Based On The Received Signal Strength Measurements Of Indoor Positioning Technology Research

With the continuous progress of human society, wireless location technology, especially indoor positioning technology in wireless sensor networks has attracted more attention domestically and abroad. Indoor environment is more complicated than outdoor and there are many factors of interference such as obstruction, reflection, diffraction, multipath effect, the stadia propagation, etc., making indoor positioning technology become a new hotspot.Indoor positioning technology are mainly based on time of arrival (TOA), time difference of arrival (TDOA), angle arrival (AOA) and the received signal strength indicator (RSSI). Compared with other location technologies, RSSI-based positioning technology has the advantage of creating a positioning system without any additional devices. In wireless location technology, multipath propagation and non-line-of-sight (NLOS) propagation are usually the main reason which can cause the positioning error. But fingerprinting localization technology can use multipath propagation to build position information, which makes RSSI-fingerprinting localization technology more suitable for complex indoor environment.Fingerprinting location approach consists of two phases:an off-line training phase and an on-line localization phase.In the off-line phase, we first arrange position of the reference nodes and the APs, and then collect a large number of signal strength from the APs, which can help to calculate the average measurement as fingerprint database information. The disadvantage is that it is time-consuming. So we take the fingerprint information of a small part of the reference nodes into consideration, and together with the signal loss model and data interpolation method we can calculate the fingerprint information of other reference nodes. Thus the fingerprint database can be built. Experimental simulation results show that based on the signal loss model database and data interpolation database have both relatively high positioning accuracy.At present, most people compare the similarity with all reference nodes and take the average coordinate of the closer reference nodes as the location of unknown nodes in the on-line localization phase. KNN algorithm and WKNN algorithm are proposed.This paper proposed an improved algorithm—weighted fuzzy C-means clustering (WFCM) algorithm. The improved algorithm does not require similarity comparison of the unknown nodes with all of the reference nodes and the unknown nodes are simply compared with the reference nodes of the same class. This paper uses the received signal strength (RSSI) and weighted fuzzy C-means clustering (WFCM) algorithm, which can overcome the time-consuming problem of the fingerprinting localization and the low accuracy problem of triangular positioning. Meanwhile, Euclidean distance is used as the similarity comparison parameter and noise errors of different level are taken during the simulation. Simulation results show that, compared with the fuzzy C-means clustering (FCM) algorithm, the proposed algorithm can achieve better cluster results and higher positioning accuracy. And the algorithm also has higher robustness.