Enhanced parametric detection employing diversified scan and iteration

Abstract

In this paper, an enhanced parametric detection method employing diversified scan and iteration is proposed for heterogeneous environment. A diversified scan, encompassing both rough scan and intensive scan, is first conducted in the normalized space–time frequency field to acquire the interference’s frequency components, which is conducive to obtaining the outline of interference in the normalized space–time two-dimensional (2-D) frequency field. The intensities of these components are subsequently determined through iteration to reduce the impact brought by the randomness of training samples. Meanwhile, the environment is commonly sparse in most cases, which is fully utilized in the proposed method. Then the cross-correlation matrix and autoregressive (AR) covariance matrix are reconstructed to form the corresponding detector. In the end, the availability and superiority of the proposed method are validated by numerical results. It is shown from numerical results that the proposed method performs well in detection performance compared to several existing typical methods in heterogeneous environment.