Focused on the issue that the location fingerprint matching localization algorithm has a large workload of offline database collection in an indoor environment, a fast virtual grid matching algorithm based on Pearson correlation coefficient was proposed. Firstly, the Received Signal Strength Indicator (RSSI) was preprocessed with Gaussian filter to obtain the received signal strength vector. Then, the Bounding-Box method was used to determine the initial virtual grid region. The grid region was rapidly and iteratively subdivided, the distance log vectors of the grid center point to beacon nodes were calculated, and the Pearson correlation coefficients between the received signal strength vector and the distance log vectors were calculated. Finally, the k nearest neighbor coordinates whose correlation coefficients close to -1 were selected, and the optimal estimation position of the undetermined node was determined by the weighted estimation of correlation coefficients. The simulation results show that the localization error of the proposed algorithm is less than 2m in 94.2% probability under the condition of 1m virtual grid and RSSI noise standard deviation of 3dBm. The positioning accuracy is better than that of the location fingerprint matching algorithm, and the RSSI fingerprint database is no longer needed, which greatly reduces the workload of localization.

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.