Covariance Intersection Fusion Robust Time-Varying Kalman Filter for Two-Sensor System with Uncertain Noise Variances

Abstract:

This paper investigates the problem of designing covariance intersection fusion robust time-varying Kalman filter for two-sensor time-varying system with uncertain noise variances. Using the minimax robust estimation principle, the local and covariance intersection (CI) fusion robust time-varying Kalman filters are presented based on the worst-case conservative system with the conservative upper bounds of noise variances. Their robustness is proved based on the proposed Lyapunov equation, and the robust accuracy of time-varying CI fuser is higher than that of each local robust time-varying Kalman filter. A two-sensor tracking system simulation verifies the robustness and robust accuracy relations.

• Periodical: Applied Mechanics and Materials (Volumes 475-476)
• Pages: 470-475
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.475-476.470
• Online since: December 2013
• Authors: Wen Juan Qi, Peng Zhang, Gui Huan Nie, Zi Li Deng*
• Keywords: Covariance Intersection Fusion, Multisensor Information Fusion, Robust Time-Varying Kalman Filter, Uncertain Noise Variance

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