Research On UWB Localization Based On Genetic Algorithm

Ultra wideband(UWB)localization is used to transmit data by sending a narrow pulse signal of the nanosecond level.It can achieve centimeter-level precise localization in line of sight environment and is widely used in various localization service scenarios such as precision agriculture,smart industry,intelligent storage,etc.Therefore,the research on UWB localization has vast application value and practical significance.After the UWB localization system obtains the ranging information,the localization equation is established,and the intelligent optimization method is used to solve the equation to obtain the tag position.Among intelligent optimization solution methods,genetic algorithm has the advantages of good optimization performance,fast convergence and strong robustness,and has become a hotspot of UWB localization research.However,in the solution process,the fitness function used by the traditional genetic localization algorithm only considers the distance between the tag and the base station,and does not consider the distance between the tags.The ranging information is not comprehensive enough,which leads to limited improvement in the localization accuracy of the algorithm.Therefore,it is necessary to improve the fitness function,make full use of the ranging information between the tags,and improve the localization accuracy.In addition,the adoption of a new fitness function will change the dimension of the solution space from few-dimension to multi-dimension,which will affect the convergence rate of the localization results.In view of this,UWB localization based on the genetic algorithm is studied to improve the localization accuracy and accelerate the convergence speed of the localization results.The main work of the paper is as follows:(1)Aiming at the limited improvement of traditional genetic localization accuracy,an improved genetic localization algorithm is proposed.First,the fitness function is improved.The improved fitness function not only considers the distance between the tag and the base station,but also considers the distance between the tags,and makes full use of all the ranging information to improve the localization accuracy.Then an improved mutation method is adopted to prevent the problem that the search range is so large to slow the convergence of the localization results.Finally,in order to improve the convergence performance of the localization results,a search mechanism based on Brownian motion is introduced.The simulation results show that the improved genetic localization algorithm significantly improves the localization accuracy compared with the traditional genetic localization algorithm,and the improved mutation method and the Brownian motion-based search mechanism improve the convergence performance of the localization results.(2)In the further study of the improved genetic localization algorithm,it is found that the randomly selected initial values seriously affects the performance of the algorithm,resulting in a large localization error.Therefore,an initial values selection method based on gray wolf optimization is proposed to provide good initial values for the improved genetic localization algorithm.The simulation results show that the method can reduce the influence of random initial values on localization performance.(3)The improved genetic localization algorithm belongs to the random search algorithm,and the optimal solution obtained is not accurate enough.Aiming at this problem,a localization algorithm fusing improved genetic and Taylor series expansion is proposed,which combines the strong optimization ability of the improved genetic algorithm and the iterative solution ability of Taylor series expansion method.The simulation results show that the fusion algorithm can further improve the localization accuracy.(4)In order to evaluate the performance of the improved genetic localization algorithm,UWB localization platform is designed.The localization platform is mainly composed of two parts: localization algorithm and localization interface,which can realize functions such as customization of algorithm parameters and display of positioning results.