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
Papers
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TL;DR: A new adaptive state estimation algorithm, namely adaptive fading Kalmanfilter (AFKF), is proposed to solve the divergence problem of Kalman filter and has been successfully applied to the headbox of a paper-making machine for state estimation.
210 citations
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TL;DR: In this paper, the particle filter is applied to highly nonlinear models with non-Gaussian uncertainties and compared with the extended Kalman filter for Bayesian state and parameter estimation.
205 citations
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TL;DR: A novel road-matching method designed to support the real-time navigational function of cars for advanced systems applications in the area of driving assistance provides an accurate estimation of position for a vehicle relative to a digital road map using Belief Theory and Kalman filtering.
Abstract: This paper describes a novel road-matching method designed to support the real-time navigational function of cars for advanced systems applications in the area of driving assistance. This method provides an accurate estimation of position for a vehicle relative to a digital road map using Belief Theory and Kalman filtering. Firstly, an Extended Kalman Filter combines the DGPS and ABS sensor measurements to produce an approximation of the vehicle's pose, which is then used to select the most likely segment from the database. The selection strategy merges several criteria based on distance, direction and velocity measurements using Belief Theory. A new observation is then built using the selected segment, and the approximate pose adjusted in a second Kalman filter estimation stage. The particular attention given to the modeling of the system showed that incrementing the state by the bias (also called absolute error) of the map significantly increases the performance of the method. Real experimental results show that this approach, if correctly initialized, is able to work over a substantial period without GPS.
205 citations
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TL;DR: In this article, the iterated unscented Kalman filter (IUKF) is proposed based on the analysis and comparison of conventional nonlinear tracking problem, which can obtain more accurate state and covariance estimation.
Abstract: It is of great importance to develop a robust and fast tracking algorithm in passive localization and tracking system because of its inherent disadvantages such as weak observability and large initial errors. In this correspondence, a new algorithm referred to as the iterated unscented Kalman filter (IUKF) is proposed based on the analysis and comparison of conventional nonlinear tracking problem. The algorithm is developed from UKF but it can obtain more accurate state and covariance estimation. Compared with the traditional approaches (e.g., extended Kalman filter (EKF) and UKF) used in passive localization, the proposed method has potential advantages in robustness, convergence speed, and tracking accuracy. The correctness as well as validity of the algorithm is demonstrated through numerical simulation and experiment results.
204 citations
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TL;DR: In this article, a theoretical analysis of the error propagation due to numerical roundoff for four different Kalman filter implementations is presented, i.e., the conventional Kalman Filter, the square root covariance filter, square root information filter, and the Chandrasekhar square root filter.
Abstract: A theoretical analysis is made of the error propagation due to numerical roundoff for four different Kalman filter implementations: the conventional Kalman filter, the square root covariance filter, the square root information filter, and the Chandrasekhar square root filter. An experimental analysis is performed to validate the new insights gained by the theoretical analysis.
204 citations