Journal of Systems Engineering and Electronics ›› 2019, Vol. 30 ›› Issue (2): 297-307.doi: 10.21629/JSEE.2019.02.09

• Systems Engineering • Previous Articles     Next Articles

Threat evaluation method of warships formation air defense based on AR(p)-DITOPSIS

Haiwen SUN*(), Xiaofang XIE()   

  • Received:2017-06-17 Online:2019-04-01 Published:2019-04-28
  • Contact: Haiwen SUN E-mail:842904820@qq.com;xiexf@yahoo.com.cn
  • About author:SUN Haiwen was born in 1990. He received his B.S. and M.S. degrees from Naval Aeronautical and Astronautical University (NAAU), Yantai, China in 2013 and 2016 respectively. He is currently pursuing his Ph.D. degree at the Coastal Defense College of Naval Aviation University. His main research interests are modeling and simulation of weapon systems. E-mail:842904820@qq.com|XIE Xiaofang was born in 1962. He is a professor and Ph.D. supervisor of the Coastal Defense College of Naval Aviation University. His current research interests are intelligent the design of weapon system and naval gun antimissile system. E-mail:xiexf@yahoo.com.cn
  • Supported by:
    the Postdoctoral Science Foundation of China(2013T60923);This work was supported by the Postdoctoral Science Foundation of China (2013T60923)

Abstract:

For the target threat evaluation of warships formation air defense, the sample data are frequently insufficient and even incomplete. The existing evaluation methods rely too much on expertise and are difficult to carry out for the dynamic evaluation on time series. In order to solve these problems, a threat evaluation method based on the AR(p) (auto regressive (AR))-dynamic improved technique for order preference by similarity to ideal solution (DITOPSIS) method is proposed. The AR(p) model is adopted to predict the missing data on the time series. Then, the entropy weight method is applied to solve each index weight at the objective point. Kullback-Leibler divergence (KLD) is used to improve the traditional TOPSIS, and to carry out the target threat evaluation. The Poisson distribution is used to assign the weight value. Simulation results show that the improved AR(p)-DITOPSIS threat evaluation method can synthetically take into account the target threat degree in time series and is more suitable for the threat evaluation under the condition of missing the target data than the traditional TOPSIS method.

Key words: AR(p) model, Kullback-Leibler divergence (KLD), dynamic improved technique for order preference by similarity to ideal solution (DITOPSIS), time series, threat evaluation