Aiming at the problem of the limited application range and low accuracy of existing radar calibration methods, this paper studies the radar calibration method based on cooperative targets, and establishes the integrated radar measurement error model. Then, the improved sparrow search algorithm (ISSA) is used to estimate the systematic error, so as to avoid the loss of partial accuracy caused by the process of approximating the nonlinear equation to the linear equation, thus improving the radar calibration effect. The sparrow search algorithm (SSA) is improved through integrating various strategies, and the convergence speed and stability of the algorithm are also improved. The simulation results show that the ISSA can solve radar systematic errors more accurately than the generalized least square method, Kalman filter, and SSA. It takes less time the than SSA and has a certain stability and real-time performance. The radar measurement error after correction is obviously smaller than that before correction, indicating that the proposed method is feasible and effective.

The trajectory data mining and analysis of maritime targets are of great significance in furthering the construction of maritime traffic facilities, improving the ability of marine supervision and maintaining national marine security. However, due to factors such as detection means and environmental interference, a large number of trajectory data have problems such as large space-time span, uneven sampling, and poor continuity, which seriously restrict the effect of trajectory mining. Therefore, this paper proposes a method of trajectory reconstruction based on navigation state recognition and bidirectional kinematic interpolation. The method mainly includes three steps: (1) data preprocessing, (2) navigation state recognition, and (3) trajectory interpolation. The method can recognize the navigation state of the targets in different segments, and then adaptively select the interpolation method to reconstruct the trajectories, that is, linear interpolation in the straight segments and bidirectional kinematic interpolation in the turning segments. Among them, bidirectional kinematic interpolation uses the cubic Hermite function to nonlinearly fit the acceleration of the interpolation section, and then calculates the velocity and coordinates of the interpolation points by time weighting from the positive and negative directions. The proposed method is verified and analyzed on the contest dataset of “Intelligent classification and recognition of XX trajectories”. Compared with the existing methods, the reconstruction results of the proposed method are closer to the real trajectories, and it can effectively reconstruct the target trajectories with better accuracy and stability. At the same time, the effect of trajectories classification based on the Long Short-Term Memory (LSTM) model, which uses trajectories before and after reconstruction, is compared and analyzed. The results show that the model has a higher classification accuracy for reconstructed trajectory, which proves the necessity of trajectory reconstruction.

Trajectory clustering is of great significance for identifying behavioral patterns and vessel types of non-cooperative ships. However, existing trajectory clustering methods suffer from limitations in extracting cross-spatiotemporal scale features and modeling the coupling relationship between positional and motion features, which restricts clustering performance. To address this, this study proposes a deep ship trajectory clustering method based on feature embedding representation learning (ERL-DTC). The method designs a Temporal Attention-based Multi-scale feature Aggregation Network (TA-MAN) to achieve dynamic fusion of trajectory features from micro to macro scales. A Dual-feature Self-attention Fusion Encoder (DualSFE) is employed to decouple and jointly represent the spatiotemporal position and motion features of trajectories. A two-stage optimization strategy of “pre-training and joint training” is adopted, combining contrastive loss and clustering loss to jointly constrain the embedding representation learning, ensuring it preserves trajectory similarity relationships while being adapted to the clustering task. Experiments on a public vessel trajectory dataset show that for a four-class task (K = 4), ERL-DTC improves ACC by approximately 14.1% compared to the current best deep clustering method, with NMI and ARI increasing by about 28.9% and 30.2%, respectively. It achieves the highest Silhouette Coefficient (SC) and the lowest Davies-Bouldin Index (DBI), indicating a tighter and more clearly separated cluster structure. Furthermore, its inference efficiency is improved by two orders of magnitude compared to traditional point-matching-based methods, without significantly increasing runtime due to model complexity. Ablation studies and parameter sensitivity analysis further validate the necessity of each module design and the rationality of hyperparameter settings. This research provides an efficient and robust solution for feature learning and clustering of vessel trajectories across spatiotemporal scales.