摘要
目前纯方位目标跟踪的滤波算法主要是UKF与EKF,它们都是最小均方差估计方法,其缺点是鲁棒性能差,易受观测噪声和目标不可观测的影响。为此提出了一种纯方位目标跟踪的鲁棒滤波算法。H∞滤波算法的准则是使由于最大的扰动引起的估计误差最小,因此其鲁棒性能好。由于修正坐标系纯方位跟踪的状态方程的非线性,采用了SUT(Scaled Unscented Transformation)线性化状态方程。通过把SUT和线性H∞滤波相结合,引出了SUT-H∞滤波算法。通过和UKF滤波算法对比,SUT-H∞滤波算法体现出了其良好的鲁棒性和稳定性。
This paper presented a robust filtering algorithm for bearings only tracking. Currently, the BOT filters are EKF and UKF, which aims to give the minimum mean-square estimate, and their disadvantages are poor robust performance, vulnerable to the impact of measurement noise and model uncertainty. The optimalH∞filter tries to minimize the effect of the worst possible disturbances on estimation of errors and hence it is more robust against model uncertainty. Because of the state equation of BOT in MPC are nonlinear, the Scaled Unscented Transformation （SUT） is used to linear this state equation. In this paper, the combination of SUT transformation and linear H∞fihering, leads to theSUT-H∞filtering algorithm. Comparing to the UKF, the SUT-H∞filter has shown better robustness and stability. KEYWORDS ： Filter ; Transform ; MPC ; BOT
出处
《计算机仿真》
CSCD
北大核心
2012年第7期388-392,共5页
Computer Simulation
基金
中国科学院知识创新工程重要方向项目(09A2040201)
