摘要
针对非高斯、强噪声背景下的高机动目标实施跟踪时,卡尔曼滤波、扩展卡尔曼滤波等算法将出现滤波精度下降甚至发散现象。粒子滤波方法作为一种基于贝叶斯估计的非线性滤波算法,在处理非高斯非线性时变系统的参数估计和状态滤波问题方面有独到的优势。以目标跟踪问题为背景,将粒子滤波与卡尔曼滤波算法进行了对比研究。
When the objects are in the background of higher maneuvering, multi-model, non-Gaussian, strong noise, the algorithms of Kalman filter and extended Kalman filter within the Gaussian background leads to the filter precision decrease and divergence phenomenon. As a nonlinear filter algorithm based on Bayesian estimation, particle filter has original advantage at treating the parameter estimation and state filtering aspects of nonlinear non-Gaussian time-varying systems. Thus a great development is obtained. With the background of object tracking, this paper focuses on the particle filter and compares it with the Kalman filter.
出处
《信息技术》
2012年第8期127-130,共4页
Information Technology
关键词
目标跟踪
粒子滤波
卡尔曼滤波
object tracking
particle filter
Kalman filter
