基于机会约束规划的机会阵雷达功率资源管理算法

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。

Power resource management algorithm of opportunistic array radar based on chance constraint programming

The problem of power resource management of opportunistic digital array radar (ODAR) for multiple targets tracking is studied. For the uncertainty introduced by the complex and volatile environments and the unknown target information, a robust power resource management model of multiple targets based on random and fuzzy chance-constraint programming (CCP) is proposed. Random variables are introduced into the model to denote the total transmitting power and fuzzy variables are introduced to denote the target RCS, and the Bayesian Cramer Rao lower bound (BCRLB) is regarded as measurement criteria of target tracking accuracy. Afterwards, the random simulation and the fuzzy simulation are both embedded into the genetic algorithm (GA), and under the condition that the predetermined confidence level is satisfied, the optimal power allocation is predicted for all the targets minimizing the BCRLB of the next time, then in accordance with the optimal -power- allocation, the unscented Kalman filter (UKF) is used for targets tracking. Finally, the validity and robustness of the proposed algorithm are verified with the simulation results.