Wideband Interference Time–Frequency Feature Prediction and Its Application to Cognitive Radar HRRP Estimation

Wideband interference (WBI) is detrimental to high-resolution radar due to its high power and wide frequency occupancy. In this study, a deep learning (DL) method is proposed to predict the time–frequency (TF) feature of WBI and applied to cognitive radar high-resolution range profile (HRRP) estimation. Specifically, by performing short-time Fourier transform (STFT) on the WBI signal collected in the past and using a sliding window, a series of WBI TF figures is generated. A long short-time memory (LSTM) network is then used to learn the spatiotemporal (ST) correlation of these TF figures, thus predicting the WBI TF feature in the future, based on which, a cognitive method is used for target HRRP estimation with reduced influences of WBI. Numerical results demonstrate the effectiveness of the proposed methods.