井下基于动态指纹更新的指纹定位算法研究

Research on Fingerprint Location Algorithm Based on Dynamic Fingerprint Update

围绕煤矿井下环境特点,提出一种基于动态指纹更新的指纹定位算法。该算法运用FCM(Fuzzy C-Means Clustering)按信号分布特征划分井下定位区域,在各个子区域建立训练学习模型。在FCM算法基础上提出一种基于移动用户位置的HMM(Hidden Markov Model)运动信息序列模型,通过用户无意识地参与RSSI(Received Signal Strength Indication)序列的采集,实现指纹数据库的动态更新。运用具有自学习能力的ANFIS(Adaptive Network-based Fuzzy Inference System)算法定位未知节点。实验结果表明:所提的井下基于动态指纹更新的指纹定位算法定位精度可达2.6 m,满足煤矿井下巷道的实时定位需求。

According to the characteristics of underground environment, a fingerprint location algorithm based on dynamic fingerprint updating is proposed. FCM(Fuzzy C-Means Clustering) is used to divide the location area according to the signal distribution characteristics, and the training and learning model is established in each sub area. On the basis of FCM algorithm, a HMM(Hidden Markov Model) motion information sequence model based on the location of mobile users is proposed. The dynamic update of fingerprint database is realized by users unconsciously participating in the collection of RSSI(Received Signal Strength Indication) sequence. ANFIS(Adaptive Network-based Fuzzy Inference System) algorithm with self-learning ability is used to locate unknown nodes. The experimental results show that the accuracy of the fingerprint location algorithm based on dynamic fingerprint update can reach 1.6 m, which can meet the real-time location requirements of the underground roadway.