Journal ArticleDOI
Robust derivative-free Kalman filter based on Huber's M-estimation methodology
TLDR
In this paper, a discrete-time robust nonlinear filtering algorithm is proposed to deal with the contami-nated Gaussian noise in the measurement, which is based on a robust modification of the derivative-free Kalman filter.About:
This article is published in Journal of Process Control.The article was published on 2013-11-01. It has received 66 citations till now. The article focuses on the topics: Extended Kalman filter & Fast Kalman filter.read more
Citations
More filters
Journal ArticleDOI
Robust Kalman filtering based on Mahalanobis distance as outlier judging criterion
TL;DR: In this article, a robust Kalman filter scheme is proposed to resist the influence of the outliers in the observations, where a judging index is defined as the square of the Mahalanobis distance from the observation to its prediction.
Journal ArticleDOI
Robust Kalman Filters Based on Gaussian Scale Mixture Distributions With Application to Target Tracking
TL;DR: A new robust Kalman filtering framework for a linear system with non-Gaussian heavy-tailed and/or skewed state and measurement noises is proposed through modeling one-step prediction and likelihood probability density functions as Gaussian scale mixture (GSM) distributions.
Journal ArticleDOI
Huber’s M-Estimation-Based Process Uncertainty Robust Filter for Integrated INS/GPS
TL;DR: In this paper, Huber's M-estimation methodology is investigated to suppress the process uncertainty, founded on the cascaded form of the Mestimation-based Kalman filter.
Journal ArticleDOI
A Variational Bayesian-Based Unscented Kalman Filter With Both Adaptivity and Robustness
TL;DR: In this article, a modified unscented Kalman filter (UKF) with both adaptivity and robustness is proposed, where the adaptivity is achieved by estimating the time-varying measurement noise covariance based on variational Bayesian approximation.
Journal ArticleDOI
Generalized Kalman smoothing: Modeling and algorithms
TL;DR: State-space smoothing has found many applications in science and engineering as discussed by the authors, under linear and Gaussian assumptions, smoothed estimates can be obtained using efficient recursions, for example Rauch...
References
More filters
Journal ArticleDOI
A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking
TL;DR: Both optimal and suboptimal Bayesian algorithms for nonlinear/non-Gaussian tracking problems, with a focus on particle filters are reviewed.
Book
Estimation with Applications to Tracking and Navigation
TL;DR: Estimation with Applications to Tracking and Navigation treats the estimation of various quantities from inherently inaccurate remote observations using a balanced combination of linear systems, probability, and statistics.
Journal ArticleDOI
A new method for the nonlinear transformation of means and covariances in filters and estimators
TL;DR: A new approach for generalizing the Kalman filter to nonlinear systems is described, which yields a filter that is more accurate than an extendedKalman filter (EKF) and easier to implement than an EKF or a Gauss second-order filter.
BookDOI
Optimal State Estimation Kalman, Hoo and Nonlinear Approches
TL;DR: This is a list of errors in the book Optimal State Estimation, John Wiley & Sons, 2006.
BookDOI
Optimal State Estimation
TL;DR: In this article, a list of errors in the book Optimal State Estimation, John Wiley & Sons, 2006, is presented, along with a detailed discussion of the errors.
Related Papers (5)
Robust Kalman Filter Based on a Generalized Maximum-Likelihood-Type Estimator
Mital A Gandhi,Lamine Mili +1 more