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 language | English |
|---|---|
| Article number | 8648400 |
| Pages (from-to) | 26740-26751 |
| Number of pages | 12 |
| Journal | IEEE Access |
| Volume | 7 |
| DOIs | |
| Publication status | Published - 2019 |
Keywords
- Discrete interference suppression
- STAP
- nonhomogeneous clutter
- sparse Bayesian learning (SBL)