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...

وصف كامل

التفاصيل البيبلوغرافية
المؤلفون الرئيسيون: Hee-Seung Kim, Lingyi Zhang, Adam Bienkowski, Krishna R. Pattipati
التنسيق: مقال
اللغة:English
منشور في: IEEE 2021-01-01
سلاسل:IEEE Access
الموضوعات:
Adaptive Kalman filtering
noise covariance estimation
Adam
RMS prop
bold-driver
stochastic gradient descent
الوصول للمادة أونلاين:https://ieeexplore.ieee.org/document/9475033/

الانترنت

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

مواد مشابهة