Research on Indoor Positioning Method Based on K-means and KNCN Algorithm
In view of the problems of fixed parameters of adjacent reference points,inflexible position-ing,and large error in the K-nearest neighbor(KNN)algorithm,an improved indoor positioning algo-rithm based on K-means and K-nearest neighbor centroid(KNCN)was proposed.By performing K-means clustering on the received signal strength indicator(RSSI)of the main features in the fingerprint database,multiple clusters with high cluster center similarity were dynamically selected according to the threshold.The KNCN algorithm used the RSSI weight distribution of the main features to calculate the weighted distance and used the Gaussian weighted distance to match the cluster data,so as to deter-mine the unknown position.The experimental results show that the average positioning accuracy of the improved algorithm is improved by 29.4%and 3%compared with KNN and KNCN algorithms,and the average positioning time is shortened by about 83.4%compared with KNCN algorithm.
万方数据
维普
