IMU-Aided Precise Point Positioning Performance Assessment with Smartphones in GNSS-Degraded Urban Environments

Abstract

The tracking of satellite signals with the passive linearly polarized embedded global navigation satellite system (GNSS) antenna of smartphones in dynamic scenarios is susceptible to the changing multipath and obstructions in urban environments, which lead to a significant decrease in the availability and reliability of GNSS solutions. Accordingly, based on the characteristics of smartphone GNSS and inertial measurement unit (IMU) sensors data in GNSS-degraded environments, we established an IMU-aided uncombined precise point positioning (PPP) mathematical model that is suitable for smartphones. To enhance the reliability of initial alignment in dynamic mode, the step function variances depending on carrier-to-noise density ratio were established with the variances of GNSS measurements, and the inertial navigation system (INS) parameters were initialized while both the velocity of smartphones and the position dilution of precision (PDOP) reached corresponding thresholds. Considering the measurement noise and observations gaps of smartphones, the robust Kalman filter (RKF) with equivalent variance matrix was used for parameter estimation to improve the convergence efficiency of the coupled PPP/INS model. Experimental results indicated that the proposed PPP/INS method can effectively improve the positioning performance of smartphones in GNSS-degraded environments. Compared with the conventional smartphone PPP scheme, the PPP/INS horizontal errors in the eastern and western areas of the long trajectory experiment decreased by 49.37% and 48.29%, respectively. Meanwhile, the trajectory deviation of smartphones can remain stable in the tunnel where GNSS signals are blocked.

Static Positioning under Tree Canopy Using Low-Cost GNSS Receivers and Adapted RTKLIB Software

“…The performance under vegetation canopy must be further studied. Considering the smartphone receivers, utilization of raw GNSS data is being studied with aim to provide kinematic/real-time solutions [ 8 , 23 , 49 , 66 ]. A periodically held Google Decimeter Challenge [ 67 ] provides a very good overview of new approaches focused on post-processing of kinematic smartphone GNSS measurements.…”

“…The other group of studies is focused on practical applications, taking into account many differing positioning approaches, including single point positioning (SPP) [ 11 , 12 ], precise point positioning (PPP) [ 13 , 14 ], differential GNSS (DGNSS) [ 15 , 16 ], real-time kinematics (RTK) [ 17 , 18 ], post-processed kinematics (PPK) [ 19 ] and static (carrier-phase based) positioning [ 20 ]. To supplement the low-quality GNSS data from smartphones, authors have recently focused on combinations with other sensors usable for positioning, e.g., inertial measurement units (IMU) [ 21 , 22 , 23 ]. Other approaches include, e.g., the aid of 3D maps [ 24 ] and collaborative smartphone positioning [ 1 ].…”

GNSS Observation Generation from Smartphone Android Location API: Performance of Existing Apps, Issues and Improvement

“…According to the results, applying smart device-level GNSS observations in ionospheric studies is feasible. Zhu et al [ 27 ] proposed an inertial measurement unit (IMU)-aided uncombined PPP coupled mathematical model, suitable for smartphone positioning. The proposed PPP/INS-coupled model integrated the dual-frequency GNSS observations and IMU data from smartphones with C/N0-dependent stochastic model and robust Kalman filter (RKF) model to improve the positioning performance under GNSS-degraded environments.…”

Improving smartphone-based positioning accuracy with height constraint and application to pedestrian and vehicular positioning

“…They can be integrated with the smartphone GNSS observables to achieve a better localization solution. Zhu et al (2022) proposed an IMU-aided uncombined PPP coupled mathematical model, suitable for smartphone positioning. The proposed PPP/INS-coupled model integrated the dualfrequency GNSS observations and IMU data from smartphones with C/N0-dependent stochastic model and robust Kalman filter (RKF) model to improve the positioning performance under GNSS-degraded environments.…”