基于粒子滤波的弹道目标跟踪  

Ballistic object tracking based on particle filter

作　　者：徐长爱[1] 李尚生[1] 殷勇[1] 刘军[1] 

机构地区：[1]海军航空工程学院电子信息工程系,山东烟台264001

出　　处：《电光与控制》2008年第10期81-83,96,共4页Electronics Optics & Control

摘　　要：弹道目标再入段的运动受到空气阻力、重力等力的影响,具有明显的非线性特征。传统的卡尔曼滤波是线性、高斯问题的最优滤波器,但无法处理非线性的估计问题。扩展卡尔曼滤波利用泰勒级数展开把非线性方程线性化,是解决非线性估计问题的有效算法;而近些年来出现的粒子滤波以其解决非线性问题的卓越性能,得到了迅速发展。文章对弹道目标再入段的运动特征进行研究,建立了目标的状态空间模型,并应用扩展卡尔曼滤波和粒子滤波实现了对弹道目标的跟踪。通过比较仿真结果,证明粒子滤波比扩展卡尔曼滤波精度更高,对噪声的抑制能力更强,也更稳定,因而具有重大的研究意义。The reentry phase movement of a ballistic object is obvious nonlinear because of the effects from gravity and resistance of the air. Traditional Kalman filter is an optimum filter for linear or Gaussian distribution problem,which is not suitable for nonlinear evaluation issues. Extend Kalman Filter （EKF） can resolve nonhnear problems effectively by converting a nonlinear equation into a linear one through Taylor series expansion. Particle Filter （PF） appeared recently has prominent performance in dealing with nonlinear problems. Based on the study of ballistic object motion at reentry phase, a state-space model is established. PF and EKF are used in implementing the ballistic object tracking. Analysis to the simulation result shows that PF is more accurate than EKF, has stronger noise suppressing capability and higher stability.

关 键 词：目标跟踪 弹道目标 粒子滤波 扩展卡尔曼滤波 

分 类 号：V271.4[航空宇航科学与技术—飞行器设计] TN953.6[电子电信—信号与信息处理]