Ship target identification is of great significance in both military and civilian fields. Many methods have been proposed to identify the targets using tracks information. However, most of existing studies can only identify two or three types of targets, and the accuracy of identification needs to be further improved. Meanwhile, they do not provide a reliable probability of the identification result under a high-noise environment. To address these issues, a Bayesian-Transformer Neural Network (BTNN) is proposed to complete the ship target identification task using tracks information. The aim of the research is improving the ability of ship target identification to enhance the maritime situation awareness and strengthen the protection of maritime traffic safety. Firstly, a Bayesian-Transformer Encoder (BTE) module that contains four different Bayesian-Transformer Encoders is used to extract discriminate features of tracks. Then, a Bayesian fully connected layer and a SoftMax layer complete the classification. Benefiting from the superiority of the Bayesian neural network, BTNN can provide a reliable probability of the result, which captures both aleatoric uncertainty and epistemic uncertainty. The experiments show that the proposed method can successfully identify nine types of ship targets. Compared with traditional methods, the identification accuracy of BTNN increases by 3.8% from 90.16%. In addition, compared with non-Bayesian Transformer Neural Network, the BTNN can provide a more reliable probability of the identification result under a high-noise environment.

Ship target recognition based on trajectories has great potential in the field of target recognition. In the existing research, the context information is ignored, which limits the improvement of ship target recognition ability. In addition, the process of trajectory feature extraction is complex, and recognition accuracy needs to be further improved. In this paper, a ship target recognition method based on a context-enhanced trajectory is proposed. The maritime context knowledge base is constructed to enhance the trajectory information and to improve the separability of different types of target trajectories. A deep learning model is used to extract trajectory features and context features automatically. Offline training and online recognition are adopted to complete the target recognition task. Experimental analysis and verification are carried out using the automatic identification system (AIS) dataset. The recognition accuracy increases by 7.91% after context enhancement, which shows that the context enhancement is efficient. The proposed method also has a strong anti-noise ability. In the noisy environment set in this paper, the recognition accuracy of the proposed method is still maintained at 86.13%.

With the continuous expansion of the application field of artificial intelligence, radar data processing has also begun to fully enter the era of intelligence, and new achievements have emerged in the research fields of target detection, target tracking, and target recognition. At a time of rapid development of artificial intelligence technology, it is necessary to think about the future development of radar data intelligent processing. To this end, combining the research history and the superficial understanding of radar data intelligent processing in the past ten years, our team analyzes the main research progress and challenges of radar data intelligent processing, examines the new requirements for autonomous, multi-modal, and multi-mode radar data intelligent processing, and explores new opportunities in the era of large models, etc. Furthermore, along a research path of ``1 domain foundation model + 3 core task models + N typical application implementations'', the research conceptions and main contents of radar data large model are put forward in order to enable the research of intelligent radar.

For autonomous maritime perception and situational awareness, the end-to-end multi-object tracking paradigm has achieved complete learning, from image sequences to tracking results, reducing the reliance on manually designed association rules and holding great potential. However, in maritime radar video multi-object tracking, due to the limited visual features of targets and significant feature variations under long-term tracking, problems such as identity switching are prone to occur, making it difficult to directly apply existing end-to-end approaches. To solve these problems, this paper proposes a trajectory-aware end-to-end multi-object tracking method. The real-time trajectory of the targets contains temporal context information. This work uses it as prior knowledge to enhance visual feature encoding and compensate for the shortcomings of single-frame visual features. Specifically, the trajectory feature is encoded by the trajectory encoder module while, simultaneously, the visual features are encoded through the backbone and the visual feature encoder module. Then, in the frame-trajectory cross-modal attention module, the trajectory feature encoding is used to reconstruct the visual feature encoding with cross-attention, dynamically enhancing the features related to the target identity. Experiments on actual collected maritime radar video data show that the proposed method is effective, achieving improvements in several key indicators.