High Speed Target Detection Based on Sparse LVD in Interrupted Sampling Repeater Jamming

‍ ‍Interrupted sampling repeater jamming， prevalent in radar environments， samples and retransmits portions of the transmitter signal to create false targets on the radar receiver， thereby hindering the detection of true targets. Considering that high-speed radar targets are characterized by linear frequency modulation signals， the Lv’s distribution （LVD） algorithm is typically employed for detection. However， this method is rendered ineffective in scenarios involving interrupted sampling repeater jamming. To solve this problem， a novel sparse LVD for a high-speed target detection method is proposed. This method exploits the discontinuity in the time domain of sampling repeater jamming. It extracts the undisturbed portion of the high-speed target’s radar echo signal using an energy function-based threshold. After that， the parametric symmetric instantaneous autocorrelation function of a signal is established for vector representation. Subsequently， the sparse LVD representation model is constructed using the chirp-z transform and fast Fourier transform matrices， which transform high-speed target detection into a sparse solution problem in the centroid frequency-chirp rate domain. After that， the orthogonal matching pursuit （OMP） algorithm is used to obtain the sparse solution. Finally， high-speed moving target detection and parameter estimation are achieved. Simulation experimental results show that the proposed sparse LVD representation model is accurate and effective， and the proposed method can solve the problem of high-speed moving object detection in interrupted sampling direct repeater jamming and interrupted sampling periodic repeater jamming situations accurately， and it has good target detection and parameter estimation performance.