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1 day ago · The Kalman filter, serving as a recursive estimation technique for real-time applications, has found extensive application in various domains including navigation and target tracking [1]. Based on explicit system parameters and the assumption of Gaussian noise, the Kalman filter can provide the optimal estimate of the state.
Aug 1, 2024 · To address this issue, a new adaptive fast DKF (AFDKF) algorithm and adaptive fast desensitized extended Kalman filter (AFDEKF) have been proposed. The fast filters have an adaptive factor that enables them to adjust the sensitivity-weighting matrix based on the orthogonality principle of measurement residuals.
Adaptive kalman filtering is crucial in modern applications such as autonomous vehicles, sensor fusion, and financial modeling because it provides accurate real-time state estimation in rapidly changing environments.
In this work, we introduce aknet, an adaptive mb / dd filter that is trained with data to cope with a model mismatch, and can rapidly adapt to changes in the ss model without retraining. Our aknet extends kn[ 6] by adapting its mapping based on a context information parameter coined sow (sow).
Combining the classical Kalman filter (KF) with a deep neural network (DNN) enables tracking in partially known state space (SS) models. A major limitation of current DNN-aided designs stems from the need to train them to filter data originating from a specific distribution and underlying SS model.
Feb 14, 2019 · In this paper, a new adaptive Kalman filter is proposed for a linear Gaussian state-space model with inaccurate noise statistics based on the variational Bayesian (VB) approach.
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Can a Kalman filter be combined with a deep neural network?
Can adaptive Kalman filters be used in maneuvring target tracking?
Is kalmannet a data-driven/model-based filter?
How accurate is Kalman filtering?
Can Kalman filter improve Gaussian state-space model with inaccurate noise statistics?
Does Kalman filter performance degrade if inaccurate noise statistics are used?
May 13, 2021 · We present KalmanNet, a hybrid data-driven/model-based filter that does not require full knowledge of the underlying model parameters. KalmanNet is inspired by the classical KF flow and implemented by integrating a dedicated and compact neural network for the Kalman gain computation.
