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

doi:10.1109/ACCESS.2019.2900712

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

2019-01-01

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DOI：10.1109/ACCESS.2019.2900712

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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.

Keyword:

Discrete interference suppression

nonhomogeneous clutter

sparse Bayesian learning (SBL)

STAP

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

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