Research On Wireless Indoor Positioning Technology Through TRM Based On RSSI Prediction Model

As the growing urbanization today, the activities of humans is gathering to the indoor, the design of large buildings become more complete in function and more complex in structure at the same time, this is an opportunity and a challenge for indoor positioning technology. The special nature of the indoor environment caused a variety of locating limitation when many existing common positioning methods is used in indoor scenes, such as the Global Positioning System who is widely used in our world is also powerless for indoor location services because of the signal fading problem, so many positioning solutions based on a variety of communications technology are constantly emerging.Until now, however, the high-precision indoor positioning applications that could be widely accepted and recognized by us do not really appear. The major reason is that most indoor location technology based on the change of specific signal to achieve position estimation regards the complex indoor environment as a simple propagating model, this process mode like black box leads to the ideal positioning accuracy naturally. Currently, the method that indoor environment could be predicted in advance then relocation include mainly two ways: the technology based on fingerprint database and technology based on TR-FMM. While the former is capable of handling the positioning in NLOS and multipath transmission environment, the establishment of fingerprint database would make a huge workload and has poor self-adaptability. The latter could use the fast marching method to utilize the Time of Flight to estimate the channel propagation model, then pinpoint the target through Time Reversal technology, but the TOF in interior space is a small signal at picosecond level, which requires expensive precision equipment to get, so this technology has poor practicability. Based on above, we propose a wireless indoor positioning technology used TRM based on RSSI prediction model.This technology could be divided into two parts: IA-RSSI algorithm which could estimate the indoor environment and TR method which could achieve the target positioning. Here the received signal strength indication RSSI is used as a new estimation parameter to replace TOF, which could not only obtain high positioning accuracy but simplifying devices and lowing costs, thus the usability of indoor location system is enhanced. The main work of this paper include:(1) Study on the relationship between the characteristics of the indoor channel and RSSI. According to the advantages and disadvantages of RSSI, we could know from RSSI-based intrusion detection technology that its disadvantage which could fluctuate with environmental changes can be used to detect channel changes in turn. This paper verified the change relationship between the conductivity and RSSI in a complex environment from both theory and measured simulation.(2) Put forward the innovative IA-RSSI algorithm to estimate the indoor channel model. By using the heterogeneous distribution of conductivity of object to simulate indoor non-uniform environment, we utilized the fluctuation characteristic of RSSI and combined the electromagnetic characteristic of signal propagation to constantly iterate to update conductivity distribution, until we can obtain an indoor environment model closest to the actual environment. Lastly we used the time reversal technique to locate accurately indoor small target on the basis of known model. The research indicated that the average positioning error of small terminal in this paper does not exceed 3cm.The proposed indoor locating technology has found a new parameter from another point of view, this advance solved the shortcoming of some model-forecast technique which have poor utility. It could not only satisfy the demand of precise positioning of indoor terminal, but also to lay a solid foundation for the achievement of indoor product-level positioning system.