平均场网络在航迹关联中的应用

在多节点分布式多传感器融合系统中,航迹关联问题可以化为多维分配问题。多维分配问题是一个典型的组合优化问题,很难得到问题的最优解,而且其计算量会随着问题维数和目标数的增加容易呈现指数爆炸现象。在二维平均场人工神经网络的基础上提出了一种三维平均场网络模型用于解决此三维分配问题。仿真结果表明,该人工神经网络模型,能够有效解决多维分配问题,具有较高的关联正确率,当目标数不是很多时,可满足工程上的要求。另外,提出的三维网络模型可以推广到多维情况用于解决多维分配问题。

Application of Mean-field Network to Track Correlation

In a multi-node distributed multisensor fusion system, the problem of track correlation can be transformed to a problem of multi-dimension assignment. The problem of multi-dimension assignment is a typical combined optimization problem, it is very hard to obtain the optimum solution, and its computing burden is easy to increase exponentially with the increase of the numbers of targets and dimensions. A three-dimension mean-field neural network model is proposed to solve the problem based on the two-dimension mean-field neural network. The experimental results illustrate that this network model can solve three-dimension assignment problem effectively, and that the correct percent of track association is high. It can satisfy the practical needs when the number of target is not very large. The three-dimension network model proposed in this paper can be generalized to the condition of multi-dimension.