Discrete Interference Suppression Method Based on Robust Sparse Bayesian Learning for STAP

Xiaopeng Yang, Yuze Sun, Jian Yang, Teng Long, Tapan Kumar Sarkar

Research output: Contribution to journalArticle

1 Scopus citations

Abstract

Discrete interference influences the performance of existing space-time adaptive processing methods in practical scenarios. In order to effectively suppress discrete interference in real clutter environment, a discrete interference suppression method based on robust sparse Bayesian learning (SBL) is proposed for airborne phased array radar. In the proposed method, the estimation of spatial-temporal spectrum and the calibration of space-time overcomplete dictionary are carried out iteratively. During one iteration, the prominent components of clutter and discrete interference in the spatial-temporal plane are first estimated by SBL, and then the overcomplete dictionary is calibrated by calculating the error matrix. Because of the robust estimation of spatial-temporal spectral distribution, both the discrete interference and the homogeneous clutter profiles can be effectively suppressed with a small number of space-time data. The effectiveness of the proposed method is verified in the nonhomogeneous environment by utilizing simulated and actual airborne phased array radar data.

Original languageEnglish (US)
Article number8648400
Pages (from-to)26740-26751
Number of pages12
JournalIEEE Access
Volume7
DOIs
StatePublished - Jan 1 2019

Keywords

  • Discrete interference suppression
  • nonhomogeneous clutter
  • sparse Bayesian learning (SBL)
  • STAP

ASJC Scopus subject areas

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)

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