A Study Of RSSI Based Indoor Localization Algorithm

With the continuous improvement of living standards, the indoor positioning technology is widely used in various occasions. People require more and more intelligence and security in everyday life, so it has important research meaning and practical value, and attracts a large number of researchers to make an intensive study. The focus has been drawn to the RSSI-based indoor positioning technology because of its low hardware requirements of the system and simple implementation. This thesis aims at the indoor positioning technology based on the received signal strength indication, mainly including the following research work:1. An indoor positioning system based on RSSI is implemented. Firstly, this thesis describes the RSSI location principle and its algorithm in detail, and introduces the unknown nodes, positioning beacon nodes. Secondly, this thesis does the software development in the Texas Instruments Zig Bee protocol stack, extracts packet RSSI value from among the nodes communicate with each other, and then defines the corresponding structure to store data by the unpacking method, and transmits a certain data format to a remote client. Lastly, this thesis develops software platform to receive and handle and show the location information of the nodes. The laboratory environment and corridors inside the experimental site are selected as experimental field to implement the algorithm in a real environment and display the concrete results. Experimental results show that the algorithm is simple and has strong practical value.2. RSSI and multidimensional scaling based indoor localization algorithm is studied. Aiming at the problems that the indoor mobile object localization algorithms for wireless sensor networks(WSN) based on RSSI methods are easy to be interfered and the localization errors have large variation, this thesis studies RSSI and multidimensional scaling based indoor localization algorithm. The RSSIs of the nodes in WSN are used to construct the dissimilarity matrix. The classical multidimensional scaling method is used to solve the relative coordinates of the nodes and perform the coordinate transform with planar four parameter model according to the actual coordinates of the reference nodes. The particle swarm optimization algorithm is used to optimize the parameters, and then the actual coordinates(the real positions)of the mobile nodes are calculated. The thesis does the simulation experiments and carries out positioning error comparative experiments. The experimental results show that: RSSI and multidimensional scaling based indoor localization algorithm has good positioning performance even in the presence of random RSS fluctuations, and has high positioning accuracy compared to distance measurement technology based on RSSI.