运用自组织神经网络进行多目标跟踪的算法

总结了自组织神经网络的结构、训练方法；分析了在多目标跟踪问题中数据关联的重要性及传统的数据关联方法的局限性；研究了在多目标环境下运用自组织神经网络解决数据关联的问题。提出了一种基于自组织神经网络对多个目标实施跟踪的算法，此算法采用自组织神经网络的聚类功能对目标进行数据关联处理，并将经过卡尔曼滤波后的数据信息结合到神经网络的学习训练中。仿真实验结果表明此算法能在多目标环境下取得较好的跟踪效果。

An Algorithm for Multiple Targets Tracking with Self-organizing Neural Network

In this paper, the structure and the training method of self-organizing neural network are summarized, the importance of the data association and limitations of some traditional data association methods of tracking multiple targets are analyzed, and the problem of how to solve the data association with this neural network in multiple target condition is studied. A new tracking algorithm for multiple targets tracking based on self-organizing neural network is proposed, which solves the problem of data association by means of a clustering function of self-organizing neural network and incorporating the Kalman filter data into the training method of self-organizing neural network. Simulation results show that the tracking algorithm works well in multiple targets condition.