Sea Surface Small Target Detection Integrating OTFS and Deep Unfolding

To address challenges such as sparse feature representation difficulties and poor robustness in detecting weak targets against sea clutter backgrounds, this study investigates the adaptability of channel modeling and sparse reconstruction techniques for target recognition. It proposes a method for detecting small sea targets that integrates OTFS with deep unfolding. Using OTFS modulation to map signals from the time domain to the Delay-Doppler domain, a sparse recovery model is constructed. Deep unfolding is employed to transform the FISTA iterative process into a trainable network architecture. A GAN model is employed for adaptive parameter optimization across layers, while the CBAM mechanism enhances response to critical regions. A multi-stage loss function design and false alarm rate control mechanism improve detection accuracy and interference resistance. Validation using the IPIX dataset yields average detection rates of 88.2%, 91.5%, 90.0%, and 83.3% across four polarization modes, demonstrating the proposed method’s robust performance.