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.

Introduction to random signals and applied Kalman filtering : with MATLAB exercises and solutions

Abstract: Probability and Random Variables: A Review. Mathematical Description of Random Signals. Response of Linear Systems to Random Inputs. Wiener Filtering. The Discrete Kalman Filter, State-Space Modeling, and Simulation. Prediction, Applications, and More Basics on Discrete Kalman Filtering. The Continuous Kalman Filter. Smoothing. Linearization and Additional Intermediate-Level Topics on Applied Kalman Filtering. More on Modeling: Integration of Noninertial Measurements Into INS. The Global Positioning System: A Case Study. Appendices. Index.

A novel Kalman filter for frequency estimation of distorted signals in power systems

Abstract: A simple and novel approach in the design of an extended Kalman filter (EKF) for the measurement of power system frequency has been presented in this paper. The design principles and the validity of the model have been outlined. The performance of this filter has been compared with some of the existing methods for estimating the frequency of a signal under noisy conditions. The feasibility of the proposed filter has been tested in the laboratory under worst-case measurement and network conditions, which might occur in a typical power system. Also, the proof of the stability for the proposed filter has been discussed for a single sinusoid. It has been found that the proposed algorithm is suitable for real-time applications especially when the frequency changes are abrupt and the signal is corrupted with noise and other disturbances due to harmonics.

A moving horizon-based approach for least-squares estimation

Abstract: A general formulation of the moving horizon estimator is presented. An algorithm with a fixed-size estimation window and constraints on states, disturbances, and measurement noise is developed, and a probabilistic interpretation is given. The moving horizon formulation requires only one more tuning parameter (horizon size) than many well-known approximate nonlinear filters such as extended Kalman filter (EFK), iterated EKF, Gaussian second-order filter, and statistically linearized filter. The choice of horizon size allows the user to achieve a compromise between the better performance of the batch least-squares solution and the reduced computational requirements of the approximate nonlinear filters. Specific issues relevant to linear and nonlinear systems are discussed with comparisons made to the Kalman filter, EKF, and other recursive and optimization-based estimation schemes.

Kalman filtering in two dimensions

Abstract: The Kalman filtering method is extended to two dimensions. The resulting computational load is found to be excessive. Two new approximations are then introduced. One, called the strip processor, updates a line segment at a time; the other, called the reduced update Kalman filter, is a scalar processor. The reduced update Kalman filter is shown to be optimum in that it minimizes the post update mean-square error (mse) under the constraint of updating only the nearby previously processed neighbors. The resulting filter is a general two-dimensional recursive filter.

Tracking a ballistic target: comparison of several nonlinear filters

Abstract: This paper studies the problem of tracking a ballistic object in the reentry phase by processing radar measurements. A suitable (highly nonlinear) model of target motion is developed and the theoretical Cramer-Rao lower bounds (CRLB) of estimation error are derived. The estimation performance (error mean and standard deviation; consistency test) of the following nonlinear filters is compared: the extended Kalman filter (EKF), the. statistical linearization, the particle filtering, and the unscented Kalman filter (UKF). The simulation results favor the EKF; it combines the statistical efficiency with a modest computational load. This conclusion is valid when the target ballistic coefficient is a priori known.