J Shanghai Jiaotong Univ Sci ›› 2023, Vol. 28 ›› Issue (6): 763-771.doi: 10.1007/s12204-022-2464-z
魏爽,李文瑶,苏颖,刘睿
接受日期:
2021-10-18
出版日期:
2023-11-28
发布日期:
2023-12-04
WEI Shuang (魏爽), LI Wenyao (李文瑶),SU Ying* (苏颖), LIU Rui (刘睿)
Accepted:
2021-10-18
Online:
2023-11-28
Published:
2023-12-04
摘要: 对于密集时延估计,当多个真实时延都位于一个网格间隔内时,现有的稀疏贝叶斯学习/推理方法很难获得较高估计精度以满足应用要求。为了解决这个问题,本文提出一种称为偏移全网格的离格稀疏贝叶斯推理方法,此方法进行网格演进,根据离格在两个网格之间的位置迭代移动总网格。所提出的方法通过进一步重构最优网格并偏移离格向量来更新离格字典矩阵。实验结果表明:即使网格间隔大于真实时延间隙,该方法也比其他最先进的稀疏贝叶斯推理方法和多信号分类方法性能好。另外本文研究了时延估计的时域模型和频域模型。
中图分类号:
魏爽,李文瑶,苏颖,刘睿. 基于偏移全网格的离格稀疏贝叶斯推理的密集时延估计研究[J]. J Shanghai Jiaotong Univ Sci, 2023, 28(6): 763-771.
WEI Shuang (魏爽), LI Wenyao (李文瑶),SU Ying* (苏颖), LIU Rui (刘睿). Off-Grid Sparse Bayesian Inference with Biased Total Grids for Dense Time Delay Estimation[J]. J Shanghai Jiaotong Univ Sci, 2023, 28(6): 763-771.
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