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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01 Jan 2007
TL;DR: A new computational approach for parameter identification is proposed based on the application of polynomial chaos theory, which has shown great promise for an improvement in the computational efficiency of current parameter estimation methods.
Abstract: Fast parameter estimation is a non-trivial task, and it is critical when the system parameters evolve with time, as demanded in real-time control applications. In this study, a new computational approach for parameter identification is proposed based on the application of polynomial chaos theory. The polynomial chaos approach has been shown to be considerably more efficient than Monte Carlo in the simulation of systems with a small number of uncertain parameters. In the framework of this new approach, a (suboptimal) Extended Kalman Filter (EKF) is used to recalculate the polynomial chaos expansions for the uncertain states and the uncertain parameters. As a case study, the proposed parameter estimation method is applied to a four degree-of-freedom roll plane model of a vehicle for which the vertical stiffnesses of the tires are estimated from periodic observations of the displacements and velocities across the suspensions. The results obtained with this approach are close to the actual values of the parameters. In addition, the EKF approach gives more information about the parameters of interest than a simple estimated value: the estimation comes in the form of a probability density function. The approach presented in this paper has shown great promise for an improvement in the computational efficiency of current parameter estimation methods. Possible applications of this theory to the field of off-road vehicle simulations include the estimation of various vehicle parameters of interest, as well as the estimation of parameters related to the tire-terrain contact.
43 citations
16 Mar 2009
TL;DR: In this article, an unscented Kalman filter was designed and tested for orientation estimation by comparing the output of a 3D accelerometer and a magnetometer with respectively gravity and local magnetic field vectors.
Abstract: Orientation estimation can be executed by comparing the output of a 3D accelerometer and a 3D magnetometer with respectively gravity and local magnetic field vectors. For this purpose, an unscented Kalman filter was designed and tested. However, accelerometers also measure motion other than gravity, resulting in an error when estimating orientation directly from their output signals. Therefore, extra filters are added and the input parameters of the Kalman filter are dynamically varied, in order to reduce the effect of motion. Simulations are performed to tune the filter parameters for minimal motion influence without hampering actual orientation tracking. Satisfactory orientation tracking is performed with the filter using actual sensor nodes.
42 citations
TL;DR: In this paper, the authors proposed a generalization of the Kalman-Levy filter to the case of heavy tail distributions such as power laws and Levy laws, which is known as the tail covariance matrix.
42 citations
TL;DR: In this article, a new algorithm is proposed that smoothly incorporates the nonlinear estimation of the attitude quaternion using Davenport's q-method and the estimation of nonattitude states through an extended Kalman filter.
Abstract: A new algorithm is proposed that smoothly incorporates the nonlinear estimation of the attitude quaternion using Davenport’s q-method and the estimation of nonattitude states through an extended Kalman filter. The new algorithm is compared to an existing one and the various similarities and differences are discussed. The validity of the proposed approach is confirmed by numerical simulations.
42 citations
TL;DR: In this paper, the authors investigated a general multi-level quantized filter of linear stochastic systems and derived a quantized innovations filter that achieves the minimum mean square error under the Gaussian assumption on the predicted density.
Abstract: >> In this paper we investigate a general multi-level quantized filter of linear stochastic systems. For a given multi-level quantization and under the Gaussian assumption on the predicted density, a quantized innovations filter that achieves the minimum mean square error is derived. The filter is given in terms of quantization thresholds and a simple modified Riccati difference equation. By optimizing the filtering error covariance with respect to quantization thresholds, the associated optimal thresholds and the corresponding filter are obtained. Furthermore, the convergence of the filter to the standard Kalman filter is established. We also discuss the design of a robust minimax quantized filter when the innovation covariance is not exactly known. Simulation and experimental results illustrate the effectiveness and advantages of the proposed quantized filter.
42 citations