{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,21]],"date-time":"2026-03-21T21:50:50Z","timestamp":1774129850759,"version":"3.50.1"},"reference-count":31,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2018,7,24]],"date-time":"2018-07-24T00:00:00Z","timestamp":1532390400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61673067"],"award-info":[{"award-number":["61673067"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In most practical applications, the tracking process needs to update the data constantly. However, outliers may occur frequently in the process of sensors\u2019 data collection and sending, which affects the performance of the system state estimate. In order to suppress the impact of observation outliers in the process of target tracking, a novel filtering algorithm, namely a robust adaptive unscented Kalman filter, is proposed. The cost function of the proposed filtering algorithm is derived based on fading factor and maximum correntropy criterion. In this paper, the derivations of cost function and fading factor are given in detail, which enables the proposed algorithm to be robust. Finally, the simulation results show that the presented algorithm has good performance, and it improves the robustness of a general unscented Kalman filter and solves the problem of outliers in system.<\/jats:p>","DOI":"10.3390\/s18082406","type":"journal-article","created":{"date-parts":[[2018,7,24]],"date-time":"2018-07-24T11:51:38Z","timestamp":1532433098000},"page":"2406","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Adaptive Robust Unscented Kalman Filter via Fading Factor and Maximum Correntropy Criterion"],"prefix":"10.3390","volume":"18","author":[{"given":"Zhihong","family":"Deng","sequence":"first","affiliation":[{"name":"The School of Automation, Beijing Institute of Technology, Beijing 100081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lijian","family":"Yin","sequence":"additional","affiliation":[{"name":"The School of Automation, Beijing Institute of Technology, Beijing 100081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Baoyu","family":"Huo","sequence":"additional","affiliation":[{"name":"The Second Construction Limited Company of China Construction Eighth Engineering Division, Jinan 250014, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuanqing","family":"Xia","sequence":"additional","affiliation":[{"name":"The School of Automation, Beijing Institute of Technology, Beijing 100081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,7,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"8669","DOI":"10.1109\/TVT.2015.2508456","article-title":"Consensus-Based Distributed Mixture Kalman Filter for Maneuvering Target Tracking in Wireless Sensor Networks","volume":"65","author":"Yu","year":"2016","journal-title":"IEEE Trans. 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