• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Science, Measurement & Technology, IET >Target tracking algorithm based on adaptive strong tracking particle filter
【24h】

Target tracking algorithm based on adaptive strong tracking particle filter

机译：基于自适应强跟踪粒子滤波的目标跟踪算法

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

The primary problem of tracking filtering algorithms is the tracking stability and effectiveness of target states. Based on the particle filter, an adaptive strong tracking particle filter algorithm is proposed in this study. According to the residual between actual measurement values and predicted measurement values of every moment, adjustment of the forgetting factor and the weakening factor is adaptively conducted. Then, by calculating the fading factor, transfer covariance matrix and filter gain of the system are obtained to estimate the particles state value. Updating the importance density function can alleviate the degradation phenomenon of particle filter, and it contributes to effective estimation for the optimal state value of a target. The simulation results demonstrate that the proposed algorithm provides a better tracking precision. In addition, when the target states make mutations, the proposed algorithm can track the mutation states of moving targets effectively and improve the stability of the system.
机译：跟踪滤波算法的主要问题是目标状态的跟踪稳定性和有效性。提出了一种基于粒子滤波的自适应强跟踪粒子滤波算法。根据每个时刻的实际测量值和预测测量值之间的残差，自适应地进行遗忘因子和弱化因子的调整。然后，通过计算衰落因子，获得系统的传递协方差矩阵和滤波器增益，以估计粒子状态值。更新重要性密度函数可以减轻粒子滤波器的退化现象，并且有助于有效地估计目标的最佳状态值。仿真结果表明，该算法具有较好的跟踪精度。另外，当目标状态发生突变时，该算法可以有效地跟踪运动目标的突变状态，提高了系统的稳定性。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号