loading page

Heterogeneous clutter suppression method with improved direct data domain based on sparse Bayesian learning
  • Feng Jing,
  • Qiang Wang,
  • Bin Xue
Feng Jing
Xidian University

Corresponding Author:[email protected]

Author Profile
Qiang Wang
National University of Defense Technology
Author Profile
Bin Xue
National University of Defense Technology
Author Profile

Abstract

To deal with the problem that performance degradation of airborne phased array system is caused by the serious shortage of independent and identical distributed (IID) training samples in the nonhomogeneous clutter environment, an improved direct data domain method based on sparse Bayesian learning is proposed, which can only use single snapshot data of cell under test (CUT) to suppress the clutter. In this paper, the iterative formulas of three hyper-parameters are first given. Then, the sparsity solution of CUT is obtained. Lastly, with the approximate prior information of target, the clutter covariance matrix (CCM) is effectively estimated to calculate the adaptive filter weight and realize the clutter suppression. Simulation results verify that the proposed approach has more superior heterogeneous clutter suppression performance while dramatically decreasing the computational burden.