Method for reconstructing safety and arming motion process by integrating Kalman filter and KCF.

This paper addresses the challenge of reconstructing the motion process of the safety and arming (S&A) mechanism in fuze by transforming the problem into a target detection and tracking problem. A novel tracking method, which fuses an improved Kalman filter with a temporal scale-adaptive KCF (AKF-CF), is proposed. The methodology introduces key innovations: (1) Extraction of grayscale images and directional gradient histogram (HOG) features of the target, followed by the use of an Adaptive Wave PCA-Autoencoder (AWPA) method to accurately capture multi-modal and multi-scale features of the target; (2) Application of bilinear interpolation and hybrid filtering techniques to generate a spatial and temporal scale-adaptive bounding box for the filtered target, enabling dynamic adjustment of the tracking box size; (3) Integration of an occlusion-aware mechanism using average peak correlation energy (APCE) to trigger Kalman-based position prediction when the target is occluded, thus mitigating tracking drift. Finally, the tracking curve of the target is plotted, facilitating the reconstruction of the S&A mechanism's motion trajectory. Experimental results from five datasets indicate the effectiveness of the proposed method. Compared to the ACSRCF algorithm on the OTB50 dataset, the proposed method achieves accuracy and success rate improvements of 0.8 and 0.6%, respectively. On the OTB100 dataset, it attains 92.50% accuracy and 68.10% success rate, outperforming other related filtering algorithms. These results highlight significant improvements in tracking accuracy and success rate, demonstrating the algorithm's robustness in handling challenging tracking scenarios. Additionally, the reconstructed motion curves effectively replicate mechanical trajectories, showcasing strong performance in complex occlusion environments.