Single-Frequency Divergence-free Hatch Filter for the Android N GNSS Raw Measurements

Shin, Donghyun; Lim, Cheolsoon; Park, Byungwoon; Yun, Young-Sun; Kim, Euiho; Kee, Changdon

doi:10.33012/2017.15360

Cited by 5 publications

(4 citation statements)

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“…Therefore, an elevationdependent weighting is not proper for low-cost receivers while a C/N 0 weighting would be a better choice for these devices. Banville et al (2019) Shin et al (2017) introduced a new filtering algorithm to smooth the single-frequency pseudorange observations of the Android devices. The method is almost not affected by the ionospheric variations.…”

Section: Absolute Positioningmentioning

confidence: 99%

“…The method can reduce the noise level of the GNSS pseudorange observations but causing an ionosphere-induced error especially for the lowelevation angles satellites. Therefore, Shin et al (2017) proposed a new single-frequency divergence-free Hatch filter method with the aim of reducing the effect of ionosphere-induced error based on the grid ionospheric vertical error (GIVE) from the SBAS messages. The new method was then applied to the raw measurement of a Nexus 9 device to investigate its efficiency compared with the classical Hatch filter.…”

Section: Absolute Positioningmentioning

confidence: 99%

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GNSS smartphones positioning: advances, challenges, opportunities, and future perspectives

2021

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Starting from 2016, the raw Global Navigation Satellite System (GNSS) measurements can be extracted from the Android Nougat (or later) operating systems. Since then, GNSS smartphone positioning has been given much attention. A high number of related publications indicates the importance of the research in this field, as it has been doing in recent years. Due to the cost-effectiveness of the GNSS smartphones, they can be employed in a wide variety of applications such as cadastral surveys, mapping surveying applications, vehicle and pedestrian navigation and etc. However, there are still some challenges regarding the noisy smartphone GNSS observations, the environment effect and smartphone holding modes and the algorithm development part which restrict the users to achieve high-precision smartphone positioning. In this review paper, we overview the research works carried out in this field with a focus on the following aspects: first, to provide a review of fundamental work on raw smartphone observations and quality assessment of GNSS observations from major smart devices including Google Pixel 4, Google Pixel 5, Xiaomi Mi 8 and Samsung Ultra S20 in terms of their signal strengths and carrier-phase continuities, second, to describe the current state of smartphone positioning research field until most recently in 2021 and, last, to summarize major challenges and opportunities in this filed. Finally, the paper is concluded with some remarks as well as future research perspectives.

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Section: Absolute Positioningmentioning

confidence: 99%

Section: Absolute Positioningmentioning

confidence: 99%

GNSS smartphones positioning: advances, challenges, opportunities, and future perspectives

2021

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“…As the single difference between epochs is used, ambiguity has been subtracted when no cycle slips occur. Previous research has proposed that for Android devices, the receiver clock bias in the carrier phase raw observation may not be the same as the clock bias in the pseudorange [22]. To avoid the possible influence of this inequality, we use the single difference between satellites.…”

Section: Phase-smoothed Pseudorangementioning

confidence: 99%

“…H. K. Lee proposed an improved position-domain Hatch filter, which is advantageous in real-time kinematic positioning and cycle slip detection based on dual frequency observations [21]. Besides, previous research has proposed that for Android smart devices, the clock bias in the carrier phase observation is not the same as the clock bias in the pseudorange observation [22]. Therefore, we construct single-difference observations between satellites to weaken this inequality.…”

Section: Introductionmentioning

confidence: 99%

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

Geng

Jiang

et al. 2019

Remote Sensing

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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.

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Key Technologies Analysis and Market Status of High Precision Positioning Based on Mobile Phone

Liu

Yang

et al. 2020

Lecture Notes in Electrical Engineering

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Single-Frequency Divergence-free Hatch Filter for the Android N GNSS Raw Measurements

Cited by 5 publications

References 0 publications

GNSS smartphones positioning: advances, challenges, opportunities, and future perspectives

GNSS smartphones positioning: advances, challenges, opportunities, and future perspectives

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

Key Technologies Analysis and Market Status of High Precision Positioning Based on Mobile Phone

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