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Kalman filtering withstate constraints: asurvey of linear andnonlinear algorithms D. Simon2010 год

Kalman filtering with state constraints: a survey of linear and nonlinear algorithms
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Аннотация: The Kalman filter is the minimum-variance state estimator for linear dynamic systems with Gaussian noise. Even if the noise is non-Gaussian, the Kalman filter is the best linear estimator. For nonlinear systems it is not possible, in general, to derive the optimal state estimator in closed form, but various modifications of the Kalman filter can be used to estimate the state. These modifications include the extended Kalman filter, the unscented Kalman filter, and the particle filter. Although the Kalman filter and its modifications are powerful tools for state estimation, we might have information about a system that the Kalman filter does not incorporate. For example, we may know that the states satisfy equality or inequality constraints. In this case we can modify the Kalman filter to exploit this additional information and get better filtering performance than the Kalman filter provides. This paper provides an overview of various ways to incorporate state constraints in the Kalman filter and its nonlinear modifications. If both the system and state constraints are linear, then all of these different approaches result in the same state estimate, which is the optimal constrained linear state estimate. If either the system or constraints are nonlinear, then constrained filtering is, in general, not optimal, and different approaches give different results.
Год издания: 2010
Авторы: D. Simon
Издательство: Institution of Engineering and Technology
Источник: IET Control Theory and Applications
Ключевые слова: Target Tracking and Data Fusion in Sensor Networks, Inertial Sensor and Navigation, Fault Detection and Control Systems
Другие ссылки: IET Control Theory and Applications (HTML)
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EngagedScholarship - Cleveland State University Scholarly & Creative Work from CSU (Cleveland State University) (HTML)