Radar Detection Self-Evolution Based on Incremental Learning in Time-Varying Clutter Environments

This article introduces a novel self-evolving architecture for radar target detection models, focusing on addressing generalization problem in intelligent target detection models operating within time-varying clutter environments. The architecture can continuously learn the time-varying clutter distribution features and dynamically evolve the radar detection models online with the aid of detection performance feedback, which can enhance the adaptive detection ability of the radar system. Specifically, first, an intelligent detection model library is constructed by pretraining relevant models using offline samples gathered across diverse weather conditions. Subsequently, a three-level weighted scoring criterion is established to evaluate the performance of the radar detection models in real time and provide feedback information on the direction of radar self-evolution. In addition, considering that the model retraining time is closely related to the sample size of the training set, a sample selection technique based on the feature representation is proposed to select a small number of more representative samples to facilitate rapid incremental learning of the models. Finally, to validate the efficacy of the proposed approach, the performance of the radar’s online self-evolving detection is assessed using a publicly available dataset. The results of the evaluation confirm that the proposed architecture can improve the target detection performance by 9.04% within 1 min in the face of a time-varying clutter environment, which significantly solves the problem of poor detection performance caused by insufficient generalization of intelligent detection model.