Supporting GNSS Baseband Using Smartphone IMU and Ultra-Tight Integration

This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible. Abstract—A great surge of the global navigation satellite system (GNSS) development excavates the potential of promoting pomposity in many state-of-art technologies, e.g., autonomous ground vehicles (AGVs). Nevertheless, the GNSS is fragile to the various ground interferences which significantly break down the continuity of the navigation system. Meanwhile, the GNSS-based next-generation navigation devices are being developed to be smaller, more low-cost, and lightweight as forecasted by the commercial market. This work aims to answer the question of whether the smartphone inertial measurement unit (IMU) is sufficient to support the GNSS baseband. Thus, a cascaded ultra-tightly integrated GNSS/inertial navigation system (INS) technique, where the consumer-level smartphone sensors are used, is proposed to improve the baseband performance of GNSS software-defined radios (SDRs). To integrate the GNSS baseband, a Doppler value is predicted based on an integrated extended Kalman filter (EKF) navigator where the pseudo-range-state-based measurements of GNSS and INS are fused, and it is used to assist the numerically controlled oscillator (NCO) algorithms. Then, an ultra-tight integration platform is built with an upgraded GNSS SDR of which baseband processing is integrated with the INS mechanization algorithm. Finally, by comparing with the previous algorithms, both tracking-level and carrier-based positioning performances are assessed in the proposed platform for the smartphone-IMU-aided GNSS baseband via kinematic AGV field tests. The experimental results demonstrate the performance of the tracking ability and the high-precision positioning of the proposed ultra-tight integration algorithms using the smartphone IMU.

[1]  Jennifer Denise Gautier,et al.  GPS/INS generalized evaluation tool (GIGET) for the design and testing of integrated navigation systems , 2003 .

[2]  M. Petovello,et al.  A Stand-Alone Approach for High-Sensitivity GNSS Receivers in Signal-Challenged Environment , 2017, IEEE Transactions on Aerospace and Electronic Systems.

[3]  Xiaoji Niu,et al.  High-Accuracy Positioning in Urban Environments Using Single-Frequency Multi-GNSS RTK/MEMS-IMU Integration , 2018, Remote. Sens..

[4]  Jianye Liu,et al.  Adaptive robust ultra-tightly coupled global navigation satellite system/inertial navigation system based on global positioning system/BeiDou vector tracking loops , 2014 .

[5]  Bin Jiang,et al.  Performance analysis of a federated ultra-tight global positioning system/inertial navigation system integration algorithm in high dynamic environments , 2015 .

[6]  Mark G. Petovello,et al.  Comparison of Vector-Based Software Receiver Implementations With Application to Ultra-Tight GPS/INS Integration , 2006 .

[7]  Yong Luo,et al.  A double-filter-structure based COMPASS/INS deep integrated navigation system implementation and tracking performance evaluation , 2012, Science China Information Sciences.

[8]  Yong Luo,et al.  Double-filter model with modified Kalman filter for baseband signal pre-processing with application to ultra-tight GPS/INS integration , 2012, GPS Solutions.

[9]  Tomoji Takasu,et al.  Development of the low-cost RTK-GPS receiver with an open source program package RTKLIB , 2009 .

[10]  Giancarmine Fasano,et al.  Multi-UAV Carrier Phase Differential GPS and Vision-based Sensing for High Accuracy Attitude Estimation , 2018, Journal of Intelligent & Robotic Systems.

[11]  Jian Li,et al.  Research on Time-Correlated Errors Using Allan Variance in a Kalman Filter Applicable to Vector-Tracking-Based GNSS Software-Defined Receiver for Autonomous Ground Vehicle Navigation , 2019, Remote. Sens..

[12]  Long Zhao,et al.  A GNSS/INS-integrated system for an arbitrarily mounted land vehicle navigation device , 2019, GPS Solutions.

[13]  Han Zhang,et al.  Sample-Wise Aiding in GPS/INS Ultra-Tight Integration for High-Dynamic, High-Precision Tracking , 2016, Sensors.

[14]  Jian Li,et al.  A GNSS Software-Defined Receiver with Vector Tracking Techniques for Land Vehicle Navigation , 2019, Proceedings of the ION 2019 Pacific PNT Meeting.

[15]  Naser El-Sheimy,et al.  Assessment of Ultra-Tightly Coupled GNSS/INS Integration System towards Autonomous Ground Vehicle Navigation Using Smartphone IMU , 2019, 2019 IEEE International Conference on Signal, Information and Data Processing (ICSIDP).

[16]  Matthew Lashley,et al.  Modeling and Performance Analysis of GPS Vector Tracking Algorithms , 2009 .

[17]  Yang Gao,et al.  Improvement of carrier phase tracking in high dynamics conditions using an adaptive joint vector tracking architecture , 2018, GPS Solutions.

[18]  Tao Lin,et al.  Detection and Mitigation of Spoofing Attacks on a Vector-Based Tracking GPS Receiver , 2012 .

[19]  Senlin Peng,et al.  A multiple-frequency GPS software receiver design based on a Vector tracking loop , 2012, Proceedings of the 2012 IEEE/ION Position, Location and Navigation Symposium.

[20]  Mark G. Petovello,et al.  Carrier Phase Tracking of Weak Signals Using Different Receiver Architectures , 2008 .

[21]  Xianpeng Wang,et al.  Anti-jamming scheme for GPS receiver with vector tracking loop and blind beamformer , 2014 .

[22]  Ramsey Michael Faragher,et al.  Understanding the Basis of the Kalman Filter Via a Simple and Intuitive Derivation [Lecture Notes] , 2012, IEEE Signal Processing Magazine.

[23]  Susmita Bhattacharyya,et al.  Performance and integrity analysis of the vector tracking architecture of GNSS receivers , 2012 .

[24]  Mark G. Petovello,et al.  Ultra-tight GPS/INS Receiver Performance in the Presence of Jamming Signals , 2014 .

[25]  P. Groves Principles of GNSS, Inertial, and Multi-Sensor Integrated Navigation Systems , 2007 .

[26]  Li-Ta Hsu,et al.  GNSS NLOS Pseudorange Correction based on Skymask for Smartphone Applications , 2019, Proceedings of the 32nd International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2019).

[27]  Gérard Lachapelle,et al.  Integration of GNSS and INS with a phased array antenna , 2017, GPS Solutions.

[28]  Li-Ta Hsu,et al.  Assessment of the Multipath Mitigation Effect of Vector Tracking in an Urban Environment , 2013 .

[29]  Gérard Lachapelle,et al.  Evaluation of a Low Cost Hand Held Unit with GNSS Raw Data Capability and Comparison with an Android Smartphone , 2018, Sensors.

[30]  Li-Ta Hsu,et al.  Extending Shadow Matching to Tightly-Coupled GNSS/INS Integration System , 2020, IEEE Transactions on Vehicular Technology.

[31]  Tao Lin,et al.  Design and Implementation of an RTK-Based Vector Phase Locked Loop , 2018, Sensors.

[32]  Li-Ta Hsu,et al.  Vector Tracking Loop-Based GNSS NLOS Detection and Correction: Algorithm Design and Performance Analysis , 2020, IEEE Transactions on Instrumentation and Measurement.

[33]  Li-Ta Hsu,et al.  Open-source MATLAB code for GPS vector tracking on a software-defined receiver , 2019, GPS Solutions.

[34]  Per Enge,et al.  Fundamentals Of Signal Tracking Theory , 1996 .