机载雷达组网多站测角被动跟踪轨迹优化研究

研究多观测器轨迹优化控制问题,由于多站测角被动跟踪系统运行存在误差,用机载雷达组网的可移动传感器采集信息,可对雷达载体轨迹优化进行研究,利用控制雷达载体的飞行轨迹可有效解决跟踪目标的弱观测性及估计器的稳定性。为了改善传统轨迹优化算法容易陷入早熟收敛和局部最小的问题,提出一种模拟退火(Simulated Annealing,SA)和粒子群优化(Particle Swarm Optimization,PSO)算法的混合优化方法(SA-PSO)。在给出了角度信息的适应度函数表达式基础上,结合模拟退火算法的局部搜索能力和粒子群优化算法的全局搜索能力,提高优化算法的收敛速度、精度以及全局搜索能力。实验证明,改进的混合算法对雷达载体轨迹优化有效,并减小对机动目标的被动跟踪误差。

Trajectory Optimization for Multi-Observers Based on Hybrid Optimization Algorithm

This paper focused on observers trajectory optimization and control.For the errors of multi-stations passive localization and tracking system,the problem of trajectory optimization for radars was studied by the information collected with moving sensors of airborne radar network.The degree of observability of target tracking and estimation performance of filter was improved through controlling the trajectory of radars in the network.For the traditional trajectory optimization algorithm often traps easily into premature convergence and local minimum,a hybrid optimization method based on simulated annealing(SA) and particle swarm optimization(PSO) algorithm was proposed to solve the problem.Based on the fitness function with angle measurement,this method combined the local search ability of SA algorithm with the global search ability of PSO algorithm to improve algorithm convergence speed,accuracy and global searching capability.Simulation result shows that the improved hybrid algorithm is effective to radars trajectory optimization and improves the tracking accuracy of the maneuvering target.