{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T14:54:53Z","timestamp":1781189693835,"version":"3.54.1"},"reference-count":38,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2024,1,9]],"date-time":"2024-01-09T00:00:00Z","timestamp":1704758400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["U2066203"],"award-info":[{"award-number":["U2066203"]}]},{"name":"National Natural Science Foundation of China","award":["2020SCUNG205"],"award-info":[{"award-number":["2020SCUNG205"]}]},{"name":"Sichuan University","award":["U2066203"],"award-info":[{"award-number":["U2066203"]}]},{"name":"Sichuan University","award":["2020SCUNG205"],"award-info":[{"award-number":["2020SCUNG205"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Handling the challenge of missing measurements in nonlinear systems is a difficult problem in various scientific and engineering fields. Missing measurements, which can arise from technical faults during observation, diffusion channel shrinking, or the loss of specific metrics, can bring many challenges when estimating the state of nonlinear systems. To tackle this issue, this paper proposes a technique that utilizes a robust cubature Kalman filter (RCKF) by integrating Huber\u2019s M-estimation theory with the standard conventional cubature Kalman filter (CKF). Although a CKF is often used for solving nonlinear filtering problems, its effectiveness might be limited due to a lack of knowledge regarding the nonlinear model of the state and noise-related statistical information. In contrast, the RCKF demonstrates an ability to mitigate performance degradation and discretization issues related to track curves by leveraging covariance matrix predictions for state estimation and output control amidst dynamic disruption errors\u2014even when noise statistics deviate from prior assumptions. The performance of extended Kalman filters (EKFs), unscented Kalman filters (UKFs), CKFs, and RCKFs was compared and evaluated using two numerical examples involving the Univariate Non-stationary Growth Model (UNGM) and bearing-only tracking (BOT). The numerical experiments demonstrated that the RCKF outperformed the EKF, EnKF, and CKF in effectively handling anomaly errors. Specifically, in the UNGM example, the RCKF achieved a significantly lower ARMSE (4.83) and ANCI (3.27)\u2014similar outcomes were observed in the BOT example.<\/jats:p>","DOI":"10.3390\/s24020392","type":"journal-article","created":{"date-parts":[[2024,1,9]],"date-time":"2024-01-09T03:36:58Z","timestamp":1704771418000},"page":"392","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Robust Cubature Kalman Filter for Moving-Target Tracking with Missing Measurements"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3578-9633","authenticated-orcid":false,"given":"Samer","family":"Sahl","sequence":"first","affiliation":[{"name":"College of Mathematics, Sichuan University, Chengdu 610065, China"},{"name":"Department of Statistics, Assiut University, Assiut 71515, Egypt"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Enbin","family":"Song","sequence":"additional","affiliation":[{"name":"College of Mathematics, Sichuan University, Chengdu 610065, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dunbiao","family":"Niu","sequence":"additional","affiliation":[{"name":"College of Electronic and Information Engineering, Tongji University, Shanghai 201804, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,1,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1115\/1.3658902","article-title":"A New Approach to Liner Filtering and Prediction Problems, Transaction of ASME","volume":"83","author":"Kalman","year":"1961","journal-title":"J. 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