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.