基于IMM-UKF的纯方位机动目标跟踪算法

针对在非线性机动目标跟踪中存在的滤波器易发散、跟踪误差大等问题,本文在多站纯方位跟踪的基础上,把Unscented卡尔曼滤波(Unscented Kalman filter,UKF)引进到交互多模型算法(Interacting multiple model,IMM)中,设计了交互多模型UKF滤波算法,克服了EKF中引入的较大线性化误差对机动目标跟踪算法性能的影响.最后将该算法与扩展卡尔曼滤波(EKF)、IMM-EKF算法进行了比较,仿真结果表明:IMM-UKF 算法增强了EKF滤波器的稳定性,提高了滤波收敛速度和跟踪精度.

Bearings-Only Tracking of Maneuvering Target Based on IMM-UKF Algorithm

In nonlinear maneuvering target tracking,filters are liable to diverge or have large tracking errors.To solve the problem,an interacting multiple model with unscented Kalman filter(IMM-UKF)algorithm is designed by introducing UKF into IMM algorithm based on bearings-only tracking for multi-stations.The algorithm is not affected by the linearization errot in extended Kalman filter(EKF)filter.Finally,the algorithm is compared with EKF,IMM-EKF algorithms.Simulations show that the IMM-UKF algorithm is more stable and effective,thus improving the convergence speed and tracking precision.