井下受困人员搜救定位方法研究与仿真

Research and Simulate of Searching and Rescuing of Underground Trapped Personnel

研究井下下受困人员准确搜救定位问题。井下环境复杂,水汽、瓦斯浓度较高,用于搜救定位的射频信号容易受到外界干扰,造成用于定位的射频回波信号衰减。传统的人员搜救定位方法依靠对有人区域的射频回波反射信号的特征进行检测,完成人员的搜救定位,一旦回波信号发生干扰造成信号变形,会造成人员的定位不够准确。为解决上述问题,提出自增强BP神经网络的井下受困人员定位算法,通过设计回波信号增加网络弥补射频衰减的强度,对射频回波信号衰减的进行补偿,保证在强干扰下的井下受困人员定位准确性。实验表明,自增强BP神经网络方法能够在强干扰下增强受困人员定位的准确性,准确定位被困人员的位置。

Study the accurate location problem of underground trapped staff. Underground environment is com- plex, vapor, and gas concentration is higher The paper put forward an underground trapped personnel positioning al- gorithm based on enhanced BP neural network, Through the design of echo signals to compensate the signal attenua- tion and ensure accurate positioning of the down-hole trapped. Experimental results show that, the enhanced BP neural network method can enhance the positioning accuracy of trapped personnel with strong interference.