CNN-Based Data Processing for Enhanced Detection of Small Targets in Sea Clutter

Abstract

Detecting small targets within intricate sea clutter presents a formidable challenge. In previous methods, convolutional neural network (CNN)-based detectors have relied on handcrafted features extracted through the manual data processing, which may not fully capture the discriminative features necessary to distinguish targets from sea clutter. This article introduces a novel method of target detection that utilizes CNN-based data processing to directly handle raw data. The proposed CNN-based data processing can automatically extract higher level features from signals, which are often more discriminative and valuable for subsequent detection. The two-stage design of our method allows for the easy replacement of more advanced CNN-based detectors in future applications, providing flexibility for future improvements. Experimental results demonstrate that our method achieves probabilities of detection (PDs) of 0.9008 and 0.8433 on the IPIX and SDRDSP datasets, respectively, with a probability of false alarm (PFA) of 0.001, substantially surpassing other methods. The total FLOPs of our method are 206.42M, making it suitable for real-time applications. Further experiments confirm that our proposed CNN-based data processing can enhance various CNN-based detectors across different datasets, showcasing robustness and effectiveness. Moreover, our method maintains high detection performance even with a limited number of pulses.