针对多扩展目标跟踪问题,提出了基于泊松点过程( Poisson Point Process, PPP )模型的多扩展目标跟踪的联合概率数据关联( Joint Probabilistic Data Association, JPDA )算法。首先,采用PPP对扩展目标进行测量建模,其次以“多对一”关...针对多扩展目标跟踪问题,提出了基于泊松点过程( Poisson Point Process, PPP )模型的多扩展目标跟踪的联合概率数据关联( Joint Probabilistic Data Association, JPDA )算法。首先,采用PPP对扩展目标进行测量建模,其次以“多对一”关联模型思想提出一种JPDA算法,从而计算运动目标的当前有效量测的边缘关联概率,然后结合该边缘关联概率以概率数据关联( Probability Data Association, PDA )的方式分别更新每个扩展目标的运动参数和形状参数向量,最后通过仿真实现了当扩展目标相互靠近或出现交叉时的跟踪。实验结果表明,在高杂波环境下,本文所提出的算法在计算时间和跟踪稳定上具有较明显的优势。展开更多
In this paper, methods based on ranks and signs for estimating the parameters of thefirst-order integer-valued autoregressive model in the presence of additive outliers are proposed. In particular, we use the robust s...In this paper, methods based on ranks and signs for estimating the parameters of thefirst-order integer-valued autoregressive model in the presence of additive outliers are proposed. In particular, we use the robust sample autocorrelations based on ranks and signsto obtain estimators for the parameters of the Poisson INAR(1) process. The effects ofadditive outliers on the estimates of parameters of integer-valued time series are examined. Some numerical results of the estimators are presented with a discussion of theobtained results. The proposed methods are applied to a dataset concerning the numberof different IP addresses accessing the server of the pages of the Department of Statistics of the University of Würzburg. The results presented here give motivation to use themethodology in practical situations in which Poisson INAR(1) process contains additiveoutliers.展开更多
文摘针对多扩展目标跟踪问题,提出了基于泊松点过程( Poisson Point Process, PPP )模型的多扩展目标跟踪的联合概率数据关联( Joint Probabilistic Data Association, JPDA )算法。首先,采用PPP对扩展目标进行测量建模,其次以“多对一”关联模型思想提出一种JPDA算法,从而计算运动目标的当前有效量测的边缘关联概率,然后结合该边缘关联概率以概率数据关联( Probability Data Association, PDA )的方式分别更新每个扩展目标的运动参数和形状参数向量,最后通过仿真实现了当扩展目标相互靠近或出现交叉时的跟踪。实验结果表明,在高杂波环境下,本文所提出的算法在计算时间和跟踪稳定上具有较明显的优势。
文摘In this paper, methods based on ranks and signs for estimating the parameters of thefirst-order integer-valued autoregressive model in the presence of additive outliers are proposed. In particular, we use the robust sample autocorrelations based on ranks and signsto obtain estimators for the parameters of the Poisson INAR(1) process. The effects ofadditive outliers on the estimates of parameters of integer-valued time series are examined. Some numerical results of the estimators are presented with a discussion of theobtained results. The proposed methods are applied to a dataset concerning the numberof different IP addresses accessing the server of the pages of the Department of Statistics of the University of Würzburg. The results presented here give motivation to use themethodology in practical situations in which Poisson INAR(1) process contains additiveoutliers.
