Off-Grid Radar Coincidence Imaging Based on Variational Sparse Bayesian Learning
المؤلفون المشاركون
Qin, Yu-Liang
Zhou, Xiaoli
Cheng, Yongqiang
Wang, Hongqiang
المصدر
Mathematical Problems in Engineering
العدد
المجلد 2016، العدد 2016 (31 ديسمبر/كانون الأول 2016)، ص ص. 1-12، 12ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2016-05-08
دولة النشر
مصر
عدد الصفحات
12
التخصصات الرئيسية
الملخص EN
Radar coincidence imaging (RCI) is a high-resolution staring imaging technique motivated by classical optical coincidence imaging.
In RCI, sparse reconstruction methods are commonly used to achieve better imaging result, while the performance guarantee is based on the general assumption that the scatterers are located at the prediscretized grid-cell centers.
However, the widely existing off-grid problem degrades the RCI performance considerably.
In this paper, an algorithm based on variational sparse Bayesian learning (VSBL) is developed to solve the off-grid RCI.
Applying Taylor expansion, the unknown true dictionary is approximated accurately to a linear model.
Then target reconstruction is reformulated as a joint sparse recovery problem that recovers three groups of sparse coefficients over three known dictionaries with the constraint of the common support shared by the groups.
VSBL is then applied to solve the problem by assigning appropriate priors to the three groups of coefficients.
Results of numerical experiments demonstrate that the algorithm can achieve outstanding reconstruction performance and yield superior performance both in suppressing noise and in adapting to off-grid error.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Zhou, Xiaoli& Wang, Hongqiang& Cheng, Yongqiang& Qin, Yu-Liang. 2016. Off-Grid Radar Coincidence Imaging Based on Variational Sparse Bayesian Learning. Mathematical Problems in Engineering،Vol. 2016, no. 2016, pp.1-12.
https://search.emarefa.net/detail/BIM-1111794
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Zhou, Xiaoli…[et al.]. Off-Grid Radar Coincidence Imaging Based on Variational Sparse Bayesian Learning. Mathematical Problems in Engineering No. 2016 (2016), pp.1-12.
https://search.emarefa.net/detail/BIM-1111794
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Zhou, Xiaoli& Wang, Hongqiang& Cheng, Yongqiang& Qin, Yu-Liang. Off-Grid Radar Coincidence Imaging Based on Variational Sparse Bayesian Learning. Mathematical Problems in Engineering. 2016. Vol. 2016, no. 2016, pp.1-12.
https://search.emarefa.net/detail/BIM-1111794
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1111794
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