基于机会约束规划的机会阵雷达功率资源管理算法

发布时间：2018-04-23 06:41

本文选题：机会数字阵列雷达 + 机会约束规划　；参考：《系统工程与电子技术》2017年03期

【摘要】：研究了在进行多目标跟踪时机会数字阵列雷达(opportunistic digital array radar,ODAR)的功率资源管理问题。针对复杂多变的环境和未知的目标信息所导致的不确定性,建立了基于随机和模糊机会约束规划(chance-constraint programming,CCP)的多目标稳健功率资源管理模型。模型引入随机变量表征雷达总发射功率,引入模糊变量表征每个目标的RCS,以贝叶斯克拉美罗界(Bayesian Cramer Rao lower bound,BCRLB)作为目标跟踪精度的衡量标准,将随机模拟和模糊模拟都嵌入到遗传算法(genetic algorithm,GA)当中,从而预测出下一时刻满足给定置信水平的各目标最优的功率分配,然后根据求解出来的功率分配情况,利用无迹卡尔曼滤波器(unscented Kalman filter,UKF)进行目标跟踪。最后,通过仿真实验验证了算法的有效性和稳定性。
[Abstract]:The problem of power resource management for opportunistic digital array radar (ODAR) in multi target tracking is studied. The multiple targets based on random and fuzzy chance constrained programming (chance-constraint programming, CCP) are established for the uncertainty caused by complex and changeable environment and unknown target information. A robust power resource management model is introduced. The model introduces random variables to characterize the total radar transmission power, introduces fuzzy variables to characterize the RCS of each target, and takes Bayesian Cramer Rao lower bound, BCRLB as the criterion of target tracking accuracy. The stochastic simulation and fuzzy simulation are embedded into the genetic algorithm (genetic algo). Rithm, GA), so as to predict the optimal power allocation for the target at the next time to meet the level of the set, and then use the unscented Kalman filter (UKF) to track the target according to the power distribution of the solution. Finally, the effectiveness and stability of the algorithm are verified through the simulation experiment.

【作者单位】：南京航空航天大学电子信息工程学院;南京电子技术研究所;
【基金】：国家自然科学基金(61271327) 中国博士后基金(2015M580426) 江苏省博士后基金(1501056B)资助课题
【分类号】：TN958

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