Multi-Dimensional Fuzzy Clustering-Based Trajectory Initialization Algorithm for Infrared Weak Target Trajectories in Robust Clutter Environments

When conducting maneuver target tracking, trajectory initialization plays a crucial role in enhancing the accuracy of tracking algorithms. During maneuver target tracking, the accuracy of the tracking algorithm can be significantly improved through trajectory initialization. However, the traditional trajectory initialization algorithms face issues such as susceptibility to noise interference, lack of universality, and poor robustness in environments with high clutter levels. To address these issues, this study proposes a trajectory initialization algorithm based on multidimensional fuzzy clustering (MDF-clustering). The algorithm utilizes multidimensional feature information of the target, such as speed and irradiance, to determine point trajectory affiliation by assigning weights based on the clustering center of each feature type. Subsequently, it updates the clustering center and weight assignment using the new target features, ultimately deriving the correct trajectory through iterative processes. Experimental results demonstrate that the proposed method achieves an average stable initialization frame number of 3.12 frames, an average correct trajectory initialization rate of 99.59%, an average false trajectory occupancy rate of 0.04%, and an average missed batch rate of 0.06%. These results indicate improvements of at least 0.87 frames, 27.11%, 60.28%, and 6.48%, respectively, in terms of initialization rate, false trajectory rate, and missed batch rate, when compared to traditional methods. The algorithm enhances the accuracy and robustness of trajectory initialization in challenging environments characterized by solid clutter and target maneuvers, offering significant practical value for target tracking in complex scenarios.