UAVs are now platforms that can carry out activities on their own thanks to technological advancements. This research develops a hierarchical reinforcement learning-based decision-making framework to tackle the decision-making difficulties encountered by UAVs in air combat. An approach based on the LSTM-DQN model for UAV air combat decision-making is suggested. To overcome challenges like high-dimensional space, prolonged exploration, and scarce rewards in traditional reinforcement learning, the approach splits the air combat process into a decision layer and a control layer. The behavior tree governs how action sequences are carried out in the control layer, while the enhanced DQN model is in charge of task generation in the decision layer. This framework increases training efficiency, decreases the dimensionality of the state space, and shortens exploration time. Win-rate analysis in a simulated environment is used to verify the algorithm’s efficacy and the model’s viability.

In the tasks of pose estimation and behavior analysis in computer vision, conventional models are often constrained by various factors or complex environments (such as multiple targets, small targets, occluded targets, etc.). To address this problem, this paper proposes a symmetric cascaded additive network (MulAG) to improve the accuracy of posture estimation and behavior analysis in complex environments. MulAG consists of two modules, MulA and MulG. The MulA module is designed based on a cascaded symmetric network structure and incorporates the addition operation. MulA extracts the posture spatial features of the target from a single frame image. And, the MulG module is designed based on three continuous GRUs (gated recurrent unit). Based on the MulA, MulG extracts the posture temporal features from the posture spatial features of the moving target and predicts the posture temporal features of the moving target. The paper firstly demonstrates the feasibility of addition operations in pose estimation tasks by comparing with MobileNet-v3 in ablation experiments. Secondly, on the HiEve and CrowdPose datasets, MulA achieves accuracy of 79.6% and 80.4%, respectively, outperforming the PTM model by 12.0% and 21.2%. And detection speed of MulA achieves the best value at 8.6ms, which is 1 times higher than HDGCN. The result demonstrates the effectiveness of MulA in multi-target pose estimation in complex scenes. Finally, on the HDMB-51 and UCF-101 datasets, MulAG achieves accuracy of 74.8% and 86.3%, respectively, outperforming HDGCN by 9.6% and 9.5%. Compared with SKP and GIST, the fps of MulAG (44.8s-1) is improved by 8.2% and 8.9%. These experiments highlight the generalizability and superiority of MulAG in behavior analysis and pose estimation tasks.

The purpose of single target tracking is to accurately and continuously locate a specific object when it is moving. However, when the objects encounter with fast movement, severe occlusion, too small size, and the same local features, the tracking algorithm which based on correlation filter or convolutional neural network will appear the positioning error phenomenon. Aiming at the above problems, this paper designs a single target tracking algorithm: relative temporal spatial network (RTSnet). RTSnet is a multi-thread network that composed of Relative temporal Information Network (RTInet) and Relative Spatial Information Network (RSInet). RTInet is designed on the basis of LSTM, and it has the predictable characteristics of temporal. It mainly obtains the relative temporal information between the frames before and after the target. RSInet, an improved twin network based on the Triplet Network, has the effect of similarity determination which can to obtain the spatial information between the frames before and after the target. In the experiments, the RTSnet is trained by using LASOT data set and verified by using the LASOT test set and the OTB100 data set. In the test set of LASOT, the accuracy of RTSnet reaches 85.5%, Trans-T reaches 62.3% and STMTrack reaches 57.4%. Meanwhile, its tracking speed reaches 117.3fps due to the RTSnet adopts dual-thread operation. On the OTB100 data-set, the accuracy of RTSnet is 81.1%.