An Intelligent Sample Selection Method for Space-Time Adaptive Processing in Heterogeneous Environment

In the heterogeneous interference and target-rich environment, the clutter-plus-noise covariance matrix (CCM) estimate with a good match for the cell under test is significant for space–time adaptive processing. In this paper, an intelligent sample selection method is proposed to estimate the CCM and, thus, to suppress the clutter in the heterogeneous background. First, the proposed method applies the sample amplitude and the spatial relationship between the sample and the antenna beam center to describe the characteristics of samples, which preliminarily selects the crucial homogeneous and heterogeneous samples based on the joint histogram property. Then, the generalized inner product value and the spatial included angle are used to work as two representative features for the training samples as well as the testing samples. Based on the feature vector, the multi-dimensional feature projection based on the kernel function is carried out to achieve the offline training classifier, and the testing samples are classified with intelligence in the multi-dimensional feature space by utilizing the trained support vector machine. Due to the utilization of space–time property in representative feature extraction and intelligent strategy for the decision-making process, the proposed method acquires more reliable and efficient sample selection results compared with most of conventional sample selection methods. The experimental results based on the simulated and measured data show the effectiveness of the proposed method.