Continuous–discrete derivative-free extended Kalman filter based on Euler–Maruyama and Itô–Taylor discretizations: Conventional and square-root implementations

平方根 卡尔曼滤波器 应用数学 数学 非线性系统 控制理论(社会学) 扩展卡尔曼滤波器 计算机科学 统计 几何学 量子力学 物理 人工智能 控制(管理)
作者
Maria V. Kulikova,Gennady Yu. Kulikov
出处
期刊:European Journal of Control [Elsevier BV]
卷期号:76: 100960-100960 被引量:4
标识
DOI:10.1016/j.ejcon.2024.100960
摘要

In this paper, we continue to study the derivative-free extended Kalman filtering (DF-EKF) framework for state estimation of continuous–discrete nonlinear stochastic systems. Having considered the Euler–Maruyama and Itô–Taylor discretization schemes for solving stochastic differential equations, we derive the related filters' moment equations based on the derivative-free EKF principal. In contrast to the recently derived MATLAB-based continuous–discrete DF-EKF techniques, the novel DF-EKF methods preserve an information about the underlying stochastic process and provide the estimation procedure for a fixed number of iterates at the propagation steps. Additionally, the DF-EKF approach is particularly effective for working with stochastic systems with highly nonlinear and/or nondifferentiable drift and observation functions, but the price to be paid is its degraded numerical stability (to roundoff) compared to the standard EKF framework. To eliminate the mentioned pitfall of the derivative-free EKF methodology, we develop the conventional algorithms together with their stable square-root implementation methods. In contrast to the published DF-EKF results, the new square-root techniques are derived within both the Cholesky and singular value decompositions. A performance of the novel filters is demonstrated on a number of numerical tests including well- and ill-conditioned scenarios.
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