GNSSLow-Cost MEMS-INS Integration Using Variational Bayesian Adaptive Cubature Kalman Smoother and Ensemble Regularized ELM
المؤلفون المشاركون
Xiao, Zhu
Georges, Hassana Maigary
Wang, Dong
المصدر
Mathematical Problems in Engineering
العدد
المجلد 2015، العدد 2015 (31 ديسمبر/كانون الأول 2015)، ص ص. 1-13، 13ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2015-10-25
دولة النشر
مصر
عدد الصفحات
13
التخصصات الرئيسية
الملخص EN
Among the inertial navigation system (INS) devices used in land vehicle navigation (LVN), low-cost microelectromechanical systems (MEMS) inertial sensors have received more interest for bridging global navigation satellites systems (GNSS) signal failures because of their price and portability.
Kalman filter (KF) based GNSS/INS integration has been widely used to provide a robust solution to the navigation.
However, its prediction model cannot give satisfactory results in the presence of colored and variational noise.
In order to achieve reliable and accurate positional solution for LVN in urban areas surrounded by skyscrapers or under dense foliage and tunnels, a novel model combining variational Bayesian adaptive Kalman smoother (VB-ACKS) as an alternative of KF and ensemble regularized extreme learning machine (ERELM) for bridging global positioning systems outages is proposed.
The ERELM is applied to reduce the fluctuating performance of GNSS during an outage.
We show that a well-organized collection of predictors using ensemble learning yields a more accurate positional result when compared with conventional artificial neural network (ANN) predictors.
Experimental results show that the performance of VB-ACKS is more robust compared with KF solution, and the prediction of ERELM contains the smallest error compared with other ANN solutions.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Georges, Hassana Maigary& Wang, Dong& Xiao, Zhu. 2015. GNSSLow-Cost MEMS-INS Integration Using Variational Bayesian Adaptive Cubature Kalman Smoother and Ensemble Regularized ELM. Mathematical Problems in Engineering،Vol. 2015, no. 2015, pp.1-13.
https://search.emarefa.net/detail/BIM-1074455
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Georges, Hassana Maigary…[et al.]. GNSSLow-Cost MEMS-INS Integration Using Variational Bayesian Adaptive Cubature Kalman Smoother and Ensemble Regularized ELM. Mathematical Problems in Engineering No. 2015 (2015), pp.1-13.
https://search.emarefa.net/detail/BIM-1074455
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Georges, Hassana Maigary& Wang, Dong& Xiao, Zhu. GNSSLow-Cost MEMS-INS Integration Using Variational Bayesian Adaptive Cubature Kalman Smoother and Ensemble Regularized ELM. Mathematical Problems in Engineering. 2015. Vol. 2015, no. 2015, pp.1-13.
https://search.emarefa.net/detail/BIM-1074455
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1074455
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر