A probability hypothesis density filtering algorithm based on information association weighting is proposed to address the two issues of decreased accuracy in multi-target state estimation and overestimation of the number of multi-targets， which are caused by asynchronicity and high-density clutter aliasing in passive and active radar detection information across multiple platforms. Firstly， a multi-target tracking model is constructed， and the mechanism of why existing algorithms are susceptible to clutter is analyzed. Secondly， a multi-target tracking algorithm based on information association weighting is derived. The tolerance time parameter is set according to the target speed and tolerable error， and the detection information with a short asynchronous time is approximated as synchronous information. The association algorithm is used to select passive and active radar information from the same target， and the minimum variance weighted fusion is used to improve detection accuracy. The randomly distributed clutter is filtered out， due to its association difficulties caused by large differences in angle values. Finally， simulation analysis shows that the proposed algorithm improves the accuracy of multi-target state estimation and reduces the overestimation of the number of multi-targets compared to existing algorithms.

In 2022， profound changes took place in the world military landscape. The Russia-Ukraine conflict and the situation on the Korean Peninsula have not only affected the equipment layout of hot spots and countries but also prompted countries to think deeply and make dynamic adjustments to the building of future air defense and missile capabilities. The development of major military powers such as the United States and Russia in the field of air defense and missile defense in 2022 is investigated. The trend of defense system construction and technology development is analyzed. Inspiration and suggestions for the current development status of air defense and missile defense in China are put forward to provide references for the construction of complex air and space defense operation capabilities in the future.

In war， early warning and detection system as a typical complex system， the effective evaluation of its detection effectiveness has always been an important practical problem that needs to be studied and solved. The limitations of traditional effectiveness evaluation methods are break- throughed. On the basis of analyzing the basic principles of knowledge graph technology and Bayesian evaluation method， a new method of detection efficiency evaluation of early warning detection system based on their comprehensive application is proposed. The knowledge graph is constructed by using expert knowledge and experimental data， and the Bayesian network method is further used to implement knowledge reasoning， so as to realize the evaluation of system detection efficiency. The effectiveness of the proposed method by constructing specific combat scenarios is verified， and the evaluation of the proposed method based on deduced data is analyzed.