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Cleveland State University

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Kalman filter

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Full-Text Articles in Engineering

Constrained Kalman Filtering Via Density Function Truncation For Turbofan Engine Health Estimation, Daniel J. Simon, Donald L. Simon Feb 2010

Constrained Kalman Filtering Via Density Function Truncation For Turbofan Engine Health Estimation, Daniel J. Simon, Donald L. Simon

Electrical and Computer Engineering Faculty Publications

Kalman filters are often used to estimate the state variables of a dynamic system. However, in the application of Kalman filters some known signal information is often either ignored or dealt with heuristically. For instance, state variable constraints (which may be based on physical considerations) are often neglected because they do not fit easily into the structure of the Kalman filter. This article develops an analytic method of incorporating state variable inequality constraints in the Kalman filter. The resultant filter truncates the probability density function (PDF) of the Kalman filter estimate at the known constraints and then computes the constrained …


Biogeography-Based Optimization, Daniel J. Simon Dec 2008

Biogeography-Based Optimization, Daniel J. Simon

Electrical and Computer Engineering Faculty Publications

Biogeography is the study of the geographical distribution of biological organisms. Mathematical equations that govern the distribution of organisms were first discovered and developed during the 1960s. The mindset of the engineer is that we can learn from nature. This motivates the application of biogeography to optimization problems. Just as the mathematics of biological genetics inspired the development of genetic algorithms (GAs), and the mathematics of biological neurons inspired the development of artificial neural networks, this paper considers the mathematics of biogeography as the basis for the development of a new field: biogeography-based optimization (BBO). We discuss natural biogeography and …


A Comparison Of Filtering Approaches For Aircraft Engine Health Estimation, Daniel J. Simon Jan 2008

A Comparison Of Filtering Approaches For Aircraft Engine Health Estimation, Daniel J. Simon

Electrical and Computer Engineering Faculty Publications

Different approaches for the estimation of the states of linear dynamic systems are commonly used, the most common being the Kalman filter. For nonlinear systems, variants of the Kalman filter are used. Some of these variants include the LKF (linearized Kalman filter), the EKF (extended Kalman filter), and the UKF (unscented Kalman filter). With the LKF and EKF, performance varies depending on how often Jacobians (partial derivative matrices) are updated. In other words, we see a tradeoff between computational effort and filtering performance. With the unscented Kalman filter, Jacobians are not calculated but computational effort is typically high due to …