论文标题
在不确定环境中移动对象的轨迹自适应预测
Trajectory Adaptive Prediction for Moving Objects in Uncertain Environment
论文作者
论文摘要
现有的轨迹预测方法很难准确地描述复杂和不确定环境中移动对象的轨迹。为了解决此问题，本文提出了一种基于变异的高斯混合模型（VGMM）在动态环境（ESATP）中移动对象的自适应轨迹预测方法。首先，基于传统的混合高斯模型，我们使用近似变分的贝叶斯推理方法在模型训练过程中处理混合物高斯分布。其次，变分贝叶斯期望最大化迭代用于学习模型参数，并使用先验信息来获得更精确的预测模型。最后，对于输入轨迹，参数自适应选择算法自动使用来调整参数的组合。实验结果表明，实验中的ESATP方法显示出高预测精度，并保持高时间效率。该模型可用于移动车辆定位产品。
The existing methods for trajectory prediction are difficult to describe trajectory of moving objects in complex and uncertain environment accurately. In order to solve this problem, this paper proposes an adaptive trajectory prediction method for moving objects based on variation Gaussian mixture model (VGMM) in dynamic environment (ESATP). Firstly, based on the traditional mixture Gaussian model, we use the approximate variational Bayesian inference method to process the mixture Gaussian distribution in model training procedure. Secondly, variational Bayesian expectation maximization iterative is used to learn the model parameters and prior information is used to get a more precise prediction model. Finally, for the input trajectories, parameter adaptive selection algorithm is used automatically to adjust the combination of parameters. Experiment results perform that the ESATP method in the experiment showed high predictive accuracy, and maintain a high time efficiency. This model can be used in products of mobile vehicle positioning.
