Research On Indoor Location Based On DBSCAN Algorithm

With the popularity of smart phones,data and multimedia services are developing rapidly. The information demand of the location and spatial characteristics is increasing at the same time, so location based services are becoming more and more important. For outdoor environment, there have been a lot of mature positioning technology in the world. Among them, the global positioning system (GPS) has been the gold standard of outdoor navigation industry. However, about eighty percent to ninety percent of the people's activity time is in the indoor environment now. The demand for location services is gradually changing from outdoor to indoor. GPS can not get the accurately information for indoor location affected by the complex indoor environment. Therefore, the research of indoor positioning technology is becoming more and more important.In recent years, it has been found that the fingerprint localization method has a great development prospect in the positioning accuracy of indoor positioning.Therefore, the fingerprint localization algorithm has become one of the hot spots in the field of indoor positioning technology. In this article, a new method of fingerprint location is improved. On the basis of using KNN algorithm to match the data, this paper studies how to improve the positioning accuracy and efficiency.In the end, this paper uses Bluetooth beacon as the signal sending device to reduce the system cost. At the same time,based on the traditional fingerprint database indoor positioning method, the DBSCAN clustering algorithm is used to preprocess the database instead of the K-Means algorithm. Then, the KNN algorithm is used to match the information received by the mobile terminal with the cluster. So we can get the position information of the mobile terminal to be measured. As a result, this method can not only remove the interference noise points, but also to solve the problem of running too long without comparing all the data.