Open AccessTobit Kalman filter with time‐correlated multiplicative measurement noise
Author(s) -
Li Wenling,
Jia Yingmin,
Du Junping
Publication year - 2017
Publication title -
iet control theory and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.059
H-Index - 108
eISSN - 1751-8652
pISSN - 1751-8644
DOI - 10.1049/iet-cta.2016.0624
Subject(s) - control theory (sociology) , kalman filter , invariant extended kalman filter , multiplicative noise , extended kalman filter , mathematics , fast kalman filter , covariance intersection , alpha beta filter , ensemble kalman filter , noise (video) , filter (signal processing) , computer science , multiplicative function , algorithm , statistics , artificial intelligence , telecommunications , mathematical analysis , control (management) , signal transfer function , transmission (telecommunications) , analog signal , image (mathematics) , computer vision , moving horizon estimation
Kalman filters for discrete‐time linear systems with censored measurements have been developed, of which the Tobit Kalman filter has been shown an effective candidate. In this study, the authors expand the Tobit Kalman filter to discrete‐time linear systems with time‐correlated multiplicative measurement noise. By introducing several new terms including the estimates for the products of multiplicative measurement noise and the state as well as their error covariance matrices, the proposed filter can be implemented in a recursive manner. A numerical example involving radar tracking is provided to show the effectiveness of the proposed filter.