摘要
针对相控阵雷达多目标跟踪下的威胁度等级不同,以目标位置估计的贝叶斯克拉美罗下界(Bayesian Cramer-Rao lower bound,BCRLB)为分配准则,本文建立了一种基于威胁度的多目标跟踪时间资源分配优化模型,该模型以威胁度为基准将待跟踪目标分为两类,不同类别采用不同的时间资源分配方法。由于该模型及优化算法运行耗时巨大,该文还提出了一种基于反向传播(Back propagation,BP)神经网络的多目标跟踪时间资源拟合方法。计算机仿真表明,该模型及方法可以使各目标跟踪维持最佳状态,同时BP神经网络耗时降低2000多倍。
Aiming at the different threat levels under phased array radar multi-target tracking,the Bayesian Cramer-Rao lower bound(BCRLB)of the target position estimation is used as the allocation criterion.The paper establishes a multi-target tracking time resource allocation optimization model based on the threat degree.The model based on the threat degree to track the target can be divided into two categories and different types use different time resource allocation methods.Due to the time-consuming operation and optimization algorithm,this paper also proposes a multi-target tracking time resource fitting method based on back propagation(BP)neural network.Computer simulation shows that the model and the method can keep the target tracking in the best state,and the BP neural network reduces time consumption by more than two thousand times.
作者
陶庆
张劲东
陶庭宝
邱旦峰
TAO Qing;ZHANG Jindong;TAO Tingbao;QIU Danfeng(College of Electronic and Information Engineering,Nanjing University of Aeronautics and Astronautics,Nanjing 211106,China)
出处
《数据采集与处理》
CSCD
北大核心
2022年第1期217-227,共11页
Journal of Data Acquisition and Processing
基金
航空科学基金(2017052015,20182007001)
雷达成像与微波光子技术教育部重点实验室(南京航空航天大学)基金。
关键词
相控阵雷达
资源分配
贝叶斯克拉美罗下界
目标跟踪
神经网络
phased array radar
resource allocation
Bayesian Cramer-Rao lower bound(BCRLB)
target tracking
neural network
