Integration of Multi-Constellation GNSS Precise Point Positioning and MEMS-Based Inertial Systems Using Tightly Coupled Mechanization

We develop a new integrated navigation system, which integrates multi-constellations GNSS precise point positioning (PPP), including GPS, GLONASS and Galileo, with low-cost micro-electro-mechanical sensor (MEMS) inertial system, for precise positioning applications. To integrate GNSS and the MEMS-based inertial system, the process and measurement models are developed. Tightly coupled mechanism is adopted, which is carried out in the GNSS raw measurements domain. Both un-differenced and between-satellite single-difference (BSSD) ionosphere-free linear combinations of pseudorange and carrier phase GNSS measurements are processed. Rigorous models are employed to correct GNSS errors and biases. The GNSS inter-system biases are considered as additional unknowns in the integrated error state vector. The developed stochastic model for inertial sensors errors and biases are defined based on first order Gaussian Markov process. Extended Kalman filter is developed to integrate GNSS and inertial measurements and estimate inertial measurements biases and errors. Two field experiments are executed, which represent different real-world scenarios in land-based navigation. The data are processed by using our developed Ryerson PPP GNSS/MEMS software. The results indicate that the proposed integrated system achieves decimeter to centimeter level positioning accuracy when the measurement updates from GNSS are available. During complete GNSS outages the developed integrated system continues to achieve decimeter level accuracy for up to 30 seconds while it achieves meter-level accuracy when a 60-second outage is introduced.

PPP Accuracy Enhancement Using GPS/GLONASS Observations in Kinematic Mode

Commonly, kinematic PPP techniques employ un-differenced ionosphere-free linear combination of GPS observations. This, however, may not provide continuous solution in urban areas as a result of limited satellite visibility. In this paper, the traditional un-differenced as well as between-satellite single-difference (BSSD) ionosphere-free linear combinations of GPS and GLONASS measurements are developed. Except GLONASS satellite clock products, the final precise GPS and GLONASS satellites clock and orbital products obtained from the multi-GNSS experiment (MGEX) are used. The effects of ocean loading, earth tide, carrier-phase windup, sagnac, relativity, and satellite and receiver antenna phase-center variations are rigorously modeled. Extended Kalman filter (EKF) is developed to process the combined GPS/GLONASS measurements. A comparison is made between three kinematic PPP solutions, namely standalone GPS, standalone GLONASS, and combined GPS/ GLONASS solutions. In general, the results indicate that the addition of GLONASS observations improves the kinematic positioning accuracy in comparison with the standalone GPS PPP positioning accuracy. In addition, BSSD solution is found to be superior to that of the traditional un-diffe- renced model.

Improved Between-Satellite Single-Difference Precise Point Positioning Model Using Triple GNSS Constellations: GPS, Galileo, and BeiDou

This paper introduces a new dual-frequency precise point positioning (PPP) model, which combines the observations of three different GNSS constellations, namely GPS, Galileo, and BeiDou. Our model is based on between-satellite single-difference (BSSD) linear combination, which cancels out some receiver-related biases, including receiver clock error and non-zero initial phase bias of the receiver oscillator. The reference satellite can be selected from any satellite system GPS, Galileo, and BeiDou when forming BSSD linear combinations. Natural Resources Canada’s GPS Pace PPP software is modified to enable a combined GPS, Galileo, and BeiDou PPP solution and to handle the newly introduced biases. A total of four data sets at four IGS stations are processed to verify the developed PPP model. Precise satellite orbit and clock products from the IGS-MGEX network are used to correct both of the GPS and Galileo measurements. It is shown that using the BSSD linear combinations improves the precision of the estimated parameters by about 25% compared with the GPS-only PPP solution. Additionally, the solution convergence time is reduced to 10 minutes for both BSSD scenarios, which represent about 50% improvement in comparison with the GPS-only PPP solution.

An Improved Model for Single-Frequency GPS/GALILEO Precise Point Positioning

This paper introduces a new precise point positioning (PPP) model, which combines single-fre- quency GPS/Galileo observations in between-satellite single-difference (BSSD) mode. In the absence of multipath, all receiver-related errors and biases are cancelled out when forming BSSD for a specific constellation. This leaves the satellite originating errors and atmospheric delays un- modelled. Combining GPS and Galileo observables introduces additional biases that have to be modelled, including the GPS to Galileo time offset (GGTO) and the inter-system bias. This paper models all PPP errors rigorously to improve the single-frequency GPS/Galileo PPP solution. GPSPace PPP software of Natural Resources Canada (NRCan) is modified to enable a GPS/Galileo PPP solution and to handle the newly introduced biases. A total of 12 data sets representing the GPS/Galileo measurements of six IGS-MEGX stations are processed to verify the newly developed PPP model. Precise satellite orbit and clock corrections from IGS-MEGX networks are used for both GPS and Galileo measurements. It is shown that sub-decimeter level accuracy is possible with single-frequency GPS/Galileo PPP. In addition, the PPP solution convergence time is improved from approximately 100 minutes for the un-differenced single-frequency GPS/Galileo solution to approximately 65 minutes for the BSSD counterpart when a single reference satellite is used. Moreover, an improvement in the PPP solution convergence time of 35% and 15% is obtained when one and two reference satellites are used, respectively.

Real-Time GPS/Galileo Precise Point Positioning Using NAVCAST Real-Time Corrections

Real-time precise point positioning (PPP) is possible through the use of real-time precise satellite orbit and clock corrections, which are available through a number of organizations including the International GNSS Service (IGS) real-time service (IGS-RTS). Unfortunately, IGS-RTS is only available for the GPS and GLONASS constellations. In 2018, a new real-time service, NAVCAST, which provides real-time precise orbit and clock corrections for the GPS and Galileo constellations, was launched. In this research, the potential performance of real-time PPP which makes use of NAVCAST real-time corrections is analyzed using various static and kinematic datasets. In the static dataset, 24 hours of observations from eight IGS stations in Canada over three different days were utilized. The static results show that the contribution of Galileo satellites can improve the positioning accuracy, with 30%, 34%, and 31% in east, north, and up directions compared to the GPS-only counterparts. In addition, centimeter-level positioning accuracy in the horizontal direction and decimeter-level positioning accuracy in the vertical direction can be achieved by adding Galileo observations. In the kinematic dataset, a real vehicular test was conducted in urban and suburban combined areas. The real-time kinematic GPS/Galileo PPP solutions demonstrate an improvement of about 53%, 45%, and 70% in east, north, and up directions compared to the GPS-only counterparts. It is shown that the real-time GPS/Galileo PPP can achieve a sub-decimeter horizontal positioning accuracy and about meter-level vertical positioning accuracy through the use of NAVCAST real-time corrections.

Monocular VO Scale Ambiguity Resolution Using an Ultra Low-Cost Spike Rangefinder

Monocular visual odometry (VO) is the process of determining a user’s trajectory through a series of consecutive images taken by a single camera. A major problem that affects the accuracy of monocular visual odometry, however, is the scale ambiguity. This research proposes an innovative augmentation technique, which resolves the scale ambiguity problem of monocular visual odometry. The proposed technique augments the camera images with range measurements taken by an ultra-low-cost laser device known as the Spike. The size of the Spike laser rangefinder is small and can be mounted on a smartphone. Two datasets were collected along precisely surveyed tracks, both outdoor and indoor, to assess the effectiveness of the proposed technique. The coordinates of both tracks were determined using a total station to serve as a ground truth. In order to calibrate the smartphone’s camera, seven images of a checkerboard were taken from different positions and angles and then processed using a MATLAB-based camera calibration toolbox. Subsequently, the speeded-up robust features (SURF) method was used for image feature detection and matching. The random sample consensus (RANSAC) algorithm was then used to remove the outliers in the matched points between the sequential images. The relative orientation and translation between the frames were computed and then scaled using the spike measurements in order to obtain the scaled trajectory. Subsequently, the obtained scaled trajectory was used to construct the surrounding scene using the structure from motion (SfM) technique. Finally, both of the computed camera trajectory and the constructed scene were compared with ground truth. It is shown that the proposed technique allows for achieving centimeter-level accuracy in monocular VO scale recovery, which in turn leads to an enhanced mapping accuracy.