The Research On MEMS Research On MEMS Assisted UWB In The Optimization Of Indoor Location

In recent years,as more and more researchers focus on indoor positioning of wireless communications,a number of advanced wireless positioning technologies have appeared such as Infrared,WIFI(Wireless Fidelity),RFID(Radio Frequency Identification),UWB(Ultra Wideband,UWB),Zigbee and Bluetooth.Among them,UWB as one of the main positioning technologies,has the characteristics of high transmission rate,low power consumption and strong penetrating capability,and can provide accurate positioning and dynamic tracking for moving targets in a small space,closed and complicated in internal structure indoor environment.At present,the research on the positioning system for UWB technology is relatively mature,nevertheless there are some problems also found in practical applications.For example,when UWB signals are transmitted in complicated areas such as computer labs and factories,the signal strength is largely decay or even distorted due to interference,absorption and attenuation,therefore resulting in serious deviation of the collected measurement data and fail to deduce the coordinates of the situation,and ultimately affect the positioning effect.In view of the problems above,this paper presents an improved mechanism algorithm of a MEMS-based UWB indoor positioning system,which is based on MEMS(Micro-Electro-Mechanical System)inertial sensor with the feature of fully autonomous navigation,continuous positioning and the positioning results not affected by the external environment.When the UWB signal is bad or even missing and cannot provide accurate and continuous positioning information for the system,MEMS can be used to collect moving target information,and to collect data and complement the positioning information to ensure positioning effect by means of the MEMS and the integration of UWB are filtered by data fusion.In this paper,by comparing the traditional inertial navigation algorithm based on MEMS sensors with the dead reckoning algorithm based on pedestrian motion model,and with the consideration of the precision requirements of positioning system,we chose the dead reckoning algorithm based on pedestrian motion model to achieve MEMS aided positioning.The design of MEMS and UWB indoor positioning algorithm is implemented as follows:Firstly,the initial state of hybrid Synthetical approach is given by using the measurements of MEMS and UWB sensors.Secondly,with the rules of the traditional gait detection and pedestrian movement,the"peaking detection+sliding window" method is used to obtain the parameters needed for pedestrian heading estimation,and simulate the pedestrians step by analyzing the relation between pedestrians step frequency and step length and pedestrians in indoor environment.Then,based on the acceleration information,angular velocity information and magnetic field intensity information collected by the MEMS sensor,combined with the Kalman filter principle,the heading estimation of pedestrians in real-time motion state is given.Finally,by means of data fusion filtering,UWB and MEMS hybrid navigation dead reckoning positioning dynamic adjustment method has presented.In this paper,the MEMS aided with UWB to get the positioning information,the heading test and positioning accuracy test were carried out,and the pedestrian track curve was drawn through the experimental data.The test results show that the pedestrian trajectory obtained by the optimization scheme based on MEMS and UWB positioning is more realistic than the trajectory of the UWB positioning alone.At the same time,through the statistics of positioning result data in unit time,it is found that the success rate of solution of hybrid positioning is obviously higher than that of UWB positioning alone.