The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
For highly nonlinear problems, the linear minimum mean-square error (LMMSE) estimation using a nonlinearly converted measurement can outperform the one using the original measurement. For a function space of measurement conversions, every function in the space can be represented as a linear combination of a basis of the space. Then the LMMSE estimator using a vector with its entries forming a basis...
This paper proposes a new approach for constrained multiple model (MM) maximum a posteriori (MAP) estimation through the expectation-maximization (EM) method by using our previously developed constrained sequential list Viterbi algorithm (CSLVA). The approach is general and applicable for any type of constraints provided they are verifiable. Specific algorithms for implementation are designed, and...
This paper deals with some fundamental problems of the constrained dynamic system, especially where dynamics and constraint are not congruous. First, we introduce a concept of compatibility of the system dynamics and constraint. It divides all constrained systems into three classes: incompatible (0% compatible), (partially) compatible, and congruous (100% compatible). We argue convincingly that the...
In this paper,a noninvasive method for Knee Osteoarthritis (KOA) detection and diagnosis is proposed using data from surface electromyogr- am (sEMG) signals with the purpose of accessing the state of KOA in the early stage. In our experiment, sEMG are collected from rectus femoris, vastus medialis, biceps femoris, semitendi- nosus muscle of control group and KOA group respectively when they are in...
A polytopic model (PM) structure is often used in the areas of automatic control and fault detection as an alternative multiple model approach that explicitly allows for interpolation among local models. The model that is valid, usually unknown, is represented by a weighed combination of models in a given model set. Proposed is a novel approach to PM estimation by modeling the set of PM weights as...
The measurement model nonlinearity is a major challenge in target tracking. This paper presents a comparative performance study of seven nonlinear filters in handling the measurement model nonlinearity. They are: the extended Kalman filter, the unscented filter, the second order divided-differences filter, the Gauss-Hermite quadrature filter, the two-step Kalman filter, the Gaussian particle filter,...
Most of the existing distributed estimation fusion algorithms rely on the existence of the inverses of the corresponding error covariance matrices, e.g., distributed estimation fusion algorithms based on the information form of the Kalman filter and the optimal weighted least-square (WLS) estimator. Theoretically speaking, the error covariance matrices are only at least positive semi-definite and...
In this paper, we compare several nonlinear filtering methods, namely, extended Kalman filter (EKF), unscented filter (UF), particle filter (PF), and linear minimum mean square error (LMMSE) filter for a ballistic target tracking problem. We cast EKF and UF into a general linear recursive estimation framework and reveal their pros and cons. We pinpoint using the LMMSE filter for possible analytical...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.