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.
Papers published on a yearly basis
Papers
More filters
04 Jun 2014
TL;DR: A novel formulation of the Kalman filter for Tobit Type 1 censored measurements is used, called the Tobit KalMan filter for saturated data, which converges to the standard Kalman Filter in the no-censoring case.
Abstract: Saturated, clipped or censored data arises in multiple engineering applications including sensors systems and image based tracking. The saturation limits of a measurement consist of an upper limit and lower limit on the measurements. When a measurement is near a saturated region or in saturated region a standard Kalman filter will be biased and unable to track the true states. In this paper, we use a novel formulation of the Kalman filter for Tobit Type 1 censored measurements. The proposed formulation, called the Tobit Kalman filter for saturated data, converges to the standard Kalman filter in the no-censoring case. A motivating example is presented to demonstrate the usefulness of an estimator for censored data.
38 citations
01 Oct 2006
TL;DR: An effectiveKalman filter localization method for mobile robots is investigated in terms of the robust Kalman filter with perturbation estimator, which enables a great reduction of the localization error when the odometric disturbance is large.
Abstract: An effective Kalman filter localization method for mobile robots is investigated in terms of the robust Kalman filter with perturbation estimator. In the recursive algorithm, the perturbation estimator produces equivalent perturbations with respect to the nominal state equation and the action model of a mobile robot is adaptively modified with the perturbation estimates. The integral control property of the perturbation estimator enables a great reduction of the localization error, specifically when the odometric disturbance is large. The Kalman filter recursive equations including predictor, corrector, perturbation estimator, and the corresponding covariance propagation equations are formulated systematically. The effectiveness of the proposed scheme is verified through simulation and experimental results for a wheeled mobile robot.
38 citations
22 Apr 2009
TL;DR: Bayesian filters provide a statistical tool for dealing with measurement uncertainty by representing the state by random variable and in each time step probability distribution over random variable represents the uncertainty.
Abstract: Bayesian filters provide a statistical tool for dealing with measurement uncertainty. Bayesian filters estimate a state of dynamic system from noisy observations. These filters represent the state by random variable and in each time step probability distribution over random variable represents the uncertainty. If estimate is needed with every new measurement, it is suitable to use recursive filter. Unfortunately optimal Bayesian solution exists in a restrictive set of cases, e.g. Kalman filters which assume Gaussian PDF or we need to use suboptimal solution, e.g. extended Kalman filters which use local linearization to approximate PDF to be Gaussian.
38 citations
TL;DR: In this paper, a constrained Kalman algorithm for adaptive beamforming is proposed to overcome the problem of signal distortion along the look direction which occurs in the unconstrained Kalman beamformer of Baird (1974).
Abstract: From the viewpoint of achieving rapid convergence, application of a Kalman filter to an adaptive array is considered. Compared with the Frost's (1972) constrained least-mean-square algorithm, the constrained Kalman algorithm for adaptive beamforming is proposed to overcome the problem of signal distortion along the look direction which occurs in the unconstrained Kalman beamformer of Baird (1974). A constraint on the array response along the look direction is added to the measurement equation of the Kalman filter. The weight vector of the constrained Kalman beamformer is derived and shown to converge to that of the minimum-variance distortionless-response beamformer. The convergence rate of the proposed algorithm is also analyzed. Compared to Baird's algorithm and the sidelobe canceller with one-step Kalman predictor, simulation results show the effectiveness of the proposed algorithm. >
38 citations
TL;DR: In this paper, an adaptive unscented Kalman filter (AUKF) and an augmented state method are employed to estimate the time-varying parameters and states of a kind of nonlinear high-speed objects.
38 citations