多源传感器融合的矿井瓦斯释放源定位方法

针对煤矿井下采空区漏风现象导致瓦斯释放源难以定位或者定位不准问题,提出一种基于多源传感器融合的矿井瓦斯释放源定位算法。首先通过分析综放工作面采空区瓦斯分布规律,建立矿井采空区传感器观测模型与瓦斯释放源扩散模型,然后采用混合卡尔曼粒子滤波算法对采空区瓦斯释放源参数进行估计,并依据迭代运算得到估计参数的坐标位置,最后通过无线传感器目标源感知节点与簇头节点的数据融合,实现瓦斯释放源的精确定位。结果表明:与其他算法相比,混合卡尔曼粒子滤波算法在定位精度上具有明显的优势。该方法能有效解决因漏风现象导致的瓦斯释放源定位困难的问题,进而为采空区瓦斯突出预警及瓦斯抽采提供参考依据。

Location method of mine gas diffusive position based on multi-source sensor

This paper focused on the problem of the source localization of coal mine gas release.Due to the existence of wind leakage,it is difficult to find the location accurately by using the conventional algorithms.In this study,a multi-source sensor fusion based algorithm for locating the coal mine gas release was proposed.Firstly,the gas distribution in the goaf of a fully mechanized working face was analyzed and the observation model of mine goaf area and the diffusion model of gas release were established.Then,the gas release source parameters of goaf were estimated using the Mixed Kalman Particle Filter algorithm (MKPF),and the coordinate of the estimated parameters was obtained according to the iterative operation.Finally,the data of wireless sensor target source sensing nodes and cluster head nodes were fused to locate the accurate coordinate of the gas release source.The research concludes that the MKPF algorithm has obvious advantages in accuracy localization,which can effectively overcome the difficulty of gas release source localization impacted by wind leakage.It can provide support for gas warning and gas drainage in goaf.