The Study And Implementation Of A Improved Indoor Position Algorithm

To achieve precisely indoor path tracking by making use of RSSI that was provided by existing WIFI wireless networks. In this paper, we proposed a novel indoor positioning algorithm which combined Support Vector Machine (SVM) algorithm and Weighted K Nearest Neighbor (WKNN) algorithm to complete the tracking task. The key issue of indoor positioning is how to use the instable wireless network and nonlinear wireless signal strengths to accurately locate the position of a person or object. However, the traditional linear methods, such as calculating the signal distance, are failed in dealing with this problem. So we introduced the SVM which shows a great advantage in solving the nonlinear problem to achieve the precisely indoor positioning. Because the stronger relevance between the real time measurements and the records in signal database, the more classification votes one position that related to the record may get. In most instances, the measurements are not measured right in a reference point, but in a small region surrounded by several points. In this case, although the SVM achieves a great improvement when compared with the linear methods, defects still exist in this one-time decision strategy. Thus we use a Weighted K-Nearest Neighbor (WKNN) algorithm to do the further classification to smooth the data and improve the performance and get the combined algorithm SVM-WKNN. Then on the basis of SVM-WKNN algorithm, we build a C/S mode path tracking system. We divided this system into Android client and Web server which communicate to each other by HTTP protocol. Once the Android client moves a step, it would trigger a positioning request and send it to the web server. Thus, while the Android client keeps moving, a series of requests will trigger and send. Our path tracking system is based on continuous positioning, the Android client will responsible for recording and drawing the path. The experiment results show that the SVM-WKNN algorithm can achieve excellent positioning accuracy as well as in the path tracking experiment.