Adaptive Intelligent Radar Target Detection in Time-Varying Sea Clutter via Activate Self-Learning

Maritime radar detectors developed using deep learning technology have demonstrated promising performance in the clutter environment. However, real clutter environments are usually time-varying, and the non-stationary radar data stream easily breaks the independent and identically distributed (i.i.d.) prerequisite of standard deep learning detectors, decreasing the detector’s performance. This paper considers the problem of adaptive maritime radar target detection for deep learning-based detectors in time-varying clutter environments. We propose an adaptive target detection framework based on an active self-learning strategy, which can actively sense the environment shift and update the detector parameters correspondingly through self-learning. Specifically, we first use the annotated data set to train an initial detector. Then, we design an environment sensing module by adding a sub-detection head on the detector. When the detector works in time-varying clutter environments, the entropy between the detector’s output and sub-detection head’s output is utilized to sense the environment shift. Next, we propose a self-learning strategy that combines adaptive pseudo-label generation with consistency regularization. Once the environment shift is detected, the detector parameters are updated by the proposed self-learning strategy, improving the detector’s performance in time-varying clutter environments. Experimental results on the public maritime radar database validate the effectiveness of the proposed framework.