scholarly journals Precise and Robust RTK-GNSS Positioning in Urban Environments with Dual-Antenna Configuration

Sensors ◽  
2019 ◽  
Vol 19 (16) ◽  
pp. 3586 ◽  
Author(s):  
Fan ◽  
Li ◽  
Cui ◽  
Lu

Robust and centimeter-level Real-time Kinematic (RTK)-based Global Navigation Satellite System (GNSS) positioning is of paramount importance for emerging GNSS applications, such as drones and automobile systems. However, the performance of conventional single-rover RTK degrades greatly in urban environments due to signal blockage and strong multipath. The increasing use of multiple-antenna/rover configurations for attitude determination in the above precise positioning applications, just as well, allows more information involved to improve RTK positioning performance in urban areas. This paper proposes a dual-antenna constraint RTK algorithm, which combines GNSS measurements of both antennas by making use of the geometric constraint between them. By doing this, the reception diversity between two antennas can be taken advantage of to improve the availability and geometric distribution of GNSS satellites, and what is more, the redundant measurements from a second antenna help to weaken the multipath effect on the first antenna. Particularly, an Ambiguity Dilution of Precision (ADOP)-based analysis is carried out to explore the intrinsic model strength for ambiguity resolution (AR) with different kinds of constraints. Based on the results, a Dual-Antenna with baseline VEctor Constraint algorithm (RTK) is developed. The primary advantages of the reported method include: 1) Improved availability and success rate of RTK, even if neither of the two single-antenna receivers can successfully solve the AR problem; and 2) reduced computational burden by adopting the concept of measurement projection. Simulated and real data experiments are performed to demonstrate robustness and precision of the algorithm in GNSS-challenged environments.

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Sakpod Tongleamnak ◽  
Masahiko Nagai

Performance of Global Navigation Satellite System (GNSS) positioning in urban environments is hindered by poor satellite availability because there are many man-made and natural objects in urban environments that obstruct satellite signals. To evaluate the availability of GNSS in cities, this paper presents a software simulation of GNSS availability in urban areas using a panoramic image dataset from Google Street View. Photogrammetric image processing techniques are applied to reconstruct fisheye sky view images and detect signal obstacles. Two comparisons of the results from the simulation and real world observation in Bangkok and Tokyo are also presented and discussed for accuracy assessment.


2018 ◽  
Vol 2018 ◽  
pp. 1-12
Author(s):  
Nathan A. Tehrani ◽  
Jason N. Gross

We present various performance trades for multiantenna global navigation satellite system (GNSS) multisensor attitude estimation systems. In particular, attitude estimation performance sensitivity to various error sources and system configurations is assessed. This study is motivated by the need for system designers, scientists, and engineers of airborne astronomical and remote sensing platforms to better determine which system configuration is most suitable for their specific application. In order to assess performance trade-offs, the attitude estimation performance of various approaches is tested using a simulation that is based on a stratospheric balloon platform. For GNSS errors, attention is focused on multipath, receiver measurement noise, and carrier-phase breaks. For the remaining attitude sensors, different performance grades of sensors are assessed. Through a Monte Carlo simulation, it is shown that, under typical conditions, sub-0.1-degree attitude accuracy is available when using multiple antenna GNSS data only, but that this accuracy can degrade to degree level in some environments warranting the inclusion of additional attitude sensors to maintain the desired level of accuracy. Further, we show that integrating inertial sensors is more valuable whenever accurate pitch and roll estimates are critical.


2020 ◽  
Vol 12 (2) ◽  
pp. 300
Author(s):  
Alessandro Parizzi ◽  
Fernando Rodriguez Gonzalez ◽  
Ramon Brcic

This paper deals with the integration of deformation rates derived from Synthetic Aperture Radar Interferometry (InSAR) and Global Navigation Satellite System (GNSS) data. The proposed approach relies on knowledge of the variance/covariance of both InSAR and GNSS measurements so that they may be combined accounting for the spectral properties of their errors, hence preserving all spatial frequencies of the deformation detected by the two techniques. The variance/covariance description of the output product is also provided. A performance analysis is carried out on realistic simulated scenarios in order to show the boundaries of the technique. The proposed approach is finally applied to real data. Five Sentinel-1A/B stacks acquired over two different areas of interest are processed and discussed. The first example is a merged deformation map of the northern part of the Netherlands for both ascending and descending geometries. The second example shows the deformation at the junction between the North and East Anatolian Fault using three consecutive descending stacks.


2020 ◽  
Vol 73 (6) ◽  
pp. 1202-1222 ◽  
Author(s):  
Hoi-Fung Ng ◽  
Guohao Zhang ◽  
Li-Ta Hsu

Global navigation satellite system (GNSS) positioning in dense urban areas remains a challenge due to the signal reflection by buildings, namely multipath and non-line-of-sight (NLOS) reception. These effects degrade the performance of low-cost GNSS receivers such as in those smartphones. An effective three-dimensional (3D) mapping aided GNSS positioning method is proposed to correct the NLOS error. Instead of applying ray-tracing simulation, the signal reflection points are detected based on a skyplot with the surrounding building boundaries. The measurements of the direct and reflected signals can thus be simulated and further used to determine the user's position based on the measurement likelihood between real measurements. Verified with real experiments, the proposed algorithm is able to reduce the computational load greatly while maintaining a positioning accuracy within 10 metres of error in dense urban environments, compared with the conventional method of ray-tracing based NLOS corrected positioning.


Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4728
Author(s):  
Max Jwo Lem Lee ◽  
Shang Lee ◽  
Hoi-Fung Ng ◽  
Li-Ta Hsu

3D-mapping-aided (3DMA) global navigation satellite system (GNSS) positioning that improves positioning performance in dense urban areas has been under development in recent years, but it still faces many challenges. This paper details a new algorithm that explores the potential of using building boundaries for positioning and heading estimation. Rather than applying complex simulations to analyze and correct signal reflections by buildings, the approach utilizes a convolutional neural network to differentiate between the sky and building in a sky-pointing fisheye image. A new skymask matching algorithm is then proposed to match the segmented fisheye images with skymasks generated from a 3D building model. Each matched skymask holds a latitude, longitude coordinate and heading angle to determine the precise location of the fisheye image. The results are then compared with the smartphone GNSS and advanced 3DMA GNSS positioning methods. The proposed method provides degree-level heading accuracy, and improved positioning accuracy similar to other advanced 3DMA GNSS positioning methods in a rich urban environment.


Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4302 ◽  
Author(s):  
Paolo Dabove ◽  
Vincenzo Di Pietra

Global Navigation Satellite System (GNSS) positioning is currently a common practice thanks to the development of mobile devices such as smartphones and tablets. The possibility to obtain raw GNSS measurements, such as pseudoranges and carrier-phase, from these instruments has opened new windows towards precise positioning using smart devices. This work aims to demonstrate the positioning performances in the case of a typical single-base Real-Time Kinematic (RTK) positioning while considering two different kinds of multi-frequency and multi-constellation master stations: a typical geodetic receiver and a smartphone device. The results have shown impressive performances in terms of precision in both cases: with a geodetic receiver as the master station, the reachable precisions are several mm for all 3D components while if a smartphone is used as the master station, the best results can be obtained considering the GPS+Galileo constellations, with a precision of about 2 cm both for 2D and Up components in the case of L1+L5 frequencies, or 3 cm for 2D components and 2 cm for the Up, in the case of an L1 frequency. Moreover, it has been demonstrated that it is not feasible to reach the phase ambiguities fixing: despite this, the precisions are still good and also the obtained 3D accuracies of positioning solutions are less than 1 m. So, it is possible to affirm that these results are very promising in the direction of cooperative positioning using smartphone devices.


2014 ◽  
Vol 68 (3) ◽  
pp. 411-433 ◽  
Author(s):  
Lei Wang ◽  
Paul D Groves ◽  
Marek K Ziebart

Global Navigation Satellite System (GNSS) shadow matching is a new positioning technique that determines position by comparing the measured signal availability and strength with predictions made using a three-dimensional (3D) city model. It complements conventional GNSS positioning and can significantly improve cross-street positioning accuracy in dense urban environments. This paper describes how shadow matching has been adapted to work on an Android smartphone and presents the first comprehensive performance assessment of smartphone GNSS shadow matching. Using GPS and GLONASS data recorded at 20 locations within central London, it is shown that shadow matching significantly outperforms conventional GNSS positioning in the cross-street direction. The success rate for obtaining a cross-street position accuracy within 5 m, enabling the correct side of a street to be determined, was 54·50% using shadow matching, compared to 24·77% for the conventional GNSS position. The likely performance of four-constellation shadow matching is predicted, the feasibility of a large-scale implementation of shadow matching is assessed, and some methods for improving performance are proposed. A further contribution is a signal-to-noise ratio analysis of the direct line-of-sight and non-line-of-sight signals received on a smartphone in a dense urban environment.


2021 ◽  
Vol 13 (12) ◽  
pp. 6981
Author(s):  
Marcela Bindzarova Gergelova ◽  
Slavomir Labant ◽  
Jozef Mizak ◽  
Pavel Sustek ◽  
Lubomir Leicher

The concept of further sustainable development in the area of administration of the register of old mining works and recent mining works in Slovakia requires precise determination of the locations of the objects that constitute it. The objects in this register have their uniqueness linked with the history of mining in Slovakia. The state of positional accuracy in the registration of objects in its current form is unsatisfactory. Different database sources containing the locations of the old mining works are insufficient and show significant locational deviations. For this reason, it is necessary to precisely locate old mining works using modern measuring technologies. The most effective approach to solving this problem is the use of LiDAR data, which at the same time allow determining the position and above-ground shape of old mining works. Two localities with significant mining history were selected for this case study. Positional deviations in the location of old mining works among the selected data were determined from the register of old mining works in Slovakia, global navigation satellite system (GNSS) measurements, multidirectional hill-shading using LiDAR, and accessible data from the open street map. To compare the positions of identical old mining works from the selected database sources, we established differences in the coordinates (ΔX, ΔY) and calculated the positional deviations of the same objects. The average positional deviation in the total count of nineteen objects comparing documents, LiDAR data, and the register was 33.6 m. Comparing the locations of twelve old mining works between the LiDAR data and the open street map, the average positional deviation was 16.3 m. Between the data sources from GNSS and the registry of old mining works, the average positional deviation of four selected objects was 39.17 m.


Water ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 812
Author(s):  
Sotiris Lycourghiotis

The mean sea surface topography of the Ionian and Adriatic Seas has been determined. This was based on six-months of Global Navigation Satellite System (GNSS) measurements which were performed on the Ionian Queen (a ship). The measurements were analyzed following a double-path methodology based on differential GNSS (D-GNSS) and precise point positioning (PPP) analysis. Numerical filtering techniques, multi-parametric accuracy analysis and a new technique for removing the meteorological tide factors were also used. Results were compared with the EGM96 geoid model. The calculated differences ranged between 0 and 48 cm. The error of the results was estimated to fall within 3.31 cm. The 3D image of the marine topography in the region shows a nearly constant slope of 4 cm/km in the N–S direction. Thus, the effectiveness of the approach “repeated GNSS measurements on the same route of a ship” developed in the context of “GNSS methods on floating means” has been demonstrated. The application of this approach using systematic multi-track recordings on conventional liner ships is very promising, as it may open possibilities for widespread use of the methodology across the world.


2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Fahad Alhomayani ◽  
Mohammad H. Mahoor

AbstractIn recent years, fingerprint-based positioning has gained researchers’ attention since it is a promising alternative to the Global Navigation Satellite System and cellular network-based localization in urban areas. Despite this, the lack of publicly available datasets that researchers can use to develop, evaluate, and compare fingerprint-based positioning solutions constitutes a high entry barrier for studies. As an effort to overcome this barrier and foster new research efforts, this paper presents OutFin, a novel dataset of outdoor location fingerprints that were collected using two different smartphones. OutFin is comprised of diverse data types such as WiFi, Bluetooth, and cellular signal strengths, in addition to measurements from various sensors including the magnetometer, accelerometer, gyroscope, barometer, and ambient light sensor. The collection area spanned four dispersed sites with a total of 122 reference points. Each site is different in terms of its visibility to the Global Navigation Satellite System and reference points’ number, arrangement, and spacing. Before OutFin was made available to the public, several experiments were conducted to validate its technical quality.


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