Training Sample Selection Method Based on Model-Data-Driven Maritime Radar Target Detection

Multidimensional feature extraction and fusion algorithms are widely utilized to improve the performance of radar maritime target detection. However, these methods often suffer from underfitting and overfitting issues due to the uneven distribution of training samples. To enhance the performance of maritime target detection while mitigating the issues of underfitting and overfitting, an effective approach is to improve the comprehensiveness of training samples. This study proposes a novel training sample selection method based on model-data-driven maritime radar target detection. A surrogate equation is constructed in this study using existing empirical models and measured data. Subsequently, it dynamically selects training samples exhibiting strong coverage and compactness, guided by the global and local variation characteristics of this surrogate equation. Experimental results show that when using the same feature and classifier conditions, the feature classifiers formed by the proposed method outperform those built using traditional sample selection approaches (such as random assignment, expert knowledge, and statistical methods). This superiority is evident across various target types, including daily vessels, small fishing boats, and marine buoys, as well as under diverse sea conditions and observation parameters like incidence angles. This approach not only enhances the performance of maritime target detection but also ensures the generalization ability of the method.