模型集自适应的交互多模型辅助粒子滤波算法

Adaptive Model Set Interacting Multiple Model Auxiliary Particle Filter Algorithm

  • 摘要: 为了提高机动目标的跟踪精度,提出一种基于目标转弯率模型的模型集自适应交互多模型辅助粒子滤波算法(AMSIMMAPF). 采用转弯率模型实时辨识目标的角速度,根据辨识到的角速度来更新交互多模型的模型集. 利用辅助粒子滤波可以避免粒子权值退化、样本衰减,不受线性模型高斯噪声限制的特点,各模型滤波选用辅助粒子滤波算法以提高跟踪精度. 理论分析和仿真结果表明,与交互多模型粒子滤波算法相比,本算法具有跟踪精度高,计算量小的特点.

     

    Abstract: To improve tracking accuracy, an adaptive model set interacting multiple model auxiliary particle filter (AMSIMMAPF)algorithm is presented, based on the turning model. The instant angular velocity is identified by the turning model and the model set is updated by this angle. The auxiliary particle filter can avoid weight degeneracy, sample the impoverishment and not restricted by assumptions of linearity or Gaussian noise. So it is selected in the respective model to improve its accuracy. The results of theoretical analysis and simulation verify that the algorithm has more precise filter result and less computation load, when compared with the interacting multiple model particle filter (IMMPF)algorithm.

     

/

返回文章
返回
Baidu
map