Research Of Positioning Technology Based On UWB In Indoor Complex Environment

With the arrival of the 5G,the development of IOT will enter into high peak period in recent years,and the demand for high accuracy indoor positioning is growing rapidly.UWB technology has become one of the preferred solutions for high accuracy indoor positioning,because of its advantages such as strong multipath resistance and a high temporary resolution extremely at the nanosecond level.In the complex and changeable indoor environment.When UWB indoor positioning system performs positioning,there will be measurement noise and NLOS errors.The error will lead to poor positioning effect of traditional positioning algorithm in the NLOS environment.If the NLOS error in the system is not corrected,the performance of UWB indoor positioning system will be lowered greatly.To restrain the effect of the NLOS error,the first we must distinguish the propagation environment of LOS and NLOS accurately,and then we use the related algorithm to reduce the error.Based on this background,this paper researches UWB on indoor positioning technology in complex environments.The main tasks are described as following:(1)The UWB indoor positioning signal is realized by simulation.The transmitted signal is PPM-TH-UWB.The channel model adopts the channel statistical model defined by IEEE.802.15.3a,and gaussian noise is added to simulate the complex environment.The generated signal data also provides research data for the classification research based on UWB signal.(2)We proposed two methods to classify the received UWB positioning signals of the LOS and NLOS channel environment directly without prior knowledge.One of the classification methods is based on one-dimensional convolutional neural network,which extracts and classifies the features of one-dimensional UWB signals directly.The other classification method is based on the recurrence plot and CAENN,which transform the UWB signals into a corresponding recurrence plot.Then the two-dimensional recurrence plots are processed by the CAENN.Experiments show that both algorithms have good classification performance in the simulation environments.(3)In this paper,we presented an improved algorithm.The algorithm used the data fitting method,utilizes CNN to calculate the regression of NLOS data ranging error.Then the regression error is used as the weighting coefficient of the weighting matrix in the WLS and Chan algorithms to improve the positioning accuracy.(4)We built the UWB positioning system platform in the real environment.the proposed UWB positioning signal classification algorithm and the improved positioning algorithm based on CNN were verified and analyzed in the real indoor complex environment.The results show that the classification algorithm still has high classification accuracy in the real complex environment.In the NLOS environment,the improved positioning algorithm can improve the positioning accuracy of the system compared with the traditional positioning algorithm.