Randomly Weighted CKF for Multisensor Integrated Systems

المؤلفون المشاركون

Zong, Hua
Gao, Zhaohui
Wei, Wenhui
Zhong, Yongmin
Gu, Chengfan

المصدر

Journal of Sensors

العدد

المجلد 2019، العدد 2019 (31 ديسمبر/كانون الأول 2019)، ص ص. 1-19، 19ص.

الناشر

Hindawi Publishing Corporation

تاريخ النشر

2019-11-11

دولة النشر

مصر

عدد الصفحات

19

التخصصات الرئيسية

هندسة مدنية

الملخص EN

The cubature Kalman filter (CKF) is an estimation method for nonlinear Gaussian systems.

However, its filtering solution is affected by system error, leading to biased or diverged system state estimation.

This paper proposes a randomly weighted CKF (RWCKF) to handle the CKF limitation.

This method incorporates random weights in CKF to restrain system error’s influence on system state estimation by dynamic modification of cubature point weights.

Randomly weighted theories are established to estimate predicted system state and system measurement as well as their covariances.

Simulation and experimental results as well as comparison analyses demonstrate the presented RWCKF conquers the CKF problem, leading to enhanced accuracy for system state estimation.

نمط استشهاد جمعية علماء النفس الأمريكية (APA)

Zong, Hua& Gao, Zhaohui& Wei, Wenhui& Zhong, Yongmin& Gu, Chengfan. 2019. Randomly Weighted CKF for Multisensor Integrated Systems. Journal of Sensors،Vol. 2019, no. 2019, pp.1-19.
https://search.emarefa.net/detail/BIM-1187174

نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)

Zong, Hua…[et al.]. Randomly Weighted CKF for Multisensor Integrated Systems. Journal of Sensors No. 2019 (2019), pp.1-19.
https://search.emarefa.net/detail/BIM-1187174

نمط استشهاد الجمعية الطبية الأمريكية (AMA)

Zong, Hua& Gao, Zhaohui& Wei, Wenhui& Zhong, Yongmin& Gu, Chengfan. Randomly Weighted CKF for Multisensor Integrated Systems. Journal of Sensors. 2019. Vol. 2019, no. 2019, pp.1-19.
https://search.emarefa.net/detail/BIM-1187174

نوع البيانات

مقالات

لغة النص

الإنجليزية

الملاحظات

Includes bibliographical references

رقم السجل

BIM-1187174