The Research On Indoor Localization Algorithm Based On Signal Strength

With the constant progress of the society, people increasingly demand for location services, especially the indoor positioning technology. But the GPS technology in complex indoor environment can’t well meet the demand of people, coupled with the signal transmission by all kinds of obstacles and attenuation factors such as the signal itself, cause indoor positioning technology has become a difficult and hot spots at present. There are many research methods of indoor positioning at the present stage, such as TOA, DTOA, AOA, RSSI localization technology, etc. Because the RSSI positioning technology requires no additional equipment, positioning process is relatively simple, so the RSSI positioning technology becomes the object of many scholars to study.At first, this paper puts forward a kind of indoor locating method based on least square method. The positioning method is mainly is divided into two parts: the first part is the early stage of the signal processing; Because of the wireless signal propagation in complex indoor environment will be affected by a variety of effects, such as multipath propagation, non line-of-sight propagation and reflection, diffraction, and the attenuation of the signal itself. So the signal early through the signal filtering processing and average RSSI estimates, and then based on the attenuation of signal propagation model and distance measurement model to analyze the distance between nodes. The second part is on the basis of the traditional directly using the least square method is improved, to establish a reasonable positioning coordinate system, select the appropriate reference node indoor positioning, so as to improve the accuracy of positioning.Secondly, the paper puts forward a kind of indoor location method based on weighted neighbor algorithm. This method is mainly aimed at the traditional nearest neighbor method and the positioning accuracy and stability of the KNN algorithm cannot meet the demand and on the basis of the improvement method is put forward. Improved method is mainly in the process of positioning, since each neighbor reference node to locate the influence degree of different, to each neighbor nodes of different weights of reference; Weight depends on the size of the Euclidean distance between the nodes, the size of thepositioning accuracy can be improved.Finally, by collecting and processing data, algorithm simulation experiment, to analyze the various factors influencing the localization, and to prove the effectiveness of the proposed two methods of positioning and superiority.