Research on the anti-jamming targetdetection for pulsed Doppler (PD) radar is intensively important to the normal work of radar in complicated and volatile electromagnetic environments. Generally, traditional anti-ja...
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Research on the anti-jamming targetdetection for pulsed Doppler (PD) radar is intensively important to the normal work of radar in complicated and volatile electromagnetic environments. Generally, traditional anti-jamming targetdetection methods involve several stages, each one of these stages is based on a model. Due to the issues of poor flexibility caused by multi-stage processing and model mismatch caused by model-based methods, the detection performance will be severely decreased. Therefore, a new target detection scheme for PD radar in an Interrupted-Sampling Repeater Jamming (ISRJ) environment is proposed, which is a dual-driven method of model and data. This innovative solution first achieves energy accumulation on the Doppler channel by a model-driven method, then proposes a series of preprocessing methods to process radar signals to adapt to a data-driven Convolutional Neural Network (CNN) detector. Finally, a CNN detector structure is proposed. The effectiveness of this novel scheme is demonstrated by a series of performance and visualisation experiments.
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