@inproceedings{00fa89a1f97f46cfa6a71232a49a96ee,
title = "Improved Adaptive Kalman Filter with Unknown Process Noise Covariance",
abstract = "This paper considers the joint recursive estimation of the dynamic state and the time-varying process noise covariance for a linear state space model. The conjugate prior on the process noise covariance, the inverse Wishart distribution, provides a latent variable. A variational Bayesian inference framework is then adopted to iteratively estimate the posterior density functions of the dynamic state, process noise covariance and the introduced latent variable. The performance of the algorithm is demonstrated with simulated data in a target tracking application.",
keywords = "Adaptive filtering, conjugate priors, unknown process noise covariance, variational Bayesian",
author = "Jirong Ma and Hua Lan and Zengfu Wang and Xuezhi Wang and Quan Pan and Bill Moran",
note = "Publisher Copyright: {\textcopyright} 2018 ISIF; 21st International Conference on Information Fusion, FUSION 2018 ; Conference date: 10-07-2018 Through 13-07-2018",
year = "2018",
month = sep,
day = "5",
doi = "10.23919/ICIF.2018.8455394",
language = "英语",
isbn = "9780996452762",
series = "2018 21st International Conference on Information Fusion, FUSION 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "2054--2058",
booktitle = "2018 21st International Conference on Information Fusion, FUSION 2018",
}