Large-scale distributed Kalman filtering via an optimization approach
MH de Badyn, M Mesbahi - IFAC-PapersOnLine, 2017 - Elsevier
Large-scale distributed systems such as sensor networks, often need to achieve filtering and
consensus on an estimated parameter from high-dimensional measurements. Running a
Kalman filter on every node in such a network is computationally intensive; in particular the
matrix inversion in the Kalman gain update step is expensive. In this paper, we extend
previous results in distributed Kalman filtering and large-scale machine learning to propose
a gradient descent step for updating an estimate of the error covariance matrix; this is then …
consensus on an estimated parameter from high-dimensional measurements. Running a
Kalman filter on every node in such a network is computationally intensive; in particular the
matrix inversion in the Kalman gain update step is expensive. In this paper, we extend
previous results in distributed Kalman filtering and large-scale machine learning to propose
a gradient descent step for updating an estimate of the error covariance matrix; this is then …
Large-scale distributed Kalman filtering via an optimization approach
M Hudoba de Badyn, M Mesbahi - arXiv e-prints, 2017 - ui.adsabs.harvard.edu
Large-scale distributed systems such as sensor networks, often need to achieve filtering and
consensus on an estimated parameter from high-dimensional measurements. Running a
Kalman filter on every node in such a network is computationally intensive; in particular the
matrix inversion in the Kalman gain update step is expensive. In this paper, we extend
previous results in distributed Kalman filtering and large-scale machine learning to propose
a gradient descent step for updating an estimate of the error covariance matrix; this is then …
consensus on an estimated parameter from high-dimensional measurements. Running a
Kalman filter on every node in such a network is computationally intensive; in particular the
matrix inversion in the Kalman gain update step is expensive. In this paper, we extend
previous results in distributed Kalman filtering and large-scale machine learning to propose
a gradient descent step for updating an estimate of the error covariance matrix; this is then …
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