The Design a TDCP-Smoothed GNSS/Odometer Integration Scheme with Vehicular-Motion Constraint and Robust Regression

Abstract

Global navigation satellite system (GNSS) is widely regarded as the primary positioning solution for intelligent transport system (ITS) applications. However, its performance could degrade, due to signal outages and faulty-signal contamination, including multipath and non-line-of-sight reception. Considering the limitation of the performance and computation loads in mass-produced automotive products, this research investigates the methods for enhancing GNSS-based solutions without significantly increasing the cost for vehicular navigation system. In this study, the measurement technique of the odometer in modern vehicle designs is selected to integrate the GNSS information, without using an inertial navigation system. Three techniques are implemented to improve positioning accuracy; (a) Time-differenced carrier phase (TDCP) based filter: A state-augmented extended Kalman filter is designed to incorporate TDCP measurements for maximizing the effectiveness of phase-smoothing; (b) odometer-aided constraints: The aiding measurement from odometer utilizing forward speed with the lateral constraint enhances the state estimation; the information based on vehicular motion, comprising the zero-velocity constraint, fault detection and exclusion, and dead reckoning, maintains the stability of the positioning solution; (c) robust regression: A weighted-least-square based robust regression as a measurement-quality assessment is applied to adjust the weightings of the measurements adaptively. Experimental results in a GNSS-challenging environment indicate that, based on the single-point-positioning mode with an automotive-grade receiver, the combination of the proposed methods presented a root-mean-square error of 2.51 m, 3.63 m, 1.63 m, and 1.95 m for the horizontal, vertical, forward, and lateral directions, with improvements of 35.1%, 49.6%, 45.3%, and 21.1%, respectively. The statistical analysis exhibits 97.3% state estimation result in the horizontal direction for the percentage of epochs that had errors of less than 5 m, presenting that after the intervention of proposed methods, the positioning performance can fulfill the requirements for road level applications.

GNSS Odometry: Precise Trajectory Estimation Based on Carrier Phase Cycle Slip Estimation

“…On the other hand, the relative position and velocity estimation using TDCP has been studied [3], [4], [12]- [15]. The time-sequential carrier phase difference contains the relative distance between the satellite and receiving antenna with high accuracy if the carrier phase ambiguity is time-invariant.…”

“…In recent years, a method called time differences of carrier phases (TDCP), which uses the time difference of GNSS carrier phase measurements, has been actively studied as a high-precision relative position estimation method using a single GNSS receiver [3], [4]. The TDCP-based technique can estimate the relative position (velocity) with high accuracy by canceling the ionospheric delay, tropospheric delay, satellite clocks and orbit errors, etc.…”

A zero-velocity update method based on neural network and Kalman filter for vehicle-mounted inertial navigation system

“…In [13],a hybrid filter combining an unscented particle filter (UPF) and an unscented Kalman filter (UKF) was proposed for integrating the global positioning system (GPS) and vehicle micro electromechanical inertial navigation system (MINS). The positioning accuracy of this method was improved by about 55% compared with the single UPF in the case of GPS failure [14]. The stationary state is detected by the odometer and the estimation accuracy of the extended Kalman filter (EKF) is improved according to the lateral constraint.…”

A Doppler enhanced TDCP algorithm based on terrain adaptive and robust Kalman filter using a stand-alone receiver

“…The accuracy of former is low, while the latter uses multiple receivers, which accuracy is limited by the distance from rover receiver to reference receiver. Since TDCP was proposed, it has received extensive attention (Wendel et al, 2006;Soon et al, 2008;Ding and Wang, 2011;Freda et al, 2015;Chiang et al, 2020;Li et al, 2020). Benefiting from high-precision carrier phase observations, TDCP can provide velocity estimation accuracy within 1 cm per second in low speed and open environments (Ding and Wang, 2011).…”