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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.


Papers
More filters
Journal ArticleDOI
TL;DR: A new approach overcoming issues is proposed, allowing the design of optimal filters for nonlinear systems in both the cases of known and unknown system, based on the direct filter design from a set of data generated by the system.
Abstract: Optimal filters for nonlinear systems are in general difficult to derive or implement. The common approach is to use approximate solutions such as extended Kalman filters, ensemble filters or particle filters. However, no optimality properties can be guaranteed by these approximations, and even the stability of the estimation error cannot often be ensured. Another relevant issue is that, in most practical situations, the system whose variables have to be estimated is not known, and a two-step procedure is adopted, based on model identification from data and filter design from the identified model. However, the designed filter may display large performance deteriorations in the case of modeling errors. In this paper, a new approach overcoming these issues is proposed, allowing the design of optimal filters for nonlinear systems in both the cases of known and unknown system. The approach is based on the direct filter design from a set of data generated by the system. Either experimental or simulated data can be used for design. A bound on the number of data necessary to ensure a given filter accuracy is also provided, showing that the proposed approach is not affected by the curse of dimensionality.

43 citations

Journal ArticleDOI
TL;DR: In this paper, a maximum likelihood algorithm for simultaneous estimation of state and parameter values in nonlinear stochastic state-space models is proposed, which uses a combination of expectation maximization, nonlinear filtering and smoothing algorithms.

43 citations

Journal ArticleDOI
01 Apr 2011
TL;DR: Simulation and experimental results indicate that for most conditions EKF estimates are better than UKF while error in NSF estimates is large, however NSF performance is relatively better than other two filters for specific condition like large parameter uncertainty.
Abstract: This paper deals with the design and implementation of Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF) and Neural State Filter (NSF) for the state estimation of a three-phase induction motor. Extensive simulation studies have been carried out to assess the relative performance of the three filters under various machine operating conditions and model uncertainties. Filter performance for similar conditions was verified with experimental data and found to be consistent with simulation results. The simulation and experimental results indicate that for most conditions EKF estimates are better than UKF while error in NSF estimates is large. However NSF performance is relatively better than other two filters for specific condition like large parameter uncertainty.

43 citations

Proceedings ArticleDOI
10 Apr 2007
TL;DR: An algorithm which enables a biased control input in vehicle model using neural network is proposed which is very effective compared with the standard EKF algorithm under the practical condition where the mobile robot has bias error in its modeling and environment has strong uncertainties.
Abstract: This paper addresses the problem of simultaneous localization and map building (SLAM) using a neural network aided extended Kalman filter (NNEKF) algorithm. Since the EKF is based on the white noise assumption, if there are colored noise or systematic bias error in the system, EKF inevitably diverges. The neural network in this algorithm is used to approximate the uncertainty of the system model due to mismodeling and extreme nonlinearities. Simulation results are presented to illustrate the proposed algorithm NNEKF is very effective compared with the standard EKF algorithm under the practical condition where the mobile robot has bias error in its modeling and environment has strong uncertainties. In this paper, we propose an algorithm which enables a biased control input in vehicle model using neural network

43 citations

01 Jan 2001
TL;DR: The idea of this approach consist in embedding the AR model into the Kalman Filter which makes possible to use such KF AR (Kalman Filter AR) models for linear prediction of non-stationary signals.
Abstract: This paper presents a new approach for detection of artifacts in sleep electroencephalogram (EEG) recordings. The proposed approach is based on Kalman filter. The idea of this approach consist in embedding the AR model into the Kalman Filter which makes possible to use such KF AR (Kalman Filter AR) models for linear prediction of non-stationary signals. Such model can be set up to detect and follow discrete dynamic changes of the signal. For detection of the EEG artifacts we have exploited the evolution of the state noise - increase in state noise indicate the dynamic change of the signal. The evaluation of the results was done by the Receiver-Operator Characteristics (ROC) curves in terms of the specificity and the sensitivity. For 90% of the specificity the best achieved value of the sensitivity using KF AR model was 33%. In order to achieve better results we have tried the following modification: instead of the Kalman Filter we have used extended Kalman Filter and instead of the AR model a neural network. The preliminary results look promissing: for 90% of the specificity we have achieved 65% of the sensitivity.

43 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202348
2022162
202120
20208
201914
201851