摘要
针对传统卡尔曼滤波(KF)及扩展卡尔曼滤波(EKF)在非线性目标跟踪模型中,跟踪精度较差的问题,本文给出了一种基于正则化粒子滤波(RPF)的水下目标跟踪算法。文中在一种模拟水下目标跟踪环境的非线性动态模型中对所提出的算法进行了仿真试验,并将其跟踪性能与扩展卡尔曼滤波和标准粒子滤波算法(PF)进行了比较。仿真结果表明,PF算法比EKF算法滤波精度更高,RPF的跟踪性能优于PF和RPF,而且随着粒子数的增加,PF和RPF的跟踪性能也不断提高。
In this paper, an underwater target tracking algorithm based on the regularization particle filtering is proposed to solve the problem that the tracking precision of the traditional kalman filter （KF） and the extended kalman filtering （EKF）is poor in nonlinear target tracking model. And an experiment is done to compare the EKF and the standard particle filtering （PF） on their tracking performance in a nonlinear dynamic model which simulates the underwater target tracking environment. The simulation results show that the filtering performance of PF is more accurate than EKF algorithm, but they are both poorer than RPF on tracking performance. With increasing of the number of particles, the tracking performance of PF and RPF become better.
出处
《电视技术》
北大核心
2012年第9期108-111,共4页
Video Engineering
基金
国家自然科学基金项目(61071211)
教育部博士点基金项目(20104101120011)
河南省青年骨干教师项目(2010GGJS-019)
关键词
水下目标跟踪
非线性
扩展卡尔曼滤波
粒子滤波
正则化粒子滤波
the underwater target tracking
nonlinear
the extended kalman filtering
particle filtering
regularization particle filtering
