Visible and infrared image fusion (VIF) is essential for unmanned aerial vehicle (UAV) perception by integrating complementary information from heterogeneous sensors to enhance understanding in complex environments. Existing deep learning-based VIF methods often face a trade-off between the limited receptive fields of CNNs and the high computational overhead of Transformers. Although Mamba shows great potential in modeling long-range dependencies with linear complexity, it struggles to preserve spatial information in complex spatial correlations. Moreover, most VIF methods lack effective multimodal feature disentanglement, which is particularly challenging in low-light and cluttered backgrounds. To address these challenges, we propose MCDFuse, a novel Mamba-CNN-driven VIF framework that synergistically combines the strengths of Mamba and CNN architectures. We introduce a dynamic feature activation module to improve spatial modeling and facilitate high-quality image reconstruction. A dual-branch architecture integrates ConvNeXt modules to expand the receptive field and inverted residual blocks to enhance detail representation. Furthermore, a feature decomposition loss with normalization constraints is designed to optimize feature disentanglement during training. Comparative experiments on multiple datasets, including newly constructed ones, against 12 state-of-the-art methods demonstrate that our approach achieves superior fusion quality and improves performance on downstream object detection tasks. The dataset can be downloaded from https://github.com/MCDFuse/UAVScene.

The Bearing–Angle algorithm effectively improves the observability of vision-based motion estimation for moving targets by combining the dimensional information of target detection frames. However, the robustness of this algorithm will be significantly reduced when the observation error increases due to sudden changes in the target motion state. To address this shortcoming, this paper proposes a visual target motion estimation algorithm called the Dynamic Bearing–Angle, which aims to improve the accuracy and robustness of target motion analysis in dynamic scenarios such as unmanned aerial vehicle (UAV). The algorithm innovatively introduces a dual robustness mechanism of dynamic noise intensity adaptation and outlier suppression based on M-estimation. By adjusting the noise covariance matrix in real time and assigning low weights to the outlier observations using the Huber weight function, the Dynamic Bearing–Angle algorithm is able to effectively cope with non-Gaussian noise and sudden target maneuvers. We validate the performance of the proposed algorithm with numerical simulations and real sensor data, and the results show that the Dynamic Bearing–Angle maintains good robustness and accuracy under different noise intensities.