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Statistical linearization of nonlinear functions is used in sigma point filters, that is, unscented Kalman filter (UKF), cubature Kalman filter (CKF), and central difference Kalman filter (CDKF). Stability of the proposed algorithm is also analysed with the help of linearization operation and some conservative assumptions. Two typical target tracking examples are used to demonstrate the effectiveness of the proposed algorithms. Simulation results show that the proposed algorithms are more stable than existing algorithms, and among our proposed algorithms, CKF- and CDKF-based algorithms are more accurate and stable than the UKF-based one.<\/jats:p>","DOI":"10.1177\/0142331217691758","type":"journal-article","created":{"date-parts":[[2018,5,21]],"date-time":"2018-05-21T11:58:19Z","timestamp":1526903899000},"page":"2517-2525","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":20,"title":["Hybrid consensus sigma point approximation nonlinear filter using statistical linearization"],"prefix":"10.1177","volume":"40","author":[{"given":"Guoqing","family":"Wang","sequence":"first","affiliation":[{"name":"College of Automation, Harbin Engineering University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ning","family":"Li","sequence":"additional","affiliation":[{"name":"College of Automation, Harbin Engineering University, 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