bottom, executing a turn, and translating while DVL returns arestale or invalid induce different uncertainty patterns. A covariance model suited to clean straight motion can be overconfident during turns, whereas a model that remains conservative through turns can underuse reliable measurements during straight segments. This is the motivation for situation awareness in dead reckoning: rather than use one static description of sensor trust, the estimator can condition its fusion behavior on the current motion regime.  \nExisting situation-aware estimators commonly follow one of two deployment paths. Some switch between separate estimators or run banks of submodels [3]–[5], which increases runtime complexity and can make certification or failure analysis more difficult. Others adapt continuously but depend on quantities, such as estimated error or innovation statistics, that may be difficult to validate under sparse supervision [6] . This paper takes a narrower path. It keeps one continuous error-state extended Kalman filter (EKF) and changes only its fixed matrices: the process nois","cbCaitRTx3LxtZq5","https://ap.wps.com/l/cbCaitRTx3LxtZq5","pdf",2865975,4,1,16,"English","en",105,"# Introduction\n## Motivation: drift and non-uniform uncertainty\n## Limitations of existing situation-aware estimators\n## This paper’s approach: ST-CAR-EKF\n## Transfer-learning framing for calibration\n## Contributions and evaluation setup","[{\"question\":\"Why does underwater dead reckoning need situation-aware covariance scheduling?\",\"answer\":\"Drift grows over time and uncertainty patterns change with motion type. Straight motion can be modeled differently from turns or periods with stale/invalid DVL returns, so static sensor trust can become overconfident or underutilize reliable measurements.\"},{\"question\":\"How does the ST-CAR-EKF avoid switching or resetting estimators?\",\"answer\":\"It keeps one continuous error-state EKF running and switches only the fixed noise matrices Q and R selected by an onboard probabilistic trigger. The state estimate and covariance history persist across switches.\"},{\"question\":\"How are the trigger and noise profiles calibrated and validated?\",\"answer\":\"Trigger confidence gating, noise profiles, and a one-time DVL yaw-alignment correction are calibrated offline using sparse AprilTag-supervised pool runs. A separate validation set selects the scheduling policy before held-out testing.\"}]",1784207530,40,{"code":4,"msg":31,"data":32},"ok",{"site_id":25,"language":24,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"underwater-dead-reckoning-with-deployable-situation-triggered-covariance-scheduling","",{"@graph":36,"@context":85},[37,53,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":20},"https://docshare.wps.com/document/underwater-dead-reckoning-with-deployable-situation-triggered-covariance-scheduling/85979/",{"url":52,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":24,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-25","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why does underwater dead reckoning need situation-aware covariance scheduling?","Question",{"text":75,"@type":76},"Drift grows over time and uncertainty patterns change with motion type. Straight motion can be modeled differently from turns or periods with stale/invalid DVL returns, so static sensor trust can become overconfident or underutilize reliable measurements.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the ST-CAR-EKF avoid switching or resetting estimators?",{"text":80,"@type":76},"It keeps one continuous error-state EKF running and switches only the fixed noise matrices Q and R selected by an onboard probabilistic trigger. The state estimate and covariance history persist across switches.",{"name":82,"@type":73,"acceptedAnswer":83},"How are the trigger and noise profiles calibrated and validated?",{"text":84,"@type":76},"Trigger confidence gating, noise profiles, and a one-time DVL yaw-alignment correction are calibrated offline using sparse AprilTag-supervised pool runs. 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