Due to the lack of altitude information,aircraft targets can not be made track association by the distributed 2D radar network.A novel track association algorithm for 2D and 2D radar is proposed.The tracks coming from two radars belong to different places are united based on geometrical features of radars and target,and association function is created.The linear relationship between association function and radar measurement noise is built with the first-order of Taylor expansion of the association function,and the association threshold is obtained according to the confidence interval of measurement noise.The association cost matrix based on historical information is established after rough track association,and precise track association is realized through optimal assignment with Hungary algorithm.Simulation results demonstrate that the proposed method can solve the track association in distributed 2D radar network effectively,and be of certain practical application value.

Due to the complex environment of international peacekeeping missions,numerous equipment support has become a key point affecting the achievement of the mission.Strengthening the capacity training system of the support people is a guarantee for effectively carrying out overseas equipment support tasks.Using a variety of simulation,virtual reality and other technologies,with the help of Internet,satellite and other information transmission platforms,a comprehensive simulation training system can be built,which can effectively solve the difficulties on comprehensiveness,sustainability,accessibility and economy.After analyzing the requirements of people training to implement a peacekeeping mission,the overall design of the integrated simulation training system is developed,including system overall architecture,composition,technical architecture, information interaction,etc.The key technologies are refined and analyzed.A comprehensive and effective solution for improving the effectiveness of international peacekeeping equipment support training and buildingis provided.

Facing the problems of complex types of peacekeeping equipment,complex and harsh peacekeeping environment,and prominent contradiction in support maintenance,it is necessary to strengthen the demand prediction of spare parts for peacekeeping equipment support before the start of the mission.Firstly,the characteristics of peacekeeping mission are analyzed,and the concept of peacekeeping equipment support is put forward.Secondly,the steps of peacekeeping equipment support demand prediction are proposed,and the prediction model of peacekeeping equipment support based on particle swarm optimization (PSO)-back propagation (BP) neural network is studied.Finally,the effectiveness of the algorithm is verified by experiments based on actual data.The experimental results show that the prediction model of peacekeeping equipment support based on PSO-BP neural network can effectively predict the demand for spare parts of peacekeeping equipment support and improve the efficiency of peacekeeping equipment support.