State estimation based on improved cubature Kalman filter algorithm. Issue 5 (1st July 2020)

• Record Type: Journal Article
• Authors: Zhu, Jun; Liu, Bingchen; Wang, Haixing; Li, Zihao; Zhang, Zhe
• Is Part Of: IET science, measurement & technology. Volume 14:Issue 5(2020)
• Page Start: 536
• Page End: 542
• Publication Date: 2020-07-01
• Subjects: adaptive Kalman filters -- covariance matrices -- state estimation -- permanent magnet motors -- signal denoising -- statistical analysis; cubature Kalman filter algorithm -- filter divergence -- square root filter algorithm -- Sage–Husa maximum -- posterior noise estimator -- filtering process -- multidimension system -- noise covariance matrix estimator -- nonlinear system -- adaptive square root CKF state estimation -- ASRCKF state estimation -- time‐varying noise statistical characteristics -- unknown noise statistical characteristics; Measurement -- Periodicals; Electrical engineering -- Periodicals; Electronics -- Periodicals; Nanotechnology -- Periodicals; Electromagnetism -- Periodicals; Medical instruments and apparatus -- Periodicals; 621.3
• DOI: 10.1049/iet-smt.2019.0363
• Languages: English
• ISSNs: 1751-8822

Abstract

Abstract : During the recursive calculate process of the cubature Kalman filter (CKF), the covariance matrix tends to lose positive definiteness and noise statistical characteristics are inaccurate, which results in inaccurate filtering or even filter divergence. This study presents an improved algorithm based on the CKF. The algorithm combines the square root filter algorithm and the Sage–Husa maximum a posterior noise estimator, which can ensure the non‐negative determination and symmetry of the covariance matrix and has the ability to deal with unknown and time‐varying noise statistical characteristics in the filtering process adaptively. In the multi‐dimension system, the noise covariance matrix may dissatisfy non‐negative definiteness and result in filter divergence, and then the noise covariance matrix estimator is improved. The analysis is verified by state estimation example of the non‐linear system, compared with the standard CKF, the accuracy of the adaptive square root CKF (ASRCKF) state estimation is increased by 63.13, 63.88, and 42.71%, respectively. Finally, the effectiveness of the ASRCKF is verified by the state estimation of the permanent magnet linear synchronous motor.