A guided Minimax-DDQN （Minimax-Double Deep Q-Network） algorithm was designed to solve the problems of unpredictable enemy aircraft maneuver strategy and low winning rate， which are caused by the complex environment information and strong confrontation of Unmanned Aerial Vehicle （UAV） in air combat. Firstly， on the basis of Minimax decision-making method， a guided strategy exploration mechanism was proposed. Then， combined with the guided Minimax strategy， a type of DDQN （Double Deep Q-Network） algorithm was designed to improve the update efficiency of Q-network. Finally， an advanced three-stage network training method was proposed. And through confrontation training between different decision models， better optimized decision model was obtained. Experimental results show that compared with Minimax-DQN （Minimax-DQN）， Minimax-DDQN and other algorithms， the proposed algorithm has the success rate of chasing straight target improved by 14% to 60% and the winning rate against DDQN algorithm over 60%. It can be seen that compared with algorithms such as DDQN and Minimax-DDQN， the proposed algorithm has stronger decision-making capability and better adaptability in high confrontation combat environment.

Existing evidential reasoning methods have fixed model structure， single information processing mode and reasoning mechanism， making these methods difficult to be applied to target identification in an environment with a variety of incomplete information such as uncertain， error and missing information. To address this problem， a Switching Reasoning Evidential Network （SR-EN） method was proposed. Firstly， a multi-template network model was constructed considering evidence-node deletion and other situations. Then， conditional correlation between each evidence variable and target type was analyzed to establish an reasoning rule base for incomplete information. Finally， an intelligent spatio-temporal fusion reasoning method based on three evidence input and correction methods was proposed. Compared with traditional Evidential Network （EN） and combination methods of two information correction methods， such as EN and Technique for Order Preference by Similarity to an Ideal Solution （TOPSIS）， SR-EN can achieve continuous and accurate identification for aerial targets under multiple types of random incomplete information while ensuring reasoning timeliness. Experimental results show that SR-EN can realize adaptive switching of evidence processing methods， network structures and fusion rules among nodes in continuous reasoning process through effective identification of various types of incomplete information.

To address the problem that the existing algorithm uses synchronous manual optimization of deep learning networks， and ignores the negative information of network learning， which leads to a large number of redundant parameters or even overfitting， thereby affecting the counting accuracy， a parameter asynchronous updating algorithm based on Multi-column Convolutional Neural Network （MCNN） was proposed. Firstly， a single frame image was input to the network， and after three columns of convolutions to extracting features with different scales respectively， the correlation of every two columns of feature maps was learned through the mutual information between columns. Then， the parameters of each column were updated asynchronously according to the optimized mutual information and the updated loss function until the algorithm converges. Finally， the dynamic Kalman filtering was used to deeply fuse the output density maps output by the columns， and all pixels in the fused density map were summed up to obtain the total number of people in the image. Experimental results show that on the UCSD （University of California San Diego） dataset， the Mean Absolute Error （MAE） of the proposed algorithm is 1.1% less than that of ic-CNN+McML （iterative crowd counting Convolution Neural Network Multi-column Multi-task Learning） with the best MAE performance on the dataset， and the Mean Square Error （MSE） of the proposed algorithm is 4.3% less than that of Contextual Pyramid Convolution Neural Network （CP-CNN） with the best MSE performance on the dataset； on the ShanghaiTech Part_A dataset， the MAE of the proposed algorithm is reduced by 1.7% compared to that of ic-CNN+McML with the best MAE performance on the dataset， and the MSE of the proposed algorithm is reduced by 3.2% compared to that of ACSCP （Adversarial Cross-Scale Consistency Pursuit）with the best MSE performance on the dataset； on the ShanghaiTech Part_B dataset， the proposed algorithm has the MAE and MSE reduced by 18.3% and 35.2% respectively compared to ic-CNN+McML with the best MAE and MSE performances on the dataset； on the UCF_CC_50 （University of Central Florida Crowd Counting） dataset， the proposed algorithm has the MAE and MSE reduced by 1.9% and 9.8% respectively compared to ic-CNN+McML with the best MAE and MSE performances on the dataset. The above shows that this algorithm can effectively improve the accuracy and robustness of crowd counting， and allows the input image to have any size or resolution， and can adapt to the large-scale transformation of the detected target.