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.

Extended Kalman Filter Based Nonlinear Model Predictive Control

Abstract: This paper formulates a nonlinear model predictive control algorithm based on successive linearization. The extended Kalman filter (EKF) technique is used to develop multi-step prediction of future states. The prediction is shown to be optimal under an affine approximation of the discrete state / measurement equations (obtained by integrating the nonlinear ODE model) made at each sampling time. Connections with previously available successive linearization based MPC techniques by Garcia (NLQDMC, 1984) and Gattu & Zafiriou (1992) are made. Potential benefits and shortcomings of the proposed technique are discussed using a bilinear control problem of paper machine.

Particle filtering and ensemble Kalman filtering for state updating with hydrological conceptual rainfall-runoff models

Abstract: [1] Sequential importance resampling (SIR) filter, residual resampling filter (RR), and an ensemble Kalman (EnKF) filter that can handle dynamic nonlinear/non-Gaussian models are compared to correct erroneous model inputs and to obtain a rainfall-runoff update with a conceptual rainfall-runoff model HBV-96 for flood forecasting purposes. EnKF performs best with a low number of ensemble members. The RR filter performs best at intermediate and high number of particles, although differences are small. With all filters the rainfall error could be estimated during a synthetic experiment when the soil is not too dry and the measurement error on the discharge is not dominant. The temperature error could only be estimated when the temperature is close to 0� C. When applying these methods to a real case, good results are obtained. For low flows, EnKF outperforms both particle filters, because it is less sensitive to misspecification of the model and uncertainties. These methods are feasible and easy to implement in real flood forecasting systems. Further research on the assumptions on model uncertainties and measurement uncertainties is recommended.

A Generalized Extended Kalman Filter Implementation for the Robot Operating System

Abstract: Accurate state estimation for a mobile robot often requires the fusion of data from multiple sensors. Software that performs sensor fusion should therefore support the inclusion of a wide array of heterogeneous sensors. This paper presents a software package, robot_localization, for the robot operating system (ROS). The package currently contains an implementation of an extended Kalman filter (EKF). It can support an unlimited number of inputs from multiple sensor types, and allows users to customize which sensor data fields are fused with the current state estimate. In this work, we motivate our design decisions, discuss implementation details, and provide results from real-world tests.