Research On Wireless Location Technology In Complex Environment

With the rise of emerging technology industries such as "Internet+" and"Smart+",it is of great importance to provide accurate location information.However,the existing research results cannot meet the needs of the location information in some complex environments,including the obstacles in geographical conditions,such as valleys,disasters,etc.,as well as restricted spaces of man-made structures,such as indoors and tunnels.Meanwhile,the positioning of high-speed objects such as trains and airplanes are also included.In order to obtain accurate position information under these conditions,it is of great significance to study wireless positioning technology that can be applied in such complex environments.By studying the basic theory of wireless positioning and analyzing the existing research results at home and abroad,this paper studies the localization algorithm of train-mounted mobile terminal,train positioning technology and indoor positioning technology respectively.The research content and innovations of this paper are as follows:(1)When the train is running at high speed,the train-mounted mobile terminal moves freely,and the absolute position of the train is not the same as the position of the train-mounted mobile terminal.This paper proposes a relay positioning scheme for train-mounted mobile terminals,which uses relays in the sense of position to avoid the loss of signal propagation passing through the train wall and positions the train and train-mounted mobile terminals are located step-by-step.In order to solve the problem that when the train is in a complex geographical environment such as a valley,or the train and the base station are too far away to communicate directly,the UAVs are used to replace the reference nodes to form a wireless sensor network unit.In addition,the traditional RSSI ranging positioning algorithm is improved for the positioning error caused by the speed difference between the train and the UAV.The simulation results show that the improved positioning algorithm has higher positioning accuracy than that of the traditional RSSI positioning algorithm.(2)The inertial navigation systems for train positioning has cumulative errors and is only suitable for short-term positioning.In this paper,the strapdown inertial navigation system is integrated with the improved RSSI positioning algorithm,and the fusion positioning algorithm for train is proposed.In addition,by using the measurement result of the inertial measurement unit as the observation vector,the train running state model and observation model are established based on the hidden Markov model.The Gaussian mixture model is used to simulate the posterior probability function,and the maximum expected method is used to estimate the corresponding parameters to achieve the target tracking and positioning of the train.(3)In indoor spaces,non-line-of-sight and multipath have a serious impact on signal propagation.This paper designs and implements a semi-supervised learning indoor positioning system.The quality of data is classified,and the recursive feature elimination method is used to select features according to the marginal correlation.And then,the support vector machine theory is used generate the classifier for positioning.Finally,the implemented system is applied to the relay positioning scheme of the train-mounted mobile terminals.The simulation results show that the semi-supervised learning indoor positioning system can improve the positioning accuracy of the positioning scheme with relay overall by improving the local positioning algorithm.There are 36 figures,2 tables and 64 references in this paper.