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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: In this article, a robust strong tracking cubature Kalman filter (RSTCKF) is proposed for the spacecraft attitude estimation with quaternion constraint, which uses two multiple fading factor matrices to make different channels have respective filter adjustment capability, which improves the tracking performance.

40 citations

Journal ArticleDOI
TL;DR: In this article, the authors explore the application of Kalman-Levy filter to handle maneuvering targets and show that the performance of the Kalman filter in the non-maneuvering portion of track is worse than that of a KF.
Abstract: Among target tracking algorithms using Kalman filtering-like approaches, the standard assumptions are Gaussian process and measurement noise models. Based on these assumptions, the Kalman filter is widely used in single or multiple filter versions (e.g., in an interacting multiple model (IMM) estimator). The oversimplification resulting from the above assumptions can cause degradation in tracking performance. In this paper we explore the application of Kalman-Levy filter to handle maneuvering targets. This filter assumes a heavy-tailed noise distribution known as the Levy distribution. Due to the heavy-tailed nature of the assumed distribution, the Kalman-Levy filter is more effective in the presence of large errors that can occur, for example, due to the onset of acceleration or deceleration. However, for the same reason, the performance of the Kalman-Levy filter in the nonmaneuvering portion of track is worse than that of a Kalman filter. For this reason, an IMM with one Kalman and one Kalman-Levy module is developed here. Also, the superiority of the IMM with Kalman-Levy module over only Kalman-filter-based IMM for realistic maneuvers is shown by simulation results.

40 citations

Proceedings ArticleDOI
Fred Daum1, Jim Huang1
TL;DR: In this article, the authors proposed a particle filter that implements Bayes' rule using particle flow rather than with a pointwise multiplication of two functions, which is a radical departure from other particle filters.
Abstract: We solve the fundamental and well known problem in particle filters, namely "particle collapse" or "particle degeneracy" as a result of Bayes' rule. We do not resample, and we do not use any proposal density; this is a radical departure from other particle filters. The new filter implements Bayes' rule using particle flow rather than with a pointwise multiplication of two functions. We show numerical results for a new filter that is vastly superior to the classic particle filter and the extended Kalman filter. In particular, the computational complexity of the new filter is many orders of magnitude less than the classic particle filter with optimal estimation accuracy for problems with dimension greater than 4. Moreover, our new filter is two orders of magnitude more accurate than the extended Kalman filter for quadratic and cubic measurement nonlinearities. We also show excellent accuracy for problems with multimodal densities.

40 citations

Journal ArticleDOI
TL;DR: A new version of extended Kalman filtering (EKF) for state estimation in chemical nonlinear continuous-discrete stochastic systems is elaborates and it is shown that its quality is raised by using the adaptive sixth-order nested implicit Runge–Kutta (NIRK) method of Gauss type with automatic local and global error controls.

40 citations

Journal ArticleDOI
TL;DR: An invariant extended Kalman filter (IEKF)-based and a multiplicative extended KalMan filter-based solution to localization in indoor environments that is successfully validated in experiments and demonstrates the advantage of the IEKF design.
Abstract: Localization in indoor environments is a technique that estimates the robot’s pose by fusing data from onboard motion sensors with readings of the environment, in our case obtained by scan matching point clouds captured by a low-cost Kinect depth camera. We develop both an invariant extended Kalman filter (IEKF)-based and a multiplicative extended Kalman filter-based solution to this problem. The two designs are successfully validated in experiments and demonstrate the advantage of the IEKF design.

40 citations


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