Multi-Pass Sequential Mini-Batch Stochastic Gradient Descent Algorithms for Noise Covariance Estimation in Adaptive Kalman Filtering

Multi-Pass Sequential Mini-Batch Stochastic Gradient Descent Algorithms for Noise Covariance Estimation in Adaptive Kalman Filtering

Estimation of unknown noise covariances in a Kalman filter is a problem of significant practical interest in a wide array of applications. Although this problem has a long history, reliable algorithms for their estimation were scant, and necessary and sufficient conditions for identifiability of the...

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Bibliografski detalji
Glavni autori: Hee-Seung Kim, Lingyi Zhang, Adam Bienkowski, Krishna R. Pattipati
Format: Članak
Jezik:English
Izdano: IEEE 2021-01-01
Serija:IEEE Access
Teme:
Adaptive Kalman filtering
noise covariance estimation
Adam
RMS prop
bold-driver
stochastic gradient descent
Online pristup:https://ieeexplore.ieee.org/document/9475033/

Internet

https://ieeexplore.ieee.org/document/9475033/

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