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Traditional radar target detectors, which are model-driven, often suffer remarkable performance degradation in complex clutter environments due to the weakness in modeling the unpredictable clutter. Deep learning (DL) methods, which are data-driven, have been introduced into the field of radar target detection (RTD) since their intrinsic non-linear feature extraction ability can enhance the separability between targets and the clutter. However, existing DL-based detectors are unattractive since they require a large amount of independent and identically distributed (i.i.d.) training samples of target tasks and fail to be generalized to the other new tasks. Given this issue, incorporating the strategy of meta-learning, we reformulate the RTD task as a few-shot classification problem and develop the Inter-frame Contrastive Learning-Based Meta Detector (IfCMD) to generalize to the new task efficiently with only a few samples. Moreover, to further separate targets from the clutter, we equip our model with Siamese architecture and introduce the supervised contrastive loss into the proposed model to explore hard negative samples, which have the targets overwhelmed by the clutter in the Doppler domain. Experimental results on simulated data demonstrate competitive detection performance for moving targets and superior generalization ability for new tasks of the proposed method.
Zhang et al. (Mon,) studied this question.