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, two methods based on an extended Kalman filter (EKF) and unscented Kalman Filter (UKF) were proposed to estimate the battery state of charge (SoC) of a lithium-ion battery used in EVs.
Abstract: The battery state of charge (SoC), whose estimation is one of the basic functions of battery management system (BMS), is a vital input parameter in the energy management and power distribution control of electric vehicles (EVs). In this paper, two methods based on an extended Kalman filter (EKF) and unscented Kalman filter (UKF), respectively, are proposed to estimate the SoC of a lithium-ion battery used in EVs. The lithium-ion battery is modeled with the Thevenin model and the model parameters are identified based on experimental data and validated with the Beijing Driving Cycle. Then space equations used for SoC estimation are established. The SoC estimation results with EKF and UKF are compared in aspects of accuracy and convergence. It is concluded that the two algorithms both perform well, while the UKF algorithm is much better with a faster convergence ability and a higher accuracy.
59 citations
TL;DR: In this paper, a fast and convenient alignment method is proposed to improve the speed of convergence, using rotation vectors instead of traditional Euler angles, and an algorithm is developed to automatically tune the measurement noise covariance matrix using adaptive Kalman filtering.
Abstract: A fast and convenient alignment method is proposed. To improve the speed of convergence, we used rotation vectors instead of traditional Euler angles. Furthermore, we developed an algorithm to automatically tune the measurement noise covariance matrix using adaptive Kalman filtering. Finally, the developed algorithms were applied to an aerial imaging system to automatically geo-locate the centers of the images.
59 citations
TL;DR: It is rigorously proved that the proposed self-tuning Kalman fusers converge to the steady-state optimal Kalmanfuser fusers in a realization or with probability one, so that they have asymptotic global optimality.
Abstract: For the multisensor systems with unknown noise variances, based on the solution of the matrix equations for the correlation function, the on-line estimators of the noise variance matrices are obtained, whose consistency is proved using the ergodicity of sampled correlation function. Further, two self-tuning weighted measurement fusion Kalman filters are presented for the multisensor systems with identical and different measurement matrices, respectively. Based on the stability of the dynamic error system, a new convergence analysis tool is presented for a self-tuning fuser, which is called the dynamic error system analysis (DESA) method. A new concept of convergence in a realization is presented, which is weaker than the convergence with probability one. It is rigorously proved that the proposed self-tuning Kalman fusers converge to the steady-state optimal Kalman fusers in a realization or with probability one, so that they have asymptotic global optimality. A simulation example for a target tracking system with 3 sensors shows their effectiveness.
58 citations
TL;DR: A comparative study of several nonlinear filters, namely, extended Kalman Filter (EKF), unscented KF (UKF), particle filter (PF), and recursive linear minimum mean square error (LMMSE) filter for the problem of satellite trajectory estimation is presented.
Abstract: In this paper, we present a comparative study of several nonlinear filters, namely, extended Kalman Filter (EKF), unscented KF (UKF), particle filter (PF), and recursive linear minimum mean square error (LMMSE) filter for the problem of satellite trajectory estimation. We evaluate the tracking accuracy of the above filtering algorithms and obtain the posterior Cramer-Rao lower bound (PCRLB) of the tracking error for performance comparison. Based on the simulation results, we provide recommendations on the practical tracking filter selection and guidelines for the design of observer configurations.
58 citations
TL;DR: This paper proposes another version of the Kalman filter, to be called Structural Kalman Filter, which can successfully work its role of estimating motion information under such a deteriorating condition as occlusion and experimental results show that the suggested approach is very effective in estimating and tracking non-rigid moving objects reliably.
58 citations