复杂环境下基于动态贝叶斯网络的目标识别

为了提升高动态复杂电磁环境下空战过程中对目标的识别能力,针对SBN网络模型无法满足战场的动态性要求以及对目标的经常性误识别问题,设计了一种基于变结构动态贝叶斯网络的目标类型识别模型。该模型是由静态贝叶斯网络模型演变而来,具有良好的动态表达性和滤波功能,弥补了SBN的不足,并且对空战过程中目标特征信息丢失的问题有良好的容错能力。仿真结果表明,基于动态贝叶斯网络的目标识别的识别效果,优于基于参数学习贝叶斯网络的目标识别。使用该模型后目标识别的准确性提高了5%,有效地解决目标类型识别过程中数据缺失和信息不足的问题。

Target Recognition Based on Dynamic Bayesian Networks under High Dynamic and Complex Conditions of Environment

Aimed at the problem that the SBN network model fails to meet the requirements of the dynamic performance and regularly and accidentally mistake target recognition, a new target recognition model is designed based on variable structure dynamic Bayesian network to improve the capability of target recognition under high dynamic and complex electromagnetic conditions of environment. This modified model is developed by Static Bayesian Network model, has a good dynamic expression and filtering function, makes up for the lack of SBN, and has a good fault tolerance capability. The simulation results show that the effect of target recognition based on dynamic Bayesian networks is better than that of target fused recognition based on parameter learning Bayesian. The accuracy of target identification and the stability of the algorithm are significantly improved. By so doing, the model effectively solves the problem of missing data and information in the process of target identification.