Volume 11 Issue 3
Jun.  2022
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Article Navigation >  Journal of Radars  > 2022  >  11(3): 434-442
YE Hongzhen, GUO Haizhao, GUAN Haoliang, et al. Multi-emitters direct localization method via multi-dictionaries and hierarchical block sparse Bayesian framework[J]. Journal of Radars, 2022, 11(3): 434–442. doi: 10.12000/JR21162
Citation: YE Hongzhen, GUO Haizhao, GUAN Haoliang, et al. Multi-emitters direct localization method via multi-dictionaries and hierarchical block sparse Bayesian framework[J]. Journal of Radars, 2022, 11(3): 434–442. doi: 10.12000/JR21162
shu

Multi-emitters Direct Localization Method via Multi-dictionaries and Hierarchical Block Sparse Bayesian Framework

doi: 10.12000/JR21162
  • 1.

    Research Institute of Electronic Science and Technology, University of Science and Technology of China, Chengdu 611731, China

  • 2.

    54th Research Institute of CETC, Shijiazhuang 050081, China

  • 3.

    School of Information and Communication Engineering, University of Science and Technology of China, Chengdu 611731, China

Funds:  The National Natural Science Foundation of China (62171092)
More Information
  • Corresponding author: ZHANG Shunsheng, zhangss@uestc.edu.cn
  • Received Date: 2021-10-31
  • Accepted Date: 2021-12-17
  • Rev Recd Date: 2021-12-16
    • Available Online: 2021-12-21
  • Publish Date: 2022-01-17
    Abstract
  • The direct position determination method based on compressed sensing depends on the accurate signal propagation model. With partially unknown propagation model parameters, its location performance will decline significantly. Thus, this study proposed a localization method via multi-dictionaries and hierarchical block sparse Bayesian framework. Herein, the emitter location problem is transformed into recovering signals from different dictionaries but with shared sparsity, and the emitter location with channel attenuation is solved by a multi-dictionary combination. Simulation results revealed that the algorithm has better performance than the traditional Sparse Bayesian Learning (SBL) method and Direct Position Determination (DPD) method under the condition of low signal-to-noise ratio and a few snapshots.

     

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