Topic
Invariant extended Kalman filter
About: Invariant extended Kalman filter is a research topic. Over the lifetime, 7079 publications have been published within this topic receiving 187702 citations.
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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: It is argued that when the flow forecasting model is assumed to be an autoregressive moving average (ARMA) model and the corresponding flow data are considered to be free of measurement errors, the minimum mean-square error forecasts obtained by the ‘conventional’ Box and Jenkins-type time series forecasting method are identical with those obtained by using the Kalman filtering technique.
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
TL;DR: In this article, an adaptive estimation of forecast error covariance matrices is proposed for Kalman filtering data assimilation, where a forecast covariance matrix is initially estimated using an ensemble of perturbation forecasts and then adjusted with scale parameters that are adaptively estimated by minimizing −2log-likelihood of observed-minus-forecast residuals.
Abstract: An adaptive estimation of forecast error covariance matrices is proposed for Kalman filtering data assimilation. A forecast error covariance matrix is initially estimated using an ensemble of perturbation forecasts. This initially estimated matrix is then adjusted with scale parameters that are adaptively estimated by minimizing −2log-likelihood of observed-minus-forecast residuals. The proposed approach could be applied to Kalman filtering data assimilation with imperfect models when the model error statistics are not known. A simple nonlinear model (Burgers’ equation model) is used to demonstrate the efficacy of the proposed approach.
38 citations
TL;DR: A nonlinear filter for estimating constant parameters in dynamic systems is presented to illustrate one application of the proposed GTSEKF, which is shown to be equivalent to the EKF with a decoupled computing structure.
Abstract: A general two-stage extended Kalman filter (GTSEKF), which extends the linear general two-stage Kalman filter to nonlinear systems, is further proposed. A new nonlinear two-stage transformation is introduced to facilitate achieving this extension. As in the linear one, the GTSEKF is derived mainly by applying the nonlinear two-stage transformation to the well-known extended Kalman filter (EKF), and is shown to be equivalent to the EKF with a decoupled computing structure. A nonlinear filter for estimating constant parameters in dynamic systems is presented to illustrate one application of the proposed GTSEKF. A literature example is also given to demonstrate the correctness and usefulness of the proposed results.
38 citations