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Researches On Key Techniques Of Android Smart Device Precise Positioning

Posted on:2020-01-05Degree:MasterType:Thesis
Country:ChinaCandidate:K S ZhangFull Text:PDF
GTID:2428330620453198Subject:Control Science and Engineering
Abstract/Summary:PDF Full Text Request
In recent years,with the rapid development of electronic communication technology,portable smart devices have been widely used by everyone.Smart devices,such as smartphones,tablets,smart-watches and driving computers,have become an indispensable part of modern public life,business and manufacture industry.Location-Based Service(LBS)based on smart devices has greatly promoted the rapid transformation of commerce,media communication,transportation,engineering construction,agriculture and forestry planting.It is also going to have a profound influence on science research,environmental resources survey and military information construction.The wide application and broad prospects of LBS,make precise positioning technology of smart devices gradually become an international research hotspot in the field of navigation.Android smart device has been able to provide location information since years before.The accuracy of Android chipset positioning is about 5-10 m.However,with the limitation of limitation of technology,the process of Android chipset positioning is not open-source.Developers can only obtain the positioning result,but cannot read the GNSS measurements directly.In 2016,Google Inc.released Android 7.0 operating system.As the smart operating system with the highest market share,the new version of Android system began to support the output of GNSS(Global Navigation Satellite System)raw measurements by smart devices,which made it possible to achieve precise positioning on smart devices.To meet the navigation application requirements of mobile smart devices this paper systematically studies the principles and methods of GNSS precise positioning,SINS and low-cost GNSS/SINS integrated navigation.The quality and characteristics of GNSS observations of Android smart devices is also assessed to figure out the character of Android GNSS measurement.Based on the comprehensive evaluation,Smart RTK method is proposed to optimize the GNSS dynamic differential positioning on Android smart devices.Smart RTK method combines Doppler-Smoothed-Code filter(DSC filter),Carrier-to-Noise Ratio(C/N0)based stochastic model and Constant Acceleration model(CA model).This method can significantly improve the positioning accuracy and reliability of Android smart terminal,and can achieve decimeter-level kinematic positioning in good environment.Additionally,the gyroscopes and accelerometers on Android smart devices are also fully used with the simplified low-cost SINS algorithm and the GNSS/SINS integrated navigation algorithm to achieve Smart RTK/SINS integrated navigation of smart devices.The main work and contributions of this paper are as follows:(1)Comprehensively assess the quality of GPS and BDS observation of Android smart devices by carefully evaluating the C/N0,code measurement noise,carrier phase noise,Doppler measurement noise and carrier phase cycle slip.The experimental results show that the C/NO value of Android smart devices is much lower than that of geodetic GNSS receivers.The code measurement noise is up to about 4 m,the RMS of Doppler measurement noise is about 0.039 m/s,and the carrier phase cycle slip occurs more frequently.Due to the limitation of its low-cost hardware,the GNSS measurement quality of Android smart devices still lags behind that of geodetic GNSS receiver,which is supposed to be improved and optimized urgently to meet the requirements of GNSS precise positioning.(2)An optimized solution for GNSS kinematic differential positioning of GNSS in Android smart devices,named Smart RTK,is proposed in this dissertation.The method uses DSC filter to optimize the obtained pseudorange observations,and then weights the observations according to the C/NO based stochastic model.After obtaining the initial information,the CA model is used to predict the kinematic state,and epoch-by-epoch updates kinematic parameter of the carrier.In order to comprehensively analyze the positioning performance of Smart RTK on Android smart devices,the dynamic positioning experiments in pseudo-dynamic,walking and vehicular environments were set up.In the pseudo-kinematic experiment,the performance of Smart RTK method is significantly better than that of system output and conventional RTK method,and it can achieve horizontal decimeter-level accuracy in a short time.In the walking kinematic experiment,Smart RTK can achieve decimeter-level horizontal positioning accuracy,82%better than Chipset Fused Positioning and 56%better than Ordinary RTK method.In the vehicle experiment,Smart RTK reaches decimeter-level horizontal positioning accuracy,59%higher than that of Chipset Fused Positioning and 49%higher than that of Ordinary RTK method.(3)The Smart RTK/SINS integrated navigation of Android intelligent terminal is achieved by using the simplified method of integrated navigation for low cost terminals.Under complex observation conditions such as urban canyons and forest parks,the multi-path effect of GNSS signal is aggravated,the number of trackable navigation satellites significantly reduced,and the accuracy of satellite positioning is poor.So it is difficult to output valid GNSS positioning solutions in this case.Smart RTK/SINS integrated navigation system makes full use of the inertial measurement elements of Android intelligent terminal,which can significantly improve its positioning performance.In pseudo-dynamic experiment,Smart RTK/SINS reaches a positioning RMSE of 0.20 m in the East and 0.30 m in the north.And its horizontal positioning accuracy is about 47%higher than that of pure GNSS positioning.In walking dynamic experiment,Smart RTK/SINS reaches a positioning RMSE of 1.87 m in the East and RMSE 1.52 m in the north,and its horizontal positioning accuracy improves about 80%comparing with pure GNSS positioning.
Keywords/Search Tags:Android smart device, Kinematic differential positioning, Doppler-smoothed-code filter, Constant acceleration model, GNSS/IMU integrated navigation
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