网络模糊信息数据集合多目标优化跟踪仿真

Multi-Target Element Optimization Tracking Simulation of Network Fuzzy Information Data Set

为了更好地对网络模糊信息数据集合多目标元素进行利用,需要对其进行跟踪融合。当前基于FCM的多目标元素跟踪方法,利用模糊聚类的形式完成目标跟踪,存在跟踪质量差,跟踪过程时延长等问题。现提出一种基于神经网络的多目标元素跟踪融合方法,利用CRPF法对跟踪目标的状态进行确定,依据粒子滤波具有的跟踪性能,对粒子进行重新采样,并对目标状态估计所需费用进行计算。更新粒子,获得粒子分量,对目标状态估计费用更新,并利用单调递减函数对目标状态值进行确定,以提高目标跟踪精度。利用最大似然法对网络模糊信息数据集合多目标元素最优跟踪融合值进行估计。将估计目标跟踪融合误差的协方差矩阵,转换为估计辅助矩阵和估计产生的误差构成的矩阵,以减少多目标元素跟踪融合计算量,降低元素跟踪融合时延。对神经网络进行离线训练,并获取目标跟踪融合权值,以此得到多目标元素高质量跟踪融合结果。仿真结果表明,上述方法可对多目标元素进行高精度跟踪,且可将跟踪延时控制在可接受范围内。

At present,the multi-objective element tracking method based on FCM uses fuzzy clustering to track the target,which has low tracking quality and long time delay of tracking process. Therefore,this paper puts forward a method of multi-objective element tracking fusion based on neural network. The CRPF method was used to determine the state of tracking target. According to the tracking performance of particle filter,the particle was resampled and the cost of estimating target state was calculated. Moreover,the particle was updated to obtain particle components and the estimated cost of target state was updated. Meanwhile,the monotone decreasing function was used to determine the target state value,so as to improve the accuracy of target tracking. In addition,the maximum likelihood method was used to estimate optimal tracking fusion value of multi-objective element of network fuzzy information data set. Then,the covariance matrix of estimated target tracking fusion error was transformed into the estimated auxiliary matrix and the matrix formed by estimated error,so as to reduce the amount of calculation of multi-objective element tracking fusion and decrease the time delay of element tracking fusion. Finally,the neural network was trained offline to obtain the fusion weight value of target tracking. Thus,the result of high-quality tracking fusion of multi-objective element was obtained. Simulation results show that the proposed method can track multi-objective element with high precision. Meanwhile,it can control the tracking delay within the acceptable range.