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

Abstract

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 nonstationary 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 article 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 (SL) strategy, which can actively sense the environment shift and update the detector parameters correspondingly through SL. Specifically, we first use the annotated dataset to train an initial detector. Then, we design an environment sensing module by adding a subdetection head on the detector. When the detector works in time-varying clutter environments, the entropy between the detector’s output and the subdetection head’s output is utilized to sense the environment shift. Next, we propose an SL strategy that combines adaptive pseudo-label generation with consistency regularization. Once the environment shift is detected, the detector parameters are updated by the proposed SL 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.