A modified unscented particle filtering scheme for nonlinear tracking is proposed, in view of the potential drawbacks （such as, particle impoverishment and numerical sensitivity in calculating the prior） of the conv...A modified unscented particle filtering scheme for nonlinear tracking is proposed, in view of the potential drawbacks （such as, particle impoverishment and numerical sensitivity in calculating the prior） of the conventional unscented particle filter （UPF） confronted in practice. Specifically, a different derivation of the importance weight is presented in detail. The proposed method can avoid the calculation of the prior and reduce the effects of the impoverishment problem caused by sampling from the proposal distribution, Simulations have been performed using two illustrative examples and results have been provided to demonstrate the validity of the modified UPF as well as its improved performance over the conventional one.展开更多
为了在视域(field of view,FOV)不同的条件下实现对数量时变的不确定目标的最优跟踪,提出一种高斯混合概率假设密度(Gaussian mixture probability hypothesis density,GM-PHD)滤波器的去相关算术平均(arithmetic average,AA)融合算法...为了在视域(field of view,FOV)不同的条件下实现对数量时变的不确定目标的最优跟踪,提出一种高斯混合概率假设密度(Gaussian mixture probability hypothesis density,GM-PHD)滤波器的去相关算术平均(arithmetic average,AA)融合算法。鉴于多目标AA融合被分解为多组单目标分量的合并,先通过重构贝叶斯融合推导出最优去相关估计融合,后将其用作单目标分量的合并方法。由于推导的去相关估计融合需要先验估计,设计了一个包含主滤波器的分层结构,以自动提供需要的先验估计。为了解决不同FOV导致的目标势低估问题,融合节点利用FOV补偿单目标分量的权重。仿真结果证实了提出的算法在各种场景中的最优性,提升了多目标跟踪的精度。展开更多
文摘A modified unscented particle filtering scheme for nonlinear tracking is proposed, in view of the potential drawbacks （such as, particle impoverishment and numerical sensitivity in calculating the prior） of the conventional unscented particle filter （UPF） confronted in practice. Specifically, a different derivation of the importance weight is presented in detail. The proposed method can avoid the calculation of the prior and reduce the effects of the impoverishment problem caused by sampling from the proposal distribution, Simulations have been performed using two illustrative examples and results have been provided to demonstrate the validity of the modified UPF as well as its improved performance over the conventional one.
文摘为了在视域(field of view,FOV)不同的条件下实现对数量时变的不确定目标的最优跟踪,提出一种高斯混合概率假设密度(Gaussian mixture probability hypothesis density,GM-PHD)滤波器的去相关算术平均(arithmetic average,AA)融合算法。鉴于多目标AA融合被分解为多组单目标分量的合并,先通过重构贝叶斯融合推导出最优去相关估计融合,后将其用作单目标分量的合并方法。由于推导的去相关估计融合需要先验估计,设计了一个包含主滤波器的分层结构,以自动提供需要的先验估计。为了解决不同FOV导致的目标势低估问题,融合节点利用FOV补偿单目标分量的权重。仿真结果证实了提出的算法在各种场景中的最优性,提升了多目标跟踪的精度。
