Frontiers of Information Technology & Electronic Engineering
>> 2022,
Volume 23,
Issue 3
doi:
10.1631/FITEE.2000642
A novel multiple-outlier-robust Kalman filter
哈尔滨工程大学智能科学与工程学院，中国哈尔滨市，150001
Received: 2020-11-17
Accepted: 2022-03-22
Available online: 2022-03-22
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Abstract
This paper presents a novel multiple-outlier-robust Kalman filter (MORKF) for linear stochastic discrete-time systems. A new is first proposed to evaluate the similarity between two random vectors from dimension to dimension. Then, the proposed MORKF is derived via maximizing a based cost function. The MORKF guarantees the convergence of iterations in mild conditions, and the boundedness of the approximation errors is analyzed theoretically. The selection strategy for the similarity function and comparisons with existing robust methods are presented. Simulation results show the advantages of the proposed filter.
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