The research on the emergence of swarm intelligence plays an important role in people's understanding of the laws of nature， optimizing social management， and promoting knowledge innovation. The air and space defense system provides a new scene for the research and application of swarm intelligence. However， the aerospace defense system is a typical nonlinear system， and the controllable emergence of the system faces problems such as exponential explosion of state space， complex dynamic process model， and difficult performance analysis. Aiming at the battlefield defense scenario， this paper carries out the research on the controllable emergence mechanism of intelligent defense system for large-scale target response， complex task scenario adaptation， multi-task coordination and cooperation， and complex system design faced by the air and space defense system， explores the emergence mechanism of the air and space defense system， and provides a new perspective for improving the multi-target response capability， rapid scenario adaptation ability and effective coordination and cooperation among the elements of the air and space defense system.

The paper makes a quantitative analysis on the range walk of high-speed target and its influence on signal-to-noise ratio in interrupted continuous wave dechirp system. The analysis results show that the difference frequency signal when range walk occurs is a LFM（linear frequency modulation） signal. Aiming at this phenomenon， a time-domain inversion compression compensation method is proposed， which uses the time-frequency characteristics of the signal to generate a time-reversed signal in the time domain， which is conjugated and multiplied with the original signal to remove the frequency modulation slope and realize signal compression. At the same time， a quadratic matched filter compression method is proposed. Based on the speed search information， the frequency modulation slope of the difference frequency signal during range walking is calculated， and the matched filter with the opposite slope is generated. The range walking is eliminated by quadratic matched filtering. It can eliminate the influence of distance walking of high-speed targets， improve the detection power and detection accuracy， and can be applied to the declination system interrupt CW detection radar， which has a good reference significance in the field of high-speed target detection.

Multi-agent cooperation and competition has the characteristics of real-time and action continuity， incomplete information， huge search space， multi-complex tasks and time-space inference， etc. It is one of the most challenging problems in the current artificial intelligence field. Aiming at the problem of long training time for large-scale multi-agent reinforcement learning， this paper proposes an Actor-Critic-based cooperative confrontation framework， which uses meta curriculum reinforcement learning method to extract meta-models of basic tasks for small-scale scenarios. We carry out model migration to large-scale scenarios based on the curriculum learning， which continues training based on the meta-models and finally obtains a better collaboration strategy. This paper conducts simulation experiments on the "Star-Craft II" platform. The results show that the multi-agent cooperative confrontation technology based on the meta curriculum reinforcement learning can effectively accelerate the training process， and can achieve a higher win rate within a shorter time compared with the traditional training methods. The training speed is increased by about 40%. This method can effectively support the efficient generation of multi-agent cooperative confrontation strategies.