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.
Papers published on a yearly basis
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TL;DR: A novel resilient extended Kalman filter is proposed for discrete-time nonlinear stochastic systems with sensor failures and random observer gain perturbations, designed for state estimation under these conditions.
Abstract: Missing sensor data is a common problem, which severely influences the overall performance of modern data-intensive control and computing applications. In order to address this important issue, a novel resilient extended Kalman filter is proposed for discrete-time nonlinear stochastic systems with sensor failures and random observer gain perturbations. The failure mechanisms of multiple sensors are assumed to be independent of each other with different failure rates. The locally unbiased robust minimum mean square filter is designed for state estimation under these conditions. The performance of the proposed estimation method is verified by means of numerical Monte Carlo simulation of two different nonlinear stochastic systems, involving a sinusoidal system and a Lorenz oscillator system.
37 citations
01 Apr 1979
TL;DR: The application of the reduced update Kalman filter in the enhancement of two-dimensional images using a composite model description of the image shows considerable improvement in the visual quality compared with linear constant coefficient Kalman filtering.
Abstract: In this paper, we demonstrate the application of the reduced update Kalman filter in the enhancement of two-dimensional images using a composite model description of the image. Typically, for the purpose of simulation, five models corresponding to four predominant correlation directions (at angles of 0°, 45°, 90°, 135° to the horizontal) and one isotropic model, are considered. These models are then used to synthesize a filtering algorithm that estimates the image with near minimum mean square error. The results show considerable improvement in the visual quality compared with linear constant coefficient Kalman filtering.
37 citations
TL;DR: A Tensor Network Kalman filter is introduced, which can estimate state vectors that are exponentially large without ever having to explicitly construct them, and which easily accommodates the case where several different state vectors need to be estimated simultaneously.
37 citations
TL;DR: In this article, the unscented Kalman filter (UKF) and the particle filter (PF) were compared for the case of significant plant-model mismatch, and the PF was shown to be less robust than the Kalman update-based filters.
37 citations
TL;DR: This article presents a classical type of solution to the time series prediction competition, the CATS benchmark, which is organized as a special session of the IJCNN 2004 conference, based on sequential application of the Kalman smoother.
37 citations