Multi-object tracking in reconnaissance unmanned aerial vehicle (UAV) videos under foggy weather presents several challenges: (1) The boundaries between the target and the background become blurred; (2) targets have limited target pixels and few available features; (3) reconnaissance UAV movement causes variations in the shape and size of targets. We tackl these challenges by developing a hierarchical supervised network for multi-object tracking (HSTrack). First, we employ the salient foreground perception (SFP) module to extract foreground targets from blurry backgrounds. SFP is incorporated to enhance the limited pixel objects and increase foreground-background contrast. Second, we utilize multi-scale object detection (MSOD) to predict the objects with scale variations. Meanwhile, to implement hierarchical supervision, we devise a unified loss function to compute the loss of the prediction between MSOD and SFP. Finally, we use mixed metrics to connect detections to tracklets, achieving multi-object tracking. We validate our approach through experiments on a self-assembled dataset collected from a reconnaissance UAV under foggy weather, named UAV-Fog. The experimental results demonstrate that our proposed method outperforms other state-of-the-art methods in reconnaissance UAV videos under foggy weather. Furthermore, we validate the effectiveness of HSTrack on the vision meets drone multi-object tracking (VisDrone2019-MOT) and UAV detection and tracking (UAVDT) datasets, where it also achieved outstanding performance.

Within the context of ground-air cooperation, the distributed formation trajectory tracking control problems for the Heterogeneous Multi-Agent Systems (HMASs) is studied. First, considering external disturbances and model uncertainties, a graph theory-based formation control protocol is designed for the HMASs consisting of Unmanned Aerial Vehicles (UAVs) and Unmanned Ground Vehicles (UGVs). Subsequently, a formation trajectory tracking control strategy employing adaptive Fractional-Order Sliding Mode Control (FOSMC) method is developed, and a Feedback Multilayer Fuzzy Neural Network (FMFNN) is introduced to estimate the lumped uncertainties. This approach empowers HMASs to adaptively follow the expected trajectory and adopt the designated formation configuration, even in the presence of various uncertainties. Additionally, an event-triggered mechanism is incorporated into the controller to reduce the update frequency of the controller and minimize the communication exchange among the agents, and the absence of Zeno behavior is rigorously demonstrated by an integral inequality analysis. Finally, to confirm the effectiveness of the proposed formation control protocol, some numerical simulations are presented.