基于WSN的气体扩散态势图空间分辨率增强方法

在应急救援中,通常采用建模方法获取气体扩散态势图(羽流图),随着无线传感器网络的发展,其快速部署与分布式感知为更精确的态势图绘制供了新途径。但是,在实际应用中,由于节点部署的误差与数量限制,存在感知盲点区域与空间分辨率偏低的问题。针对此问题提出了增强态势图空间分辨率的算法。该算法基于高斯掩模计算的节点位置势能图,虚拟生成亚感知节点位置,利用贝叶斯分类器估计其浓度值,并结合环境矢量场信息,简化参数学习过程。仿真结果表明,在满足实时应用要求的同时,算法提高了态势图分辨率1～3倍,减少10%～30%盲点区域。

Method to enhance the spatial resolution of gas propagation situation map based on wireless sensor networks

In emergency rescue, the situation map (plume) of gas propagation is normally gained by modeling method. With the development of WSN (wireless sensor network), it provides a new strategy to plot accuracy situation map due to its strengths in fast deployment and distributed sensing. However, in practice, the nodes may be deployed limitedly and erroneously, which could cause blind area and low spatial resolution. To solve this problem, a method that can improve the resolution of the situation map is proposed. The method use potential energy map yielded by Gaussian mask to generate the sensor sub node position. Then the concentration value of sub node is evaluated by Bayesian classifier. Also the vector field of environment is concerned to simplify the course of parameter learning. The simulation result shows that the method can improve the spatial resolution of the situation map by 1~3 times, and decrease the blind area by 10%~30%. Furthermore, the algorithm can be used for real time applications.