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.

State observer for linear systems with piece-wise constant output delays

Abstract: This paper analyses the stability properties of a state observer estimating the system states from delayed measurements for a linear time invariant plant. The delay is assumed to be a known piecewise constant function of time. The observer construction is a two-step procedure and has a ‘chain-like’ structure, consisting of two cascaded dynamical systems. The manifestation of the time-varying delayed output on the observer stability is analysed at both the ‘zeroth’ and the ‘first’ links in the chain of observers.

Performance analysis of derivative-free filters

Abstract: Nonlinear state estimation by the derivative-free Sigma Point Kalman Filters is treated. Particularly, impact of the derivative-free Kalman filters on estimation quality of the Sigma Point Gaussian Sum Filters is discussed. New relations between the Unscented Kalman Filter and the Divided Difference Filters are derived. The main stress is laid on the covariance matrixes which have crucial role for the behaviour explanation of the Sigma Point Gaussian Sum Filters. The theoretical results are illustrated in some numerical examples.

Kalman Filtering for Fuzzy Discrete Time Dynamic Systems

Abstract: This paper uses Kalman filter theory to design a state estimator for noisy discrete time Takagi–Sugeno (T– S) fuzzy models. One local filter is designed for each local linear model using standard Kalman filter theory. Steady state solutions can be found for each of the local filters. Then a linear combination of the local filters is used to derive a global filter. The local filters are time-invariant, which greatly reduces the computational complexity of the global filter. The global filter is shown to be unbiased and (under certain conditions) stable. In addition, under the approximation of uncorrelatedness among the local models, the global filter is shown to be minimum variance. The proposed state estimator is demonstrated on a backing up truck–trailer example.

Event-Based Measurement Updating Kalman Filter in Network Control Systems

Abstract: An event based measurement updating method is introduced for discrete Kalman filters to estimate the state feedback of Lebesgue sampled data systems. It is proposed that the conventional prediction and the measurement updating stages of discrete Kalman filters are not processed at the same rate. The prediction can be constant but the measurement rate is varied based on Lebesgue sampling. The measurement update portion is executed when an event takes place. The stability of discrete Kalman filters is investigated when the Lebesgue threshold is increased.

Improved Kalman Filtering for Systems with Randomly Delayed and Lost Measurements

Abstract: In this paper, a novel Kalman filter is developed for discrete-time linear systems with intermittent observations. The measurement data latency and dropout in transmission from the sensor to the filter is modeled by a group of Bernoulli distributed random variables. The minimum variance filter is designed when the measurement data packets are/are not time stamped by the reorganized innovation approach. Moreover, the steady-state behavior of the proposed Kalman filter is investigated. Finally, simulation results are presented to illustrate that the suggested estimator leads to the remarkably improved performance compared with the previously developed approaches.