The Research And Improvement Of Indoor Localization Algorithm Based On RSSI Distance Measurement

In recent years, the application of Wireless positioning technology has entered a new field, as the rapid development of wireless senor network. Especially in large buildings, such as gym, large shopping malls, etc., indoor positioning technology is particularly important, and can provide the relevant location information for both persons and the goods. On the security work, it also plays an important role. At present, the indoor positioning technology has been developed more further. But the positioning accuracy is still expected to be improved.After studying the indoor positioning technology based on RSSI(received signal strength indicator) ranging, this paper presents two optimization schemes.In the traditional positioning system based on RSSI ranging, a signal propagation model according to relationship of the RSSI and the distance is firstly established so that it can fit into the change of RSSI attenuation values with the increase of the distance as good as possible. However the indoor environment is too complex, and there is positioning error on the influence of non line-of-sight and multipath effect. To solve the problem, this paper put forward linear regression analysis method based on weighted RSSI to correct model parameters real-time. It can reduce the ranging error.Based on the traditional RSSI ranging localization algorithm, a new localization algorithm is proposed. After analyzing and dealing with the real-tine training data, the improved maximum likelihood estimation method is used to calculate the target node and get a estimate position coordinates. Then Taylor series expansion(Taylor)localization algorithm is used to correct the coordinates. Therefore, the proposed new localization algorithm combind the improved maximum likelihood estimation method and Taylor series expansion position. Namely, the calculation method set the estimated position coordinates as the iterative initial value, and take it into Taylor series expansion localization algorithm.The results of Matlab simulation show that the optimized parameters can better fitting curve of signal transmission, compared with the traditional ranging localization algorithm. And the positioning precision is greatly improved after the combination of the two algorithms.