随着当今无线通信产业的蓬勃发展,车联网(Internet of Vehicles,IoV)作为其应用场景之一,受到了科研工作者的广泛关注,而保证信息传递的安全性是IoV系统函待解决的诸多难题之一。文章结合了Nakagami-m衰落环境下的信道特性和IoV现实道...随着当今无线通信产业的蓬勃发展,车联网(Internet of Vehicles,IoV)作为其应用场景之一,受到了科研工作者的广泛关注,而保证信息传递的安全性是IoV系统函待解决的诸多难题之一。文章结合了Nakagami-m衰落环境下的信道特性和IoV现实道路场景,考虑了车辆与基站之间的通信,即根据车辆终端位置的随机分布特性,建立了拥有一个发射基站、一个合法车辆终端和一个窃听者的系统模型,并求解了Nakagami-m衰落信道下的安全中断概率(Secrecy Outage Probability,SOP)的闭式解析表达式。最后,通过蒙特卡洛仿真与数值分析的方法验证了文中所建立的分析模型的正确性,并探讨分析了该模型在Nakagamim衰落环境中的安全性能和影响因素。展开更多
On the assumption that random interruptions in the observation process are modeled by a sequence of independent Bernoulli random variables, we firstly generalize two kinds of nonlinear filtering methods with random in...On the assumption that random interruptions in the observation process are modeled by a sequence of independent Bernoulli random variables, we firstly generalize two kinds of nonlinear filtering methods with random interruption failures in the observation based on the extended Kalman filtering （EKF） and the unscented Kalman filtering （UKF）, which were shortened as GEKF and CUKF in this paper, respectively. Then the nonlinear filtering model is established by using the radial basis function neural network （RBFNN） prototypes and the network weights as state equation and the output of RBFNN to present the observation equation. Finally, we take the filtering problem under missing observed data as a special case of nonlinear filtering with random intermittent failures by setting each missing data to be zero without needing to pre-estimate the missing data, and use the GEKF-based RBFNN and the GUKF-based RBFNN to predict the ground radioactivity time series with missing data. Experimental results demonstrate that the prediction results of GUKF-based RBFNN accord well with the real ground radioactivity time series while the prediction results of GEKF-based RBFNN are divergent.展开更多
文摘随着当今无线通信产业的蓬勃发展,车联网(Internet of Vehicles,IoV)作为其应用场景之一,受到了科研工作者的广泛关注,而保证信息传递的安全性是IoV系统函待解决的诸多难题之一。文章结合了Nakagami-m衰落环境下的信道特性和IoV现实道路场景,考虑了车辆与基站之间的通信,即根据车辆终端位置的随机分布特性,建立了拥有一个发射基站、一个合法车辆终端和一个窃听者的系统模型,并求解了Nakagami-m衰落信道下的安全中断概率(Secrecy Outage Probability,SOP)的闭式解析表达式。最后,通过蒙特卡洛仿真与数值分析的方法验证了文中所建立的分析模型的正确性,并探讨分析了该模型在Nakagamim衰落环境中的安全性能和影响因素。
基金Project supported by the State Key Program of the National Natural Science of China (Grant No. 60835004)the Natural Science Foundation of Jiangsu Province of China (Grant No. BK2009727)+1 种基金the Natural Science Foundation of Higher Education Institutions of Jiangsu Province of China (Grant No. 10KJB510004)the National Natural Science Foundation of China (Grant No. 61075028)
文摘On the assumption that random interruptions in the observation process are modeled by a sequence of independent Bernoulli random variables, we firstly generalize two kinds of nonlinear filtering methods with random interruption failures in the observation based on the extended Kalman filtering （EKF） and the unscented Kalman filtering （UKF）, which were shortened as GEKF and CUKF in this paper, respectively. Then the nonlinear filtering model is established by using the radial basis function neural network （RBFNN） prototypes and the network weights as state equation and the output of RBFNN to present the observation equation. Finally, we take the filtering problem under missing observed data as a special case of nonlinear filtering with random intermittent failures by setting each missing data to be zero without needing to pre-estimate the missing data, and use the GEKF-based RBFNN and the GUKF-based RBFNN to predict the ground radioactivity time series with missing data. Experimental results demonstrate that the prediction results of GUKF-based RBFNN accord well with the real ground radioactivity time series while the prediction results of GEKF-based RBFNN are divergent.
