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 carr...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.展开更多
在实测数据较少的情况下,采用何种模型能对GPS Block IIR(M)卫星钟差进行最佳预报?经研究发现,采用GM(1,1)-AR(p)复合模型进行1天短期预报的精度在1 ns之内,进行10天长期预报的精度在10 ns之内,这不仅优于二次多项式和GM(1,1)等传统钟...在实测数据较少的情况下,采用何种模型能对GPS Block IIR(M)卫星钟差进行最佳预报?经研究发现,采用GM(1,1)-AR(p)复合模型进行1天短期预报的精度在1 ns之内,进行10天长期预报的精度在10 ns之内,这不仅优于二次多项式和GM(1,1)等传统钟差预报模型,而且好于IGS(the International GPS Service for Geodynamics)提供的预报钟差7 ns的精度。展开更多
基金supported by the Postdoctoral Science Foundation of China(2013T60923)
文摘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.
文摘在实测数据较少的情况下,采用何种模型能对GPS Block IIR(M)卫星钟差进行最佳预报?经研究发现,采用GM(1,1)-AR(p)复合模型进行1天短期预报的精度在1 ns之内,进行10天长期预报的精度在10 ns之内,这不仅优于二次多项式和GM(1,1)等传统钟差预报模型,而且好于IGS(the International GPS Service for Geodynamics)提供的预报钟差7 ns的精度。
