摘要
在对井下巷道人员定位优化算法的研究中,准确地知道井下人员位置信息有助于地面进行管理以及当矿难发生时能及时有效的进行救援工作,但是井下环境恶劣复杂多变,利用无线信号进行定位时,信号在井下传播会受到多径效应、随机噪声等影响而出现很大的随机误差,致使定位结果不够准确。为解决上述问题,在传统的对RSSI值进行最大似然估计滤波优化的加权质心算法的基础上,加入多点均值的思想,提出一种对四点进行滤波处理的算法,将每次参与定位的节点数扩展至四个,利用三边测量法得到四组结果取均值。仿真结果表明,与传统算法相比,改进的算法定位精度更高并有较强的可行性和优越性,能有效应用于井下人员定位问题。
The underground roadway personnel positioning optimization algorithm can accurately know mine personnel location information. This is conductive to the ground＇ s management and when the mine accident occurs to proceed in a timely and effective rescue work. But the underground environment is complicated. When the wireless signal is used to locate, signals travel in the mine will be affected by the multipath and random noise, appearing a lot of random error and leading to inaccurate positioning resuhs. Focusing on this problem, based on the traditional like- lihood estimation filter processing to the RSSI values and joining the muhipoint average thought, a filtering processing method for the four points is proposed. This algorithm will participate in the positioning of the node number each time extended to four, and use the trilateration method to get the four sets of results, and then take the average. Simulation results show that, compared with the traditional algorithm, the improved algorithm has higher localization accuracy and stronger feasibility.
出处
《计算机仿真》
CSCD
北大核心
2015年第11期270-273,419,共5页
Computer Simulation
基金
河南理工大学博士基金(72515/168)
关键词
定位算法
加权质心算法
最大似然估计
Location algorithm
Weighted centroid algorithm
Maximum likelihood estimation filter
