Zobrazeno 1 - 10
of 19
pro vyhledávání: '"Tai-Shan Lou"'
Publikováno v:
IEEE Access, Vol 7, Pp 151230-151238 (2019)
Unknown biases or perturbations in the INS/GNSS integrated navigation system may produce unforeseeable negative effects when the navigation states are estimated by using the Kalman filtering and its variants. To mitigate these undesirable effects in
Externí odkaz:
https://doaj.org/article/6202ec4cc2ea44b0bbc7cdf2e7ce7851
Publikováno v:
IEEE Access, Vol 7, Pp 78029-78036 (2019)
The ensemble Kalman filter (EnKF) is a random sampling method based on Monte Carlo and Kalman filter for the extremely high-dimensional nonlinear system. However, the uncertain parameters may be have unexpected effects on the state estimate problem.
Externí odkaz:
https://doaj.org/article/0ea2d5fcfdeb4e3da8914f6e872c5a55
Publikováno v:
IEEE Access, Vol 6, Pp 66285-66292 (2018)
To mitigate the negative effects of the sensor measurement biases for the maneuvering target, a novel incremental center differential Kalman filter (ICDKF) algorithm is proposed. Based on the principle of independent incremental random process, the i
Externí odkaz:
https://doaj.org/article/bb12af083bc84470bb26f80ca6826cf0
Publikováno v:
International Journal of Aerospace Engineering, Vol 2019 (2019)
A reliable distributed covariance intersection (CI) fusion integrated navigation algorithm with information feedback during the Mars atmospheric entry is proposed to meet robust, reliable, and high-precision Mars atmospheric entry navigation strategy
Externí odkaz:
https://doaj.org/article/fabb86ac568843dbbb315b3011fce784
Publikováno v:
International Journal of Aerospace Engineering, Vol 2017 (2017)
Signal degradation suffered by the vehicle is a combination brownout and blackout during Mars atmospheric entry. The communications brownout means that signal fades and blackout means that the signal is lost completely. The communications brownout an
Externí odkaz:
https://doaj.org/article/3ebe298f4ed84e3881dfa6a750d42cb9
Publikováno v:
2022 China Automation Congress (CAC).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3c4dc64b5de7504bb0081e7c192f83c4
https://doi.org/10.1109/cac57257.2022.10055174
https://doi.org/10.1109/cac57257.2022.10055174
Publikováno v:
Signal Processing. 202:108767
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::41832fc2b4d353275ff4150aaf50c896
https://doi.org/10.1016/j.sigpro.2022.108767
https://doi.org/10.1016/j.sigpro.2022.108767
Publikováno v:
2021 China Automation Congress (CAC).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ac7f8af536b967e2c992285727a89f0b
https://doi.org/10.1109/cac53003.2021.9727761
https://doi.org/10.1109/cac53003.2021.9727761
Publikováno v:
ICARM
Simultaneous localization and mapping (SLAM) is a crucial problem to solve the navigation and positioning for an autonomous robot moving in an unknown environment. This work proposes a rank Kalman filter (RKF) SLAM algorithm based on the principle of
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e3fcfe889636bbfc5cac35975f82ada0
https://doi.org/10.1109/icarm49381.2020.9195299
https://doi.org/10.1109/icarm49381.2020.9195299
Publikováno v:
IEEE Access, Vol 7, Pp 78029-78036 (2019)
The ensemble Kalman filter (EnKF) is a random sampling method based on Monte Carlo and Kalman filter for the extremely high-dimensional nonlinear system. However, the uncertain parameters may be have unexpected effects on the state estimate problem.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7e69ad0a4b938f7c41b74bdec0b1e999
https://doi.org/10.1109/access.2019.2921971
https://doi.org/10.1109/access.2019.2921971