Transformer has achieved good performance when used for object tracking. By introducing the self-attention mechanism, the object which is partially occluded can also be tracked since the Transformer can prioritize the most relevant parts in the image. However, when prolonged occlusion occurs, the Transformer tends to lose the objects. Kalman filter is always introduced to the neural network to predict the position of the objects. Limited by the assumption that the objects should move linearly, it is still hard to track the objects with irregular motions under prolonged occlusion. To address this problem, this paper proposes a novel approach for robust object tracking. The Transformer framework is initially constructed for detection. When prolonged occlusion occurs, feature optical flow points are extracted and the optical flow motion is estimated. The predicted values are compared with the detected ground truth to compute the association probability; meanwhile the covariance matrix of the Kalman filter is updated. Hungarian algorithm is finally employed for association matching. Experimental validation on the datasets demonstrates the effectiveness of the proposed method. Comparative analyses with the state-of-the-art algorithms prove the robustness of the proposed approach in complex scenarios.

Intelligent air combat is a hot research topic among countries with strong military power in the world. To solve the maneuver decision problem of air combat Beyond Visual Range (BVR), we propose the hierarchical decision algorithm based on deep reinforcement learning. In the decision algorithm, we use the maneuver set appropriate to the BVR air combat to control the trajectory and the attitude of the aircraft. To expand the action space of the model and increase its decision-making ability, we hierarchize the action space and model it as the multi-discrete one. To solve the problem of sparse reward in air combat, we design a set of reward function taking into consideration the factors including the position advantage, weapon launching, and weapon threat, which can guide the agent to converge to the optimal policy. We also build a complete digital-twin simulation environment for air combat and an expert system. The decision algorithm is trained in the simulation environment, and is evaluated by fighting with the expert system. The experiment results indicate that the decision algorithm proposed has the ability to make autonomous and flexible decisions in BVR air combat based on current situations, and has some advantages against the expert system.

In this paper we present a robust adaptive control for a class of uncertain continuous time multiple input multiple output (MIMO) nonlinear systems. Multiple multi-layer neural networks are employed to approximate the uncertainty of the nonlinear functions, and robustifying control terms are used to compensate for approximation errors. All parameter adaptive laws and robustifying control terms are derived based on Lyapunov stability analysis so that, under appropriate assumptions, semi-global stability of the closed-loop system is guaranteed, and the tracking error asymptotically converges to zero. Simulations performed on a two-link robot manipulator illustrate the approach and its performance.