空间光学技术研究室知识库

For the problems of the loss of observation data, the hardness on overall consideration of dynamic threat situation of the coming target, the need of confirming index weights of most existing methods and too much rely on expertise, the AR(p) dynamic catastrophe ranking method of target threat assessment is proposed by combining the AR(p) predicting model, time series weight and catastrophe theory. The AR(p) model is used to predict the missing target data on time series, which provides the basic data for threat assessment using all times' data. Time series weight is gained by the inverse form of the Poisson distribution, and the target associating information of the current time and the former time is comprehensively considered. The core thoughts of catastrophe ranking is abstracted based on the catastrophe theory, and the function of sub layer indexes on its upper layer indexes in the target threat assessment index system is attributed to the function of control variable on state variable. The degree of this function is solved by the normalization formulae, and the target threat assessment value is acquired through the bottom layer to the top layer according to this mode. The experiment result indicates that the AR(p) dynamic catastrophe ranking method can be used for target threat assessment under the loss of target data, which is more close to the actual combat. It is unnecessary to compute the weights of all indexes, which is concise, efficient, and easy to implement. In addition, the assessment result is more reasonable and has a certain application value due to the comprehensive consideration of the dynamic threat situation of targets. © 2017, Editorial Office of Systems Engineering and Electronics. All right reserved.