Machine Learning-Aided Nonhomogeneity Detection Method for Airborne Radar

The weight vector in space-time adaptive processing (STAP) algorithm will lead to notches at the position of the interfering targets when there are interfering targets in the training data. If these interfering targets are close to the target of interest on the space-time spectrum, the target signal self-nulling occurs. To deal with this problem, a machine learning-aided nonhomogeneity detection (ML-NHD) method is proposed. More specifically, the subaperture smoothing technique is first performed on each training data to obtain the subaperture sample covariance matrices (SCMs). We prove that when the airborne radar works in side-looking mode and the clutter foldover factor is an integer, the numbers of large eigenvalues (EIGs) of the subaperture SCMs are different for the ordinary training data samples and outlier training data samples. Then, four features are constructed based on the differences in the characteristics of EIGs and eigenvectors of the subaperture SCMs. Finally, a binary classifier based on support vector machine (SVM) is trained to classify the ordinary training data and the outlier training data. The performance assessment shows that the ML-NHD method can detect the outlier training data effectively and achieves better performance of clutter suppression compared with the conventional methods.