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

2019 ◽  
Vol 11 (14) ◽  
pp. 1679 ◽  
Author(s):  
Jianghui Geng ◽  
Enming Jiang ◽  
Guangcai Li ◽  
Shaoming Xin ◽  
Na Wei
Keyword(s):  
Kalman Filter ◽  
Carrier Phase ◽  
Single Point ◽  
Smart Devices ◽  
Filter Method ◽  
Threshold Detection ◽  
Cycle Slips ◽  
Window Width ◽  
Filter Solution ◽  
Gnss Measurements

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.

scholarly journals Performance evaluation of GPS / BDS combined single-point positioning based on RTKLIB

2020 ◽  
Vol 165 ◽  
pp. 03009
Author(s):  
Li Yan-yi ◽  
Huang Jin ◽  
Tang Ming-xiu
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Single Point ◽  
Least Square Method ◽  
Least Square ◽  
Filter Method ◽  
Single System ◽  
Positioning Error ◽  
Dual Mode ◽  
Kalman Filter Method

In order to evaluate the performance of GPS / BDS, RTKLIB, an open-source software of GNSS, is used in this paper. In this paper, the least square method, the weighted least square method and the extended Kalman filter method are respectively applied to BDS / GPS single system for data solution. Then, the BDS system and GPS system are used for fusion positioning and the positioning results of the two systems are compared with that of the single system. Through the comparison of experiments, on the premise of using the extended Kalman filter method for positioning, when the GPS signal is not good, BDS data is introduced for dual-mode positioning, the positioning error in e direction is reduced by 36.97%, the positioning error in U direction is reduced by 22.95%, and the spatial positioning error is reduced by 16.01%, which further reflects the advantages of dual-mode positioning in improving a system robustness and reducing the error.

scholarly journals Kinematic ME-MAFA for Pseudolite Carrier-Phase Ambiguity Resolution in Precise Single-Point Positioning

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6197
Author(s):  
Kai Liu ◽  
Xiye Guo ◽  
Jun Yang ◽  
Xiaoyu Li ◽  
Changshui Liu ◽  
...  
Keyword(s):  
Success Rate ◽  
Ambiguity Resolution ◽  
Carrier Phase ◽  
Single Point ◽  
Heuristic Method ◽  
Voronoi Cell ◽  
Static Mode ◽  
Single Point Positioning ◽  
Cycle Slips ◽  
Point Positioning

Precise single-point positioning using carrier-phase measurements can be provided by the synchronized pseudolite system. The primary task of carrier phase positioning is ambiguity resolution (AR) with rapidity and reliability. As the pseudolite system is usually operated in the dense multipath environment, cycle slips may lead the conventional least-squares ambiguity decorrelation adjustment (LAMBDA) method to incorrect AR. A new AR method based on the idea of the modified ambiguity function approach (MAFA), which is insensitive to the cycle slips, is studied in this paper. To improve the model strength of the MAFA and to eliminate the influence of constant multipath biases on the time-average model in static mode, the kinematic multi-epoch MAFA (kinematic ME-MAFA) algorithm is proposed. A heuristic method for predicting the ‘float position’ corresponding to every Voronoi cell of the next epoch, making use of Doppler-based velocity information, is implemented to improve the computational efficiency. If the success rate is very close to 1, it is possible to guarantee reliable centimeter-level accuracy positioning without further ambiguity validation. Therefore, a computing method of the success rate for the kinematic ME-MAFA is proposed. Both the numerical simulations and the kinematic experiment demonstrate the feasibility of the new AR algorithm according to its accuracy and reliability. The accuracy of the horizontal positioning solution is better than 1.7 centimeters in our pseudolite system.

Real-time regional VTEC modeling based on B-splines using real-time GPS and GLONASS observations

2021 ◽  
Author(s):  
Eren Erdogan ◽  
Andreas Goss ◽  
Michael Schmidt ◽  
Denise Dettmering ◽  
Florian Seitz ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
Carrier Phase ◽  
Background Information ◽  
Electron Content ◽  
Vertical Total Electron Content ◽  
Total Electron ◽  
B Spline ◽  
B Splines ◽  
Cycle Slips

<p>The project OPTIMAP is at the current stage a joint initiative of BGIC, GSSAC and DGFI-TUM. The development of an operational tool for ionospheric mapping and prediction is the main goal of the project.</p><p>The ionosphere is a dispersive medium. Therefore, GNSS signals are refracted while they pass through the ionosphere. The magnitude of the refraction rate depends on the frequencies of the transmitted GNSS signals. The ionospheric disturbance on the GNSS signals paves the way of extracting Vertical Total Electron Content (VTEC) information of the ionosphere.</p><p>In OPTIMAP, the representation of the global and regional VTEC signal is based on localizing B-spline basis functions. For global VTEC modeling, polynomial B-splines are employed to represent the latitudinal variations, whereas trigonometric B-splines are used for the longitudinal variations. The regional modeling in OPTIMAP relies on a polynomial B-spline representation for both latitude and longitude.</p><p>The VTEC modeling in this study relies on both a global and a regional sequential estimator (Kalman filter) running in a parallel mode. The global VTEC estimator produces VTEC maps based on data from GNSS receiver stations which are mainly part of the global real-time IGS network. The global estimator relies on additional VTEC information obtained from a forecast procedure using ultra-rapid VTEC products. The regional estimator makes use of the VTEC product of the real-time global estimator as background information and generates high-resolution VTEC maps using real-time data from the EUREF Permanent GNSS Network. EUREF provides a network of very dense GNSS receivers distributed alongside Europe.</p><p>Carrier phase observations acquired from GPS and GLONASS, which are transmitted in accordance with RTCM standard, are used for real-time regional VTEC modeling. After the acquisition of GNSS data, cycle slips for each satellite-receiver pair are detected, and ionosphere observations are constructed via the linear combination of carrier-phase observations in the data pre-processing step. The unknown B-spline coefficients, as well as the biases for each phase-continuous arc belonging to each receiver-satellite pair, are simultaneously estimated in the Kalman filter.</p><p>Within this study, we compare the performance of regional and global VTEC products generated in real-time using the well-known dSTEC analysis.</p>

Real-time regional VTEC modeling based on B-splines using real-time GPS, GLONASS and GALILEO observations

2020 ◽  
Author(s):  
Eren Erdogan ◽  
Andreas Goss ◽  
Michael Schmidt ◽  
Denise Dettmering ◽  
Florian Seitz ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
Carrier Phase ◽  
Background Information ◽  
Electron Content ◽  
Vertical Total Electron Content ◽  
Total Electron ◽  
B Spline ◽  
B Splines ◽  
Cycle Slips

<p>The project OPTIMAP is at the current stage a joint initiative of BGIC, GSSAC and DGFI-TUM. The development of an operational tool for ionospheric mapping and prediction is the main goal of the project.</p><p>The ionosphere is a dispersive medium. Therefore, GNSS signals are refracted while they pass through the ionosphere. The magnitude of the refraction rate depends on the frequencies of the transmitted GNSS signals. The ionospheric disturbance on the GNSS signals paves the way of extracting Vertical Total Electron Content (VTEC) information of the ionosphere.</p><p>In OPTIMAP, the representation of the global and regional VTEC signal is based on localizing B-spline basis functions. For global VTEC modeling, polynomial B-splines are employed to represent the latitudinal variations, whereas trigonometric B-splines are used for the longitudinal variations. The regional modeling in OPTIMAP relies on a polynomial B-spline representation for both latitude and longitude.</p><p>The VTEC modeling in this study relies on both a global and a regional sequential estimator (Kalman filter) running in a parallel mode. The global VTEC estimator produces VTEC maps based on data from GNSS receiver stations which are mainly part of the global real-time IGS network. The global estimator relies on additional VTEC information obtained from a forecast procedure using ultra-rapid VTEC products. The regional estimator makes use of the VTEC product of the real-time global estimator as background information and generates high-resolution VTEC maps using real-time data from the EUREF Permanent GNSS Network. EUREF provides a network of very dense GNSS receivers distributed alongside Europe.</p><p>Carrier phase observations acquired from GPS, GLONASS and GALILEO constellations, which are transmitted in accordance with RTCM standard, are used for real-time regional VTEC modeling. After the acquisition of GNSS data, cycle slips for each satellite-receiver pair are detected, and ionosphere observations are constructed via the linear combination of carrier-phase observations in the data pre-processing step. The unknown B-spline coefficients, as well as the biases for each phase-continuous arc belonging to each receiver-satellite pair, are simultaneously estimated in the Kalman filter.</p><p>Within this study, we compare the performance of regional and global VTEC products generated in real-time using the well-known dSTEC analysis.</p>

A Review of Wind Speed Estimation for Wind Turbine Systems Based on Kalman Filter Technique

2016 ◽  
Vol 6 (4) ◽  
pp. 1406 ◽  
Author(s):  
M. Najafi Khoshrodi ◽  
Mohammad Jannati ◽  
Tole Sutikno
Keyword(s):  
Kalman Filter ◽  
Wind Speed ◽  
Wind Turbine ◽  
Single Point ◽  
Estimation Methods ◽  
Filter Method ◽  
Speed Estimation ◽  
Filter Technique ◽  
Kalman Filter Method ◽  
Wind Speed Measurement

This paper presents a review of wind speed estimation based on Kalman filter technique applied to wind turbine systems. Generally, wind speed measurement is performed by anemometer. The wind speed provided by the anemometer is measured at a single point of the rotor plane which is not the accurate wind speed. Also, using anemometer increases the system cost, maintenance, complexity and reduces the reliability. For these reasons, estimation of wind speed is needed for wind turbine systems. In this paper, the several wind speed estimation methods based on Kalman filter method used for wind turbine systems are reviewed.

scholarly journals A Review of Wind Speed Estimation for Wind Turbine Systems Based on Kalman Filter Technique

2016 ◽  
Vol 6 (4) ◽  
pp. 1406
Author(s):  
M. Najafi Khoshrodi ◽  
Mohammad Jannati ◽  
Tole Sutikno
Keyword(s):  
Kalman Filter ◽  
Wind Speed ◽  
Wind Turbine ◽  
Single Point ◽  
Estimation Methods ◽  
Filter Method ◽  
Speed Estimation ◽  
Filter Technique ◽  
Kalman Filter Method ◽  
Wind Speed Measurement

This paper presents a review of wind speed estimation based on Kalman filter technique applied to wind turbine systems. Generally, wind speed measurement is performed by anemometer. The wind speed provided by the anemometer is measured at a single point of the rotor plane which is not the accurate wind speed. Also, using anemometer increases the system cost, maintenance, complexity and reduces the reliability. For these reasons, estimation of wind speed is needed for wind turbine systems. In this paper, the several wind speed estimation methods based on Kalman filter method used for wind turbine systems are reviewed.

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