Objective The information obtained through forced reconnaissance is often inaccurate, and targets frequently change course unpredictably. This degrades the performance of target maneuver detection and hampers the analysis of the target intent. Therefore, this paper proposes a detection method for maneuvering maritime targets based on prior knowledge.

Methods  The method incorporates two types of prior knowledge derived from expert experience. The first is that significant differences in target heading occur before and after maneuvering, whereas the target heading remains relatively stable during non-maneuvering periods. The second is that the heading difference before and after maneuvering reaches a local extremum. The maneuvering point in the trajectory tends to maximize the heading difference between adjacent sub-trajectories. Based on the definition of trajectory smoothness metric, a calculation method is proposed to calculate the course maneuver evaluation factor based on principal component analysis (PCA). This factor enables preliminary screening of potential maneuvering points. In order to find trajectory points that satisfy the second prior knowledge, a maximum filtering-based maneuvering point screening method is proposed. The trajectory points that meet both the first prior knowledge and the second prior knowledge are identified as maneuvering points.

Results The simulation results indicate that compared with mainstream algorithms such as interacting multiple model and information entropy-based methods, the proposed method achieves more accurate target maneuver detection. It yields the fewest missed detections and the smallest distance error when compressing the trajectory.

Conclusions The findings confirm the superiority of the proposed algorithm, which can effectively improve the accuracy and robustness of target maneuver detection and provide strong support for target behavior analysis and operational decision-making at sea.