CNN-Based Moving Target Detection for Airborne Radar With Controllable False Alarm Module

For airborne radar, it is an urgent task to detect moving targets in clutter background. At present, automatic feature extraction is an effective way to improve the performance of moving target detection (MTD). In this letter, a probability of false alarm (PFA)-controllable detection method based on attention-enhanced convolutional neural network (CNN) with multiscale depth-separable convolution (MSDCAN) is proposed. First, the airborne radar model is established to obtain the radar space-time echo dataset, and then, the trained CNN is used to extract the features of the radar space-time echo data. The designed CNN can automatically learn the different features between the target and the clutter and give the corresponding probability. Finally, the false alarm controllable (FAC) module is used as a discriminator to divide the feature vector into two categories, so as to achieve the control of the PFA. Experimental results demonstrate that compared to other CNN-based detectors, the proposed detector exhibits a higher detection probability on both simulated dataset and the dataset containing real data.