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 article, an improved version of the commonly used extended Kalman filter (EKF) by incorporating an adaptive filter procedure is presented, where the system noise covariance is updated in time segments to ensure statistical consistency between the predicted error covariance and the mean square of actual residuals.
Abstract: In the application of system identification to a structural system, unknown parameters are determined based on the numerical analysis of input and output measurements. The accuracy of an identified parameter and its uncertainty both depend on the numerical method, measurement noise and modeling error. Most studies, however, identify parameter means without addressing the issue of parameter uncertainties. Presented in this paper is an improved version of the commonly used extended Kalman filter (EKF) by incorporating an adaptive filter procedure. The system noise covariance is updated in time segments in order to ensure statistical consistency between the predicted error covariance and the mean square of actual residuals. Comprising two stages in a cycle, the adaptive EKF method not only identifies the parameter values but also gives a useful estimate of uncertainties. Two numerical examples of simulation with noise are presented. The first example illustrates the superior statistical performance of the pr...
76 citations
TL;DR: This work presents a robust procedure for optimally estimating a polynomial-form input forcing function, its time of occurrence and the measurement error covariance matrix, R, based on a running window robust regression analysis.
Abstract: A method is proposed to adapt the Kalman filter to the changes in the input forcing functions and the noise statistics. The resulting procedure is stable in the sense that the duration of divergences caused by external disturbances are finite and short and, also, the procedure is robust with respect to impulsive noise (outlier). The input forcing functions are estimated by a running window curve-fitting algorithm, which concurrently provides estimates of the measurement noise covariance matrix and the time instant of any significant change in the input forcing functions. In addition, an independent technique for estimating the process noise covariance matrix is suggested which establishes a negative feedback in the overall adaptive Kalman filter. This procedure is based on the residual characteristics of the standard optimum Kalman filter and a stochastic approximation method. The performance of the proposed method is demonstrated by simulations and compared to the conventional sequential adaptive Kalman filter algorithm. >
76 citations
26 Jul 2010
TL;DR: A novel hybrid filter is developed, which is called the KFGP, which uses Gaussian process kernels to model the spatial field while exploiting efficient Kalman filter state-based approaches tomodel the temporal component.
Abstract: We examine the close relationship between Gaussian processes and the Kalman filter and show how Gaussian processes can be interpreted using familiar Kalman filter mathematical concepts. We use this insight to develop a novel hybrid filter, which we call the KFGP, for spatial-temporal modelling. The KFGP uses Gaussian process kernels to model the spatial field while exploiting efficient Kalman filter state-based approaches to model the temporal component. We also develop a Gaussian process kernel for the familiar Kalman filter near constant acceleration model.
76 citations
TL;DR: In this article, an enhanced closed loop estimator based on Extended Kalman Filter (EKF) is proposed, considering a precise model of the cell dynamics valid for different current profiles and Open Circuit Voltage (OCV).
76 citations
TL;DR: A multiuser receiver based on the Kalman filter is introduced, which can be used for joint symbol detection and channel estimation and has the advantage of working even when the spreading codes used have a period larger than one symbol interval ("long codes"), unlike adaptive equalizer-type detectors.
Abstract: We introduce a multiuser receiver based on the Kalman filter, which can be used for joint symbol detection and channel estimation. The proposed algorithm has the advantage of working even when the spreading codes used have a period larger than one symbol interval ("long codes"), unlike adaptive equalizer-type detectors. Simulation results which demonstrate the performance advantage of the proposed receiver over the conventional detector, the minimum mean squared error (MMSE) detector and a recursive least squares (RLS) multiuser detector are presented. A thorough comparison of the MMSE detector and the proposed detector is attempted because the Kalman filter also solves the MMSE parameter estimation problem, and it is concluded that, because the state space model assumed by the Kalman filter fits the code division multiple access (CDMA) system exactly, a multiuser detector based on the Kalman filter must necessarily perform better than a nonrecursive, finite-length MMSE detector. The computational complexity of the detector and its use in channel estimation are also studied.
75 citations