The formation and development of networked air defense systems are not only necessary for adapting to anti air attack operations and responding to air and space threats， but also for promoting and guiding information technology and new combat theories. It is an inevitable choice for adapting to intelligent and networked operations of the system. As the critical basis for evaluating the success or failure of air defense operations， it has become an urgent task to assess the operational capability of air defense systems in the new mode. The article summarizes the research achievements in the field， analyzes the current research status， elaborates on the main problems faced， proposes the trend of networked operation of air defense systems， and suggests that the evaluation of air defense system combat capability has changed from “simple sum” to “emerging sum，” from “single result” to “result cloud，” and from “indicator tree” to “indicator network，” providing a theoretical reference for the evaluation of the combat capability of complex air defense systems with significant networked features in the future.

Modern defence requires intelligent perception algorithms to possess incremental and continuous learning capabilities in complex open scenarios， while traditional deep learning methods are based on closed training with the entire dataset， which limits their application ability and usage scope. Existing continuous learning algorithms face the problem of catastrophic knowledge forgetting. This paper proposes for the first time an incremental continuous target detection method based on the distillation of high-level semantic features， which guides the selection of high-value underlying features through high-level semantic features and distills the feature from the teacher model to the student model， thus effectively facilitating the transfer of knowledge of the old task and alleviating catastrophic knowledge forgetting. Experiments on the public image dataset MS COCO show that this method outperforms the previous best method for target detection in all types of continuous learning scenarios， which is expected to promote the generation of continuous learning capability and autonomous attempts of intelligent systems in open-world setting.

Multi-missile coordination is a hot research direction of missile weapons with high research value， and how to plan the flight trajectory of multiple missiles is an important research content. In this paper， a multi-ballistic trajectory simulation method based on a multi-objective evolutionary algorithm based on decomposition （MOEA/D） algorithm is proposed， which takes the glide phase of boost glide missile as the research object and aims at planning multiple high-quality trajectories. The maximum range and the maximum final velocity are selected as the objective functions to realize the trajectory planning of the glide phase. Meanwhile， in order to reduce the impact of the initial population on the multi-objective optimization problem， the pseudo-spectral method is used to provide high-quality initial populations for the multi-objective optimization algorithm. The simulation results show that the method can obtain multi-ballistic trajectories with optimal range and final velocity.