MVDR algorithm based on sample selection strategy

The MVDR (Minimum Variance Distortionless Response) algorithm is a classic Wiener filtering method used for beamforming in array signal processing. In a one-dimensional linear array high-frequency ground wave radar system, it can be employed to suppress various types of ionospheric clutter. A key step in suppression is the use of maximum likelihood estimation (MLE) to estimate the ionospheric clutter covariance matrix. However, MLE typically assumes that samples are independently and identically distributed (i.i.d.). Traditional MVDR algorithms estimate the clutter covariance matrix using all samples, which may not satisfy the i.i.d. condition. Therefore, this paper proposes two new sample selection strategies for choosing i.i.d. samples. One strategy utilizes the KL(Kullback-Leibler) divergence method from information geometry, while the other employs the weighted correlation coefficient method. Simulation results demonstrate that both new algorithms effectively suppress ionospheric clutter.