Applications of Random Parameter Matrices Kalman Filtering in Uncertain Observation and Multi-Model Systems

2006/08/18 by Dandan Luo, Luo, Dandan, Yunmin Zhu +1
Computer Science · Engineering · #FOS: Computer and information sciences #Fault Detection and Control Systems #Inertial Sensor and Navigation #Information Theory (cs.IT) #Target Tracking and Data Fusion in Sensor Networks

paper · pdf · doi:10.48550/arxiv.cs/0608072

openalex publication_date 2006/08/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

This paper considers the Linear Minimum Variance recursive state estimation for the linear discrete time dynamic system with random state transition and measurement matrices, i.e., random parameter matrices Kalman filtering. It is shown that such system can be converted to a linear dynamic system with deterministic parameter matrices but state-dependent process and measurement noises. It is proved that under mild conditions, the recursive state estimation of this system is still of the form of a modified Kalman filtering. More importantly, this result can be applied to Kalman filtering with intermittent and partial observations as well as randomly variant dynamic systems.

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