An Improved Hatch Filter Algorithm towards Sub-Meter Positioning Using only Android Raw GNSS Measurements without External Augmentation Corrections

Abstract

In May 2016, the availability of GNSS raw measurements on smart devices was announced by Google with the release of Android 7. It means that developers can access carrier-phase and pseudorange measurements and decode navigation messages for the first time from mass-market Android-devices. In this paper, an improved Hatch filter algorithm, i.e., Three-Thresholds and Single-Difference Hatch filter (TT-SD Hatch filter), is proposed for sub-meter single point positioning with raw GNSS measurements on Android devices without any augmentation correction input, where the carrier-phase smoothed pseudorange window width adaptively varies according to the three-threshold detection for ionospheric cumulative errors, cycle slips and outliers. In the mean time, it can also eliminate the inconsistency of receiver clock bias between pseudorange and carrier-phase by inter-satellite difference. To eliminate the effects of frequent smoothing window resets, we combine TT-SD Hatch filter and Kalman filter for both time update and measurement update. The feasibility of the improved TT-SD Hatch filter method is then verified using static and kinematic experiments with a Nexus 9 Android tablet. The result of the static experiment demonstrates that the position RMS of TT-SD Hatch filter is about 0.6 and 0.8 m in the horizontal and vertical components, respectively. It is about 2 and 1.6 m less than the GNSS chipset solutions, and about 10 and 10 m less than the classical Hatch filter solution, respectively. Moreover, the TT-SD Hatch filter can accurately detect the cycle slips and outliers, and reset the smoothed window in time. It thus avoids the smoothing failure of Hatch filter when a large cycle-slip or an outlier occurs in the observations. Meanwhile, with the aid of the Kalman filter, TT-SD Hatch filter can keep continuously positioning at the sub-meter level. The result of the kinematic experiment demonstrates that the TT-SD Hatch filter solution can converge after a few minutes, and the 2D error is about 0.9 m, which is about 64%, 89%, and 92% smaller than that of the chipset solution, the traditional Hatch filter solution and standard single point solution, respectively. Finally, the TT-SD Hatch filter solution can recover a continuous driving track in this kinematic test.

Testing of Software for the Planning of a Linear Object GNSS Measurement Campaign under Simulated Conditions

“…It should be noted, however, that a single satellite is capable of transmitting several signals on multiple frequencies at the same time, which considerably increases the number of pseudorange measurements that can be used by the receiver. This is why multi-GNSS receivers are now widely used in car navigation systems, other mobile applications, smartphones and sports [3][4][5][6][7][8], as well as even including modern dual-frequency solutions [9][10][11][12]. Their very rapid development, associated with increasing the positioning accuracy, particularly in urbanised areas, results primarily from the rapidly increasing number of satellites of systems under construction [13][14][15][16][17][18].…”

A Novel Dynamical Filter Based on Multi-Epochs Least-Squares to Integrate the Carrier Phase and Pseudorange Observation for GNSS Measurement

“…Thus, forming a new type of GNSS observation based on integrating carrier phase observation and pseudorange is critical for GNSS (Global Navigation Satellite System) due to the ambiguity of the carrier phase and the high noise of the pseudorange [10]. The technology can absorb the advantages of the typical GNSS measurements, without ambiguity and high precision, and thereby forming a new pseudorange to aid cycle slip detection and repair [11], fixing ambiguity [12] and improving positioning accuracy [13]. Hatch [7] firstly designed a smoother on the basis that the variation between the two observations is equal within a period, with two main issues: (i) how to determine the smoothed pseudorange of first epoch and the smoothing time, and (ii) that it neglects the error sources, such as ionospheric variations and multipath at the period [14][15][16].…”

An Improved Divergence-Free Hatch Filter Algorithm Toward Sub-Meter Train Positioning With GNSS Single- Frequency Observations Only

“…Huang et al [14] comprehensively used satellite elevation angle, external augmentation information, and the distance change from the user to the base station to calculate the width of the smoothing window for a ground-based augmentation system (GBAS). Geng et al [15] proposed the Three-Thresholds and Single-Difference (TT-SD) Hatch filter to achieve sub-meter positioning with Android devices. Zhou et al [16] suggested using the Doppler phase difference observations rather than the carrier-phase observations to estimate the window width in kinematic positioning.…”

“…Thus, the width of the sliding ionospheric variation fitting window is long, and a 20-minute initialization time is required in realtime positioning to achieve the desired accuracy, which is unbearable for train positioning. Besides, this study used the ordinary least square method for parameter estimation, which makes it challenging to keep continuous positioning at the desired accuracy when the data quality is poor [15].…”