基于二维网格融合特征参数的室内匹配定位算法

针对接收信号强度值(RSSI)的时变特性降低定位精度的问题，提出了一种基于二维网格特征参数融合的室内匹配定位算法。该算法融合RSSI和信号到达时间差(TDOA)构建网格特征参数模型，基于二维网格快速搜索策略降低匹配定位的计算量，采用网格特征向量的归一化欧氏距离进行最优网格匹配定位，最终由匹配网格的参考节点计算终端的精确位置。定位仿真实验中，该算法在3m网格粒度下的定位均方根误差为1.079m，平均定位误差小于1.865m；3m定位精度下的概率达到94.7%，相对于传统单一RSSI模型法提高了19.6%。所提算法能够有效提高室内定位精度，同时减少搜索数据量，降低匹配定位的计算复杂度。

Indoor matching localization algorithm based on two-dimensional grid characteristic parameter fusion

Focused on the issue that the time-varying characteristic of indoor Received Signal Strength Indicator (RSSI) drastically degrades the localization accuracy, an indoor matching localization algorithm based on two-dimensional grid characteristic parameter fusion was proposed. The algorithm fused received signal strength and Time Difference of Arrival (TDOA) parameters to build grid feature model, in which two-dimensional grid quick search strategy was adopted to reduce computation amount. Normalized Euclidean distance of grid feature vector was used to realize the optimal grid match localization. Finally, the precise terminal location was computed by reference nodes of the matched grid. In the localization simulation experiments, the proposed algorithm achieved the localization Root Mean Square Error (RMSE) at 1.079m, and the average localization accuracy was within 1.865m in the condition of 3m grid granularity; The probability of 3m localization accuracy reached 94.7%, which was 19.6% higher than that of traditional method only bawsed on RSSI. The proposed algorithm can effectively improve the indoor positioning accuracy, meanwhile reduces the search data quantity and the computational complexity of matching localization.