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Clutter suppression algorithm based on fast converging sparse Bayesian learning for airborne radar

机译：基于快速收敛的稀疏贝叶斯学习的机载雷达杂波抑制算法

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摘要

Adapting the space-time adaptive processing (STAP) filter with finite number of secondary data is of particular interest for airborne phased-array radar clutter suppression. Sparse representation (SR) technique has been introduced into the STAP framework for the benefit of drastically reduced training requirement. However, most SR algorithms need the fine tuning of one or more user parameters, which affect the final results significantly. Sparse Bayesian learning (SBL) and multiple sparse Bayesian learning (M-SBL) are robust and user parameter free approaches, but they converge quite slowly. To remedy this limitation, a fast converging SBL (FCSBL) approach is proposed based on Bayesian inference along with a simple approximation term, then, it is extended to the multiple measurement vector case, and the resulting approach is termed as M-FCSBL To improve the performance of STAP in finite secondary data situation, the M-FCSBL is utilized to estimate the clutter plus noise covariance matrix (CCM) from a limited number of secondary data, and then the resulting CCM is adopted to devise the STAP filter and suppress the clutter. Numerical experiments with both simulated and Mountain-Top data are carried out. It is shown that the proposed algorithm has superior clutter suppression performance in finite secondary data situation.
机译：对于机载相控阵雷达杂波抑制，采用有限数量的辅助数据适配空时自适应处理（STAP）滤波器尤为重要。为了大大减少培训需求，已将稀疏表示（SR）技术引入STAP框架。但是，大多数SR算法需要对一个或多个用户参数进行微调，这会严重影响最终结果。稀疏贝叶斯学习（SBL）和多重稀疏贝叶斯学习（M-SBL）是健壮且无用户参数的方法，但是它们收敛非常慢。为了弥补这一局限性，提出了一种基于贝叶斯推断和简单近似项的快速收敛SBL（FCSBL）方法，然后将其扩展到多重测量向量的情况，并将其称为M-FCSBL。针对STAP在有限的二次数据情况下的性能，利用M-FCSBL从有限数量的二次数据中估计杂波加噪声协方差矩阵（CCM），然后将所得的CCM用于设计STAP滤波器并抑制混乱。进行了模拟和山顶数据的数值实验。结果表明，该算法在有限的二次数据情况下具有较好的杂波抑制性能。

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