Design And Implementation Of Indoor NLOS Cluster Positioning Algorithm Based On UWB

With the continuous development of science and technology and the continuous improvement of production technology,the chip industry has developed rapidly,and various smart devices have been integrated into people's daily lives,making people's lives more and more abundant.At the same time,people's demand for indoor location services is gradually increasing,so indoor positioning technology research has gradually become a scientific research hotspot in recent years.The difference between the indoor environment and the outdoor environment is that when the former uses traditional satellite positioning technology,the positioning accuracy is seriously reduced due to obstructions such as buildings,which cannot meet people's needs.Therefore,researchers have proposed many positioning algorithms to solve this problem.In indoor scenes,walls,windows,furniture will cause varying degrees of non-line-of-sight interference to positioning signals,which leads to increased errors in traditional indoor lineof-sight positioning algorithms.Existing research mainly uses two methods of error suppression and error compensation to improve the positioning accuracy,but despite this,there are still problems of low positioning accuracy and large limitations.Moreover,when the pending tag moves outside the coverage area of the base station,the location of the tag cannot be estimated due to the lack of positioning data.Therefore,how to achieve reliable target positioning in indoor scenes with non-line-of-sight interference has become the focus and difficulty of current research.Based on the existing indoor positioning research,this thesis proposes an indoor non-line-of-sight cluster positioning algorithm based on UltraWide-Band(UWB).This algorithm can effectively solve the problems of traditional positioning algorithms in indoor non-line-of-sight scenes.In order to solve the problems such as accuracy degradation and positioning failure in traditional time of arrival(TOA)positioning algorithm in the indoor non-line-of-sight scenes.This thesis first improves the existing TOA positioning algorithm,and designs a distancebased base station combination method to improve the positioning accuracy of the algorithm.Experimental results show that the improved TOA positioning algorithm can effectively select the best base station combination,and the positioning accuracy of the traditional TOA positioning algorithm has been greatly improved.Secondly,in order to achieve the UWBbased indoor non-line-of-sight cluster positioning algorithm designed in this thesis,this thesis elaborates a cluster relative positioning algorithm that does not require anchor nodes for positioning.At the same time,the core principles of pedestrian track estimation technology are explained,and on the basis of the existing step frequency detection algorithm,the probability of peak misjudgment is reduced by increasing the change threshold discrimination,thereby improving the accuracy of step counting.After that,a complementary filtering algorithm that dynamically adjusts the weight according to the change of the object's motion state is proposed.The simulation results show that the algorithm can effectively improve the accuracy of the heading angle calculation.Next,the design walking experiment verified that the improved pedestrian track estimation algorithm has smaller positioning errors than other algorithms and is closer to the true trajectory.Then,this thesis uses the UWB positioning development board to systematically implement the proposed algorithm.The system is based on Client/Sever architecture,and the hardware level is composed of a UWB positioning development board and a wireless module as a client to complete the collection and transmission of original data.The software level realizes the positioning algorithm by programming on the server side.Finally,the built positioning system was tested through actual experiments,and the results showed that the system functions to meet the expected requirements,and it has high positioning accuracy,and at the same time,the positioning delay is small,which can meet the needs of real-time positioning.