Topic
Alpha beta filter
About: Alpha beta filter is a research topic. Over the lifetime, 5653 publications have been published within this topic receiving 128415 citations.
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09 May 2006
TL;DR: In this article, two analytical methods to incorporate state-variable inequality constraints into the Kalman filter are derived, one is a general technique that uses hard constraints to enforce inequalities on the state variable estimates and the other is a soft constraint that is required to be approximately satisfied rather than exactly satisfied.
Abstract: 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. Thus, two analytical methods to incorporate state-variable inequality constraints into the Kalman filter are now derived. The first method is a general technique that uses hard constraints to enforce inequalities on the state-variable estimates. The resultant filter is a combination of a standard Kalman filter and a quadratic programming problem. The second method uses soft constraints to estimate those state variables that are known to vary slowly with time. (Soft constraints are constraints that are required to be approximately satisfied rather than exactly satisfied.) The incorporation of state-variable constraints increases the computational effort of the filter but significantly improves its estimation accuracy. The improvement is proven theoretically and simulations are used to show that the proposed algorithms can provide an improved performance over unconstrained Kalman filtering.
162 citations
TL;DR: In this paper, three model-based filtering algorithms, including extended Kalman filter, unscented Kalman filtering, and particle filter, are respectively used to estimate state-of-charge (SOC) and their performances regarding to tracking accuracy, computation time, robustness against uncertainty of initial values of SOC, and battery degradation, are compared.
162 citations
TL;DR: In this article, the most relevant methods to increase the robustness in both the stage of residual generation and residual evaluation are surveyed, among them, the generalized observer scheme, the robust parity space check, the unknown input and observer scheme and the decorrelation filter.
162 citations
TL;DR: A comprehensive survey of the application of Kalman filtering to chemical problems is provided in this article, with a focus on the discrete Kalman algorithm and its application in analytical chemistry. But, as discussed in this paper, it is based on the Kalman filter, a recursive, linear digital filter originally developed for use in navigation, but now used in many fields.
161 citations
TL;DR: The distributed weighted robust Kalman filter developed in this paper has stronger fault-tolerance ability and is derived for uncertain systems with multiple sensors.
160 citations