参数自适应动态贝叶斯舰船态势估计算法
|
摘要
为减少动态海域下贝叶斯网络舰船态势估计算法较大的误差,提出了一种改进的动态贝叶斯网络舰船态势估计算法。该算法根据多路传感器的数据和新获取的态势信息进行推理,通过计算新态势要素与原有态势要素间的互信息构建并更新动态贝叶斯网络参数。与传统贝叶斯网络态势估计算法对比,在仿真情况下对10 000艘舰船进行合作态势估计,改进动态贝叶斯网络的舰船态势估计算法合作舰船错误率降低了7.1%;用实测数据,目标的合作态势提升了4.2%。改进的算法不仅能够实时地反映舰船环境变化,同时还提高了目标态势估计的准确概率,为海监提供了一种舰船态势分析与决策的技术支持。
In order to reduce the error of the Bayesian network algorithm for the assessment of ship situation in the dynamic area of the ocean,an improved algorithm for the assessment of the ship situation is proposed based on the dynamic Bayesian network.The algorithm makes an inference based on the data from multiple sensors and newly acquired situation information.By calculating the mutual information between new situational elements and original situational elements,the dynamic Bayesian network parameters are constructed and updated.Compared with the model of the traditional Bayesian Network,the error rate of the cooperative target of the ship reduces by 7.1%through simulation of about 10,000 ships.By using the improved dynamic Bayesian network algorithm for the assessment of ship situation,under the measured data,the cooperation of situation for the target has increased by 4.2%.The algorithm proposed in this paper not only reflects the environment of ship changes in real time,but also improves the accuracy of the target situation,thus providing a technical support for analysis and decision-making of the situation of ships for Marine Surveillance.
In order to reduce the error of the Bayesian network algorithm for the assessment of ship situation in the dynamic area of the ocean,an improved algorithm for the assessment of the ship situation is proposed based on the dynamic Bayesian network.The algorithm makes an inference based on the data from multiple sensors and newly acquired situation information.By calculating the mutual information between new situational elements and original situational elements,the dynamic Bayesian network parameters are constructed and updated.Compared with the model of the traditional Bayesian Network,the error rate of the cooperative target of the ship reduces by 7.1%through simulation of about 10,000 ships.By using the improved dynamic Bayesian network algorithm for the assessment of ship situation,under the measured data,the cooperation of situation for the target has increased by 4.2%.The algorithm proposed in this paper not only reflects the environment of ship changes in real time,but also improves the accuracy of the target situation,thus providing a technical support for analysis and decision-making of the situation of ships for Marine Surveillance.
引文
[1]孙庆鹏,李战武,常一哲.三维威力场在空战态势评估模型中的应用[J].空军工程大学学报(自然科学版),2018,19(1):37-42.SUN Qingpeng,LI Zhanwu,CHANG Yizhe.The Air Combat Situation Assessment Model Based on Three-dimensional Combat Power Field[J].Journal of Air Force Engineering University:Natural Science Edition,2018,19(1):37-42.
[2]任佳,崔亚妮,张婧.基于时变DBN的UAV航路威胁评估模型[J].计算机仿真,2014,31(6):60-64.REN Jia,CUI Yani,ZHANG Jing.Threat Assessment Model of UAV Path-planning Base on Time-varying Dynamic Bayesian Network[J].Computer Simulation,2014,31(6):60-64.
[3]孟光磊,马晓玉,刘昕,等.基于混合动态贝叶斯网的无人机空战态势评估[J].指挥控制与仿真,2017,39(4):1-6.MENG Guanglei,MA Xiaoyu,LIU Xin,et al.Situation Assessment for Unmanned Aerial Vehicles Air Combat Based on Hybrid Dynamic Bayesian Network[J].Command Control and Simulation,2017,39(4):1-6.
[4]梁洪泉,吴巍.利用节点可信度的安全链路状态路由协议[J].西安电子科技大学学报,2016,43(5):121-127.LIANG Hongquan,WU Wei.Secure Link Status Routing Protocol Based on Node Trustworthiness[J].Journal of Xidian University,2016,43(5):121-127.
[5]XU X M,YANG R,YU Y.Situation Assessment for Air Combat Based on Novel Semi-supervised Naive Bayes[J].Journal of Systems Engineering and Electronics,2018,29(4):768-779.
[6]ONTANON S,MONTANA J L,GONZALEZ A J.A Dynamic-Bayesian Network Framework for Modeling and Evaluating Learning from Observation[J].Expert Systems with Applications,2014,41(11):5212-5226.
[7]LIU H,ZHOU S,LAM W,et al.A New Hybrid Method for Learning Bayesian Networks:Separation and Reunion[J].Knowledge-Based Systems,2017,121:185-197.
[8]景国彬,张云骥,孙光才,等.一种三维地面场景SAR回波仿真的快速实现方法[J].西安电子科技大学学报,2017,44(3):1-7.JING Guobin,ZHANG Yunji,SUN Guangcai,et al.Fast Method for SAR Echo Simulation of a Three-dimensional Ground Scene[J].Journal of Xidian University,2017,44(3):1-7.
[9]邸若海,高晓光.基于限制型粒子群优化的贝叶斯网络结构学习[J].系统工程与电子技术,2011,33(11):2423-2427.DI Ruohai,GAO Xiaoguang.Bayesian Network Structure Learning Based on Restricted Particle Swarm Optimization[J].Systems Engineering and Electronics,2011,33(11):2423-2427.
[2]任佳,崔亚妮,张婧.基于时变DBN的UAV航路威胁评估模型[J].计算机仿真,2014,31(6):60-64.REN Jia,CUI Yani,ZHANG Jing.Threat Assessment Model of UAV Path-planning Base on Time-varying Dynamic Bayesian Network[J].Computer Simulation,2014,31(6):60-64.
[3]孟光磊,马晓玉,刘昕,等.基于混合动态贝叶斯网的无人机空战态势评估[J].指挥控制与仿真,2017,39(4):1-6.MENG Guanglei,MA Xiaoyu,LIU Xin,et al.Situation Assessment for Unmanned Aerial Vehicles Air Combat Based on Hybrid Dynamic Bayesian Network[J].Command Control and Simulation,2017,39(4):1-6.
[4]梁洪泉,吴巍.利用节点可信度的安全链路状态路由协议[J].西安电子科技大学学报,2016,43(5):121-127.LIANG Hongquan,WU Wei.Secure Link Status Routing Protocol Based on Node Trustworthiness[J].Journal of Xidian University,2016,43(5):121-127.
[5]XU X M,YANG R,YU Y.Situation Assessment for Air Combat Based on Novel Semi-supervised Naive Bayes[J].Journal of Systems Engineering and Electronics,2018,29(4):768-779.
[6]ONTANON S,MONTANA J L,GONZALEZ A J.A Dynamic-Bayesian Network Framework for Modeling and Evaluating Learning from Observation[J].Expert Systems with Applications,2014,41(11):5212-5226.
[7]LIU H,ZHOU S,LAM W,et al.A New Hybrid Method for Learning Bayesian Networks:Separation and Reunion[J].Knowledge-Based Systems,2017,121:185-197.
[8]景国彬,张云骥,孙光才,等.一种三维地面场景SAR回波仿真的快速实现方法[J].西安电子科技大学学报,2017,44(3):1-7.JING Guobin,ZHANG Yunji,SUN Guangcai,et al.Fast Method for SAR Echo Simulation of a Three-dimensional Ground Scene[J].Journal of Xidian University,2017,44(3):1-7.
[9]邸若海,高晓光.基于限制型粒子群优化的贝叶斯网络结构学习[J].系统工程与电子技术,2011,33(11):2423-2427.DI Ruohai,GAO Xiaoguang.Bayesian Network Structure Learning Based on Restricted Particle Swarm Optimization[J].Systems Engineering and Electronics,2011,33(11):2423-2427.