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Off-Grid Sparse Bayesian Inference with Biased Total Grids for Dense Time Delay Estimation

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

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Abstract

For dense time delay estimation (TDE), when multiple time delays are located within a grid interval, it is difficult for the existing sparse Bayesian learning/inference (SBL/SBI) methods to obtain high estimation accuracy to meet the application requirements. To solve this problem, this paper proposes a method named off-grid sparse Bayesian inference — biased total grid (OGSBI-BTG), where a mesh evolution process is conducted to move the total grids iteratively based on the position of the off-grid between two grids. The proposed method updates the off-grid dictionary matrix by further reconstructing an optimum mesh and offsetting the off-grid vector. Experimental results demonstrate that the proposed approach performs better than other state-of-the-art SBI methods and multiple signal classification even when the grid interval is larger than the gap of true time delays. In this paper, the time domain model and frequency domain model of TDE are studied.

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。

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Authors and Affiliations

  1. College of Information, Mechanical and Electrical Engineering; Shanghai Engineering Research Center of Intelligent Education and Bigdata, Shanghai Normal University, Shanghai, 200234, China

    Shuang Wei  (魏爽), Wenyao Li  (李文瑶), Ying Su  (苏颖) & Rui Liu  (刘睿)

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  1. Shuang Wei  (魏爽)
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  2. Wenyao Li  (李文瑶)
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  3. Ying Su  (苏颖)
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  4. Rui Liu  (刘睿)
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Corresponding author

Correspondence to Ying Su  (苏颖).

Additional information

Foundation item: the National Natural Science Foundation of China (No. 61401145), and the Natural Science Foundation of Shanghai (No. 19ZR1437600)

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Wei, S., Li, W., Su, Y. et al. Off-Grid Sparse Bayesian Inference with Biased Total Grids for Dense Time Delay Estimation. J. Shanghai Jiaotong Univ. (Sci.) 28, 763–771 (2023). https://doi.org/10.1007/s12204-022-2464-z

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  • DOI: https://doi.org/10.1007/s12204-022-2464-z

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