Abstract
Data fusion for nonlinear systems is one of the challenging topics in state estimation and target tracking recently. We study decentralized cubature Kalman fusion in this paper. Cubature Kalman filter (CKF) is a more effective method than the conventional nonlinear filters, such as extended Kalman filter (EKF) and unscented Kalman filter (UKF). For most of the practical cases, there are correlative between process and measurement noises (Correlation I) and among measurement noises (Correlation II). So, it is more attractive to design fusion algorithms based on the CKF for the systems with complex correlated noises. Firstly, a cubature Kalman filter with correlation I (CKF-CN) is derived. Secondly, by introducing the EKF with correlated noises (EKF-CN) and its information filter EIF-CN, the CKF-CN is embedded in the EIF-CN framework to get a cubature information filter with correlated noises (CIF-CN). Consequently, a square-root cubature Kalman filter with noise correlation I (SCKF-CN) and the associated information filter SCIF-CN are presented to improve computational performance. Finally, based on the proposed SCIF-CN and matrix diagonalization, a decentralized nonlinear fusion algorithm is proposed for the multisensor system with Correlation I and Correlation II. Simulation examples are demonstrated to validate the proposed filters and fusion algorithms.
Published Version
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