基于协方差拟合准则的降维空时自适应处理方法

最优空时自适应处理方法由于计算量大且需要大量的训练样本来估计杂波协方差矩阵而无法满足实际应用,针对此问题,本文提出一种基于协方差拟合准则的降维空时自适应处理方法.该方法首先对每个阵元收到的回波信号进行时域滤波,然后通过协方差拟合准则构造了在高斯信源下与最大似然估计器渐近等价的优化问题,同时我们将协方差拟合优化问题转换为...

Reduced-dimension space-time adaptive processing method based on the covariance fitting criterion

The optimal space-time adaptive processing method is impractical due to the heavy computational complexity and the training samples required for estimating the clutter covariance matrix. To solve this problem, a reduced-dimension space-time adaptive processing method based on the covariance fitting criterion is proposed in this paper. The temporal filtering is firstly performed on the echo signals from each array element. Then, an optimization problem, which is asymptotically equivalent to the maximum likelihood estimator under Gaussian sources, is established by utilizing the covariance fitting criterion. Meanwhile, the covariance fitting optimization problem can be reformulated as a semi-definite problem, which can be solved by the CVX toolbox to estimate the clutter plus noise covariance matrix. Simulation experiments show that the proposed method requires fewer samples and has better clutter suppression performance compared with the combined space-time processing methods after Doppler filtering.