Research on object detection and tracking technology based on adaptive Kalman filter

Accurate detection and tracking of moving targets in dynamic environments are essential for various applications, including military surveillance, autonomous navigation, and robotics. In order to address the contra-maneuvering target tracking challenge in 120mm rocket guidance systems based on image strap-down guidance, which aims to identify and track maneuvering targets in TV images and provide bias information for back-end flight controllers, this paper proposes a threshold for binary segmentation based on images. The target is segmented within the tracking box using binaryvalued images. Additionally, an adaptive weight Kalman filter algorithm for target tracking is proposed. By dynamically adjusting filter weights based on target motion characteristics, the proposed approach effectively addresses challenges such as occlusion and rapid maneuvering, ensuring stable and reliable tracking throughout the trajectory. Comprehensive simulations demonstrate the efficiency of the proposed algorithm, illustrating its ability to accurately track maneuvering targets under varying conditions. The results highlight the superiority of the proposed approach in providing precise target localization and trajectory prediction, thus offering valuable advancements in image-based guidance systems.