DOA Estimation Method Based on Unsupervised Learning Network with Threshold Capon Spectrum Weighted Penalty

Zhengyan Zhang, Xiaodong Qu*, Wolin Li, Hongzhe Miao, Fengrui Liu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

In complex electronic countermeasure environment, direction-of-arrival (DOA) is very important for targets detection, localization and tracking. However, the power of interference is usually stronger than that of signal, which degrades the DOA estimation performance severely, and even makes DOA estimation failure. To solve this issue, this paper proposes a DOA estimation method based on unsupervised learning network with threshold Capon spectrum weighted penalty. In this work, an unsupervised network is proposed to obtain the DOA estimation spectrum, in which labels are no longer required. Furthermore, deep unfolded layers are introduced to remove the iterative solution of sparse recovery and increase the depth of network. Additionally, loss function contains reconstruction error and penalty term is developed to generate zero traps in direction of interference and signal, overcoming the influence of strong interference. Both numerical simulations and experiments demonstrate the effectiveness of the proposed method.

Original languageEnglish
Pages (from-to)701-705
Number of pages5
JournalIEEE Signal Processing Letters
Volume31
DOIs
Publication statusPublished - 2024

Keywords

  • Capon spectrum
  • direction-of-arrival estimation
  • unequal power signal
  • unsupervised learning network

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