Research On Indoor Distributed Localization And It's Uncertainty Analysis

In the smart warehousing,the position information plays an important role in the efficient management of intelligent warehousing for the real-time location information of materials,goods,warehousing personnel and vehicles.However,there are existing obstacles producing multi-path,obstructions causing non-line-of-sight transmission(NLOS),reflection,interference,measurement noise and many other factors in the indoor complex wireless transmission environment,resulting in low indoor localization accuracy,which is difficult to meet the application requirements of intelligent storage.Therefore,it is of important practical significance and practical value to develop high-precision indoor distributed localization theories and methods for intelligent storage for improving the localization accuracy,cost and management efficiency of the intelligent storage system.For the demand for high-precision distributed localization of intelligent three-dimensional storage,considering the adverse effects of the indoor complex localization environment on distributed localization,this paper carries out optimization-based localization research,and proposes an indoor distributed static localization method based on clustering,and an optimization Distributed nonlinear mobile localization method.The mainly research works are as follows.(1)For the uncertainty analysis problem of wireless localization,we proposed a more complete uncertainty analysis method for wireless localization.We first analyze the uncertainty of distance estimation in the localization process,and then analyze the uncertainty of localization calculation.At the same time,we also consider the uncertainty analysis problem of explicit expression model and implicit expression model.So we form a more complete uncertainty Analyzing method system,which provides a theoretical and methodological foundation for improving the accuracy of distance estimation and localization;(2)Aiming at the problem that the mixed environment of line-of-sight/non-line-of-sight in indoor distributed static localization has a large negative impact on indoor static distributed localization method based on clustering optimization is proposed.In the method,first,the combined localization method is adopted to obtain the localization results of multiple groups,and then the clustering of the group localization results is realized based on the K-means clustering method,which provides support for optimizing the selection of localization results,and finally realizes high-precision indoor distributed static localization.(3)Aiming at the problem of low distributed mobile localization accuracy caused by environmental interference,we propose a distributed nonlinear mobile localization method based on optimization.In the method,we start with the analysis of the uncertainty propagation mechanism in the composition of wireless localization,and we obtain the factors that have a large impact on the localization results.Then an optimized selection algorithm is used to select high-quality distance results and their corresponding anchor nodes to participate in the localization calculation.Finally,based on particle filter and localization model fusion,high-precision indoor distributed mobile localization results are achieved.Through the research of this article,a complete uncertainty analysis method system in wireless localization has been formed,including uncertainty factor analysis,uncertainty quantification,uncertainty sensitivity analysis,uncertainty propagation,and uncertainty synthesis.It provides the theoretical and methodological basis for the wireless localization uncertainty analysis.And on this basis,for the static localization and mobile localization problems in smart storage,corresponding optimal localization methods are proposed,and verified and evaluated by simulation the localization method proposed.And the experimental results show that the wireless distributed localization method designed in this paper can obtain high-precision localization results in a complex localization environment,and has certain adaptability and restraint for the complex localization environment.It is suitable for the application of the localization method to Intelligent warehousing provides an important theoretical and methodological basis.