随机超曲面模型容积卡尔曼扩展目标跟踪算法

针对复杂环境下不规则形状的星凸形扩展目标跟踪问题,论文提出了一种基于随机超曲面模型(RHM)的容积卡尔曼扩展目标跟踪算法。首先,利用随机超曲面模型对星凸形扩展目标的量测源进行建模,建立扩展目标的跟踪滤波量测模型。然后,详细推导并提出基于RHM的容积卡尔曼扩展目标跟踪算法的实现过程。最后,通过构造具有不规则形状的扩展目标跟踪仿真实验验证了论文所提算法的有效性。

Study on resource quantity of surface water based on phase space reconstruction and neural network

Proposed a new method to disclose the complicated non-linearity structure of the water-resource system, introducing chaos theory into the hydrology and water resources field, and combined with the chaos theory and artificial neural networks. Training data construction and networks structure were determined by the phase space reconstruction, and establishing nonlinear relationship of phase points with neural networks, the forecasting model of the resource quantity of the surface water was brought forward. The keystone of the way and the detailed arithmetic of the network training were given. The example shows that the model has highly forecasting precision.