A New Method of Video SAR Ground Moving Target Detection and Tracking Based on the Interframe Amplitude Temporal Curve

In the video synthetic aperture radar (Video SAR) system, the moving target will leave a shadow at its actual position due to the Doppler effect. As the shadow of the moving target moves between Video SAR frames, the amplitudes of pixel points at the corresponding positions will jump between frames as well. According to this characteristic, a new method of Video SAR ground moving target detection and tracking based on the interframe amplitude temporal curves is proposed in this article. In this method, the specially designed multiple receptive field fusion neural network model based on frame variation (MRFN-FV) is used to classify the pixel points with obvious interframe amplitude jumps on the whole-time axis, and then, the false alarms are suppressed based on the temporal change characteristics of pixel points. Finally, the improved clustering algorithm is used to detect, locate, and track the moving targets in each frame of SAR images. The effectiveness of the proposed method is verified through the measured data recorded by the THz band Video SAR system.