基于递阶ANFIS树的室内定位算法研究

Research on indoor localization algorithm based on hierarchical ANFIS tree

室内指纹定位算法易受到多种因素的影响,造成指纹数据集的模糊性。针对应用在室内定位中的自适应神经模糊推理系统(ANFIS),随着模糊系统输入参数的增加,造成ANFIS的维数灾难和计算的复杂性等问题,提出了一种基于递阶自适应神经模糊推理系统树(HANFIS-Tree)的室内定位算法。将整体ANFIS拆分为互联型的HANFIS-Tree结构,减少模糊规则数量,以此提高了系统的计算效率和模糊系统的可解释性,同时采用改进的特征权重算法选取与坐标位置相关性较高的信号强度(RSSI)之差作为输入参数,提高了系统的定位精度,并且引入减法聚类算法初始化输入参数,提高了系统的收敛速度。实验结果表明,与ANFIS相比,HANFIS-Tree在室内定位算法中,横坐标上定位精度提高了3.5cm,纵坐标上运算效率均提高了约1倍。

Indoor fingerprint localization algorithm is easily affected by many factors,then causes the fuzziness of fingerprint data set.In response to the adaptive neuro-fuzzy inference system(ANFIS)applied in indoor positioning,the increasing number of input parameters in the fuzzy system causes the curse of dimensionality and computational complexity of ANFIS.This paper proposes an indoor localization algorithm based on the hierarchical adaptive neuro-fuzzy inference system tree(HANFIS-Tree).The algorithm divides the overall ANFIS into an interconnected HANFIS-Tree structure,reducing the number of fuzzy rules,thereby improving the computational efficiency and interpretability of the system.Additionally,an improved feature weighting algorithm is used to select the difference in signal strength(RSSI)with high correlation to the coordinate position as input parameters,enhancing the positioning accuracy of the system.Moreover,a subtraction clustering algorithm is introduced to initialize the input parameters,improving the convergence speed of the system.Experimental results demonstrate that compared to ANFIS,HANFIS-Tree achieves an improvement of 3.5 cm in positioning accuracy on the x-axis,and approximately doubles the computational efficiency on the y-axis in the indoor localization algorithm.