Overcoming Data Scarcity in Maritime Radar Target Detection via a Complex-Valued Hybrid Spatiotemporal Network

Abstract

Detecting small floating targets on the sea surface has long been a major challenge in radar signal processing. Recently, deep learning (DL) has attracted considerable attention for its potential to improve detection probability. However, its performance heavily relies on the availability of sufficiently labeled datasets, which are often difficult to acquire in complex sea clutter environments. Therefore, this letter introduces the complex-valued hybrid spatiotemporal network (CVHSTNet), a novel maritime radar target detection method designed for low-data scenarios that uses time-frequency (TF) representations of radar echoes as inputs. To mitigate the overfitting issue, CVHSTNet is intentionally designed with a shallow architecture, integrating a three-layer complex-valued convolutional neural network (CV-CNN) with a one-layer CV bidirectional long short-term memory (CV-BiLSTM) network. Unlike existing real-valued models that overlook phase information, our method operates directly on CV data to capture the complete signal representation. More importantly, this hybrid architecture enables the network to effectively exploit both spatial and temporal characteristics, thereby further enhancing feature representations. Comprehensive experiments on 40 datasets from the IPIX database demonstrate that with only 50 samples per range cell for training, the proposed method achieves a detection probability exceeding 90% in 37 of 40 datasets, with a false alarm rate (FAR) of $10^{-3}$ . To the best of our knowledge, this is the first time a DL-based approach has demonstrated the ability to distinguish between small floating targets and sea clutter under limited labeled radar data conditions.