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Jingbin Liu

Bio: Jingbin Liu is an academic researcher. The author has contributed to research in topics: Computer science & Geology. The author has an hindex of 2, co-authored 9 publications receiving 16 citations.

Papers
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DOI
01 Jan 2022
TL;DR: In this article , the authors proposed a geometry-free cycle slip threshold model based on ionospheric disturbance index rate of total electron content index to reduce the false detection rate of cycle slip in GNSS precise point positioning during strong storm periods, thus improving the accuracy and reliability of GNSS PPP.
Abstract: Geomagnetic storm can affect the performance of Global Navigation Satellite System (GNSS) precise positioning services. To mitigate the adverse effects of strong geomagnetic storms, we propose to establish the geometry‐free (GF) cycle slip threshold model based on ionospheric disturbance index rate of total electron content index to reduce the false detection rate of cycle slip in GNSS precise point positioning (PPP) during strong storm periods, thus improving the accuracy and reliability of GNSS PPP. The performance of our proposed model is validated by using 171 International GNSS Service (IGS) tracking stations data on 8 September 2017. The analysis indicates that compared with conventional PPP scheme, the proposed model can improve the positioning accuracy by approximately 14.0% (36.8%) and 23.1% (51.5%) in the horizonal and vertical components for global (high latitudes) stations. Furthermore, the availability of our proposed model is also validated by PPP experiments using 379 IGS tracking stations data during another strong storm occurred on 26 August 2018.

5 citations

DOI
TL;DR: The current bottleneck limiting practical use of the vast volume of remote sensing data lies in the lack of affordable, reliable, and detailed field references, which are required for necessary calibrations of satellite and aerial data as mentioned in this paper .
Abstract: Remote sensing-based forest investigation and monitoring have become more affordable and applicable in the past few decades. The current bottleneck limiting practical use of the vast volume of remote sensing data lies in the lack of affordable, reliable, and detailed field references, which are required for necessary calibrations of satellite and aerial data and calibrations of relevant allometric models. Conventional field investigations are mostly limited to a small scale, using a small quantity of observations. Rapid development in close-range remote sensing has been witnessed during the past two decades, i.e., in the constant decrease of the costs, size, and weight of sensors; steady improvements in the availability, mobility, and reliability of platforms; and progress in computational capacity and data science. These advances have paved the way for turning conventional expensive and inefficient manual forest in situ data collections into affordable and efficient autonomous observations.

5 citations

Journal ArticleDOI
TL;DR: Wang et al. as mentioned in this paper developed a suite of enhanced 3D spatial analysis functions, including interactive route planning, instant text/image/video messaging, and progressive 3D construction and AR visualization using LiDAR and camera over local emergency network or internet.
Abstract: As with the fast advances in the technologies of big Earth data and information communication, Web-based 3D GIS system has come a long way from a few years ago. These advances reflect in many aspects of 3D GIS such as higher real-time performance, enhanced interactivity, more realistic 3D visualization effect and improved user interface. This paper aims to present a comprehensive and up-to-date 3D Web GIS for Emergency Response using the current vue.js web application framework and the well-known Cesium API, taking landslide disaster as an example. Building upon recent advances in WebGL technology, we developed a suite of enhanced 3D spatial analysis functions, including interactive route planning, instant text/image/video messaging being incorporated into both 3D WebGL page and mobile GIS applications, and progressive 3D construction and AR visualization using LiDAR and camera over local emergency network or internet. Moreover, professional functions such as landslide susceptibility mapping, landslide monitoring, spatial temporal contingency plan management, landslide information management, personnel and equipment management, and communication are all implemented and integrated in the 3D GIS system. Most of the functions of the system are implemented using open-source projects, which is beneficial to the development of the 3D GIS research community.

2 citations

DOI
24 Jun 2022
TL;DR: In this paper , the rate of total electron content index (ROTI) parameter was incorporated into the EAS model to mitigate severe storm effects on GNSS PPP, which improved the PPP accuracy in 3D direction by approximately 12.9% to 14.7%.
Abstract: For global navigation satellite system (GNSS), ionospheric disturbances caused by the geomagnetic storm can reduce the accuracy and reliability of precision point positioning (PPP). At present, common stochastic models in GNSS PPP, such as the elevation angle stochastic (EAS) model or carrier‐to‐noise power‐density ratio ( C/N0 $C/{N}_{\mathit{0}}$ ) based SIGMA‐ ε $\varepsilon $ model, do not properly consider storm effects on GNSS measurements. To mitigate severe storm effects on GNSS PPP, this study further implements the rate of total electron content index (ROTI) parameter into the EAS model referred to as the EAS‐ROTI model. This model contains two operations. The first one is to adjust variance of GNSS measurements using ROTI observations on EAS model. The second one is to determine the ratio of the priori variance factor between pseudorange and carrier phase measurements during severe storm conditions. The performance of EAS‐ROTI model is verified by using a large number of international GNSS service stations datasets on 17 March and 23 June in 2015. Experimental results indicate that on a global scale, the EAS‐ROTI model improves the PPP accuracy in 3D direction by approximately 12.9%–14.7% compared with the EAS model, and by about 24.8%–45.9% compared with the SIGMA‐ ε $\varepsilon $ model.

2 citations

Journal ArticleDOI
TL;DR: In this article , the authors proposed an enhanced indoor positioning solution using spatial context knowledge, extracted from the sparse dynamic fingerprints and stored in a spatial features database to reduce the computational time and storage space complexity.
Abstract: Radio fingerprinting positioning is widely used for smartphone location-based services and Internet of Things applications given its high availability and low cost. Radio fingerprinting-based algorithms, however, are subject to the forced matching problem and often yield estimated positions even when a user is actually located outside of the fingerprint region. A positioning solution in multistory buildings should be able to locate positions accurately on the current floor; but these methods may generate unreasonable positioning trajectories, such as irrationally passing through a wall, when fingerprinting positioning is fused the with inertial measurement unit to further improve the accuracy. A radio map must be surveyed dynamically on-the-move, as a dynamic fingerprint, to reduce the time costs and on-site workload. Unlike static fingerprint-based methods, dynamic fingerprinting samples are sparse. Thus, we propose an enhanced indoor positioning solution using spatial context knowledge, extracted from the sparse dynamic fingerprints. In the offline stage of radio map calculation, we extract the dynamic fingerprint features and store them in a spatial features database to reduce the computational time and storage space complexity. The proposed floor detection, region recognition, and path correction algorithms identify the online spatial contexts from the stored spatial features to improve positioning performance. This solution was applied on smartphones combining WiFi and Bluetooth low-energy radio signals in two typical scenarios. The experimental results show that the floor detection accuracy reached 99% while region recognition accuracy reached 90.75%. The positioning path correction method enhances the accuracy of smartphone indoor positioning from 3.27 to 2.56 m.

1 citations


Cited by
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Journal ArticleDOI
TL;DR: Different learning algorithms to approximate nonlinear space weather processes and forecast VTEC for 1 h and 24 h in the future for low-, mid- and high-latitude ionospheric grid points along the same longitude are investigated.
Abstract: Space weather describes varying conditions between the Sun and Earth that can degrade Global Navigation Satellite Systems (GNSS) operations. Thus, these effects should be precisely and timely corrected for accurate and reliable GNSS applications. That can be modeled with the Vertical Total Electron Content (VTEC) in the Earth’s ionosphere. This study investigates different learning algorithms to approximate nonlinear space weather processes and forecast VTEC for 1 h and 24 h in the future for low-, mid- and high-latitude ionospheric grid points along the same longitude. VTEC models are developed using learning algorithms of Decision Tree and ensemble learning of Random Forest, Adaptive Boosting (AdaBoost), and eXtreme Gradient Boosting (XGBoost). Furthermore, ensemble models are combined into a single meta-model Voting Regressor. Models were trained, optimized, and validated with the time series cross-validation technique. Moreover, the relative importance of input variables to the VTEC forecast is estimated. The results show that the developed models perform well in both quiet and storm conditions, where multi-tree ensemble learning outperforms the single Decision Tree. In particular, the meta-estimator Voting Regressor provides mostly the lowest RMSE and the highest correlation coefficients as it averages predictions from different well-performing models. Furthermore, expanding the input dataset with time derivatives, moving averages, and daily differences, as well as modifying data, such as differencing, enhances the learning of space weather features, especially over a longer forecast horizon.

14 citations

Journal ArticleDOI
TL;DR: This letter explores the distinctiveness of event streams from a small subset of pixels and demonstrates that the absolute difference in the number of events at those pixel locations accumulated into event frames can becient for the place recognition task, when pixels that display large variations in the reference set are used.
Abstract: Event cameras continue to attract interest due to desirable characteristics such as high dynamic range, low latency, virtually no motion blur, and high energy efficiency. One of the potential applications that would benefit from these characteristics lies in visual place recognition for robot localization, i.e. matching a query observation to the corresponding reference place in the database. In this letter, we explore the distinctiveness of event streams from a small subset of pixels (in the tens or hundreds). We demonstrate that the absolute difference in the number of events at those pixel locations accumulated into event frames can be sufficient for the place recognition task, when pixels that display large variations in the reference set are used. Using such sparse (over image coordinates) but varying (variance over the number of events per pixel location) pixels enables frequent and computationally cheap updates of the location estimates. Furthermore, when event frames contain a constant number of events, our method takes full advantage of the event-driven nature of the sensory stream and displays promising robustness to changes in velocity. We evaluate our proposed approach on the Brisbane-Event-VPR dataset in an outdoor driving scenario, as well as the newly contributed indoor QCR-Event-VPR dataset that was captured with a DAVIS346 camera mounted on a mobile robotic platform. Our results show that our approach achieves competitive performance when compared to several baseline methods on those datasets, and is particularly well suited for compute- and energy-constrained platforms such as interplanetary rovers.

3 citations

DOI
24 Jun 2022
TL;DR: In this paper , the rate of total electron content index (ROTI) parameter was incorporated into the EAS model to mitigate severe storm effects on GNSS PPP, which improved the PPP accuracy in 3D direction by approximately 12.9% to 14.7%.
Abstract: For global navigation satellite system (GNSS), ionospheric disturbances caused by the geomagnetic storm can reduce the accuracy and reliability of precision point positioning (PPP). At present, common stochastic models in GNSS PPP, such as the elevation angle stochastic (EAS) model or carrier‐to‐noise power‐density ratio ( C/N0 $C/{N}_{\mathit{0}}$ ) based SIGMA‐ ε $\varepsilon $ model, do not properly consider storm effects on GNSS measurements. To mitigate severe storm effects on GNSS PPP, this study further implements the rate of total electron content index (ROTI) parameter into the EAS model referred to as the EAS‐ROTI model. This model contains two operations. The first one is to adjust variance of GNSS measurements using ROTI observations on EAS model. The second one is to determine the ratio of the priori variance factor between pseudorange and carrier phase measurements during severe storm conditions. The performance of EAS‐ROTI model is verified by using a large number of international GNSS service stations datasets on 17 March and 23 June in 2015. Experimental results indicate that on a global scale, the EAS‐ROTI model improves the PPP accuracy in 3D direction by approximately 12.9%–14.7% compared with the EAS model, and by about 24.8%–45.9% compared with the SIGMA‐ ε $\varepsilon $ model.

2 citations

Journal ArticleDOI
TL;DR: An analysis and experiment of the design and evaluation of PPP-RTK through Q channel based on the existing P PP-B2b I channel signal suggested that the positioning accuracy of GPS+BDS+Galileo PPP -RTK under 95% quantile is roughly the same.
Abstract: Precise point positioning (PPP) is one of the seven planning public services of the new generation global Beidou Navigation Satellite System (BDS), i.e. BDS-3. Up to now, the PPP service signal has already been broadcast through I channel of PPP-B2b to support decimeter level positioning within 30 min for users over China and the surrounding areas for free. Concerning the potential application of PPP real-time kinematic (PPP-RTK) in the BDS positioning service development, this paper carried out analysis and experiment of the design and evaluation of PPP-RTK through Q channel based on the existing PPP-B2b I channel signal. First, we presented the algorithm of PPP-RTK based on the undifferenced and uncombined model. Then, the format and broadcast strategy of phase delay and atmospheric delay products were discussed in detail. Finally, based on the simulation data, we analyzed the performance of PPP-RTK with different broadcast bandwidths. The results suggested that the positioning accuracy of GPS + BDS + Galileo PPP-RTK under 95% quantile were 2.3 cm and 3.3 cm in horizontal and vertical, respectively. In order to evaluate the BDS PPP-RTK service, we set the convergence thresholds as 6 cm in horizontal and 12 cm in vertical, which is given by Centimeter Level Augmentation Service of Japanese Quasi-Zenith Satellite System, and the time to converge to such thresholds were 0.5 min and 1.0 min in horizontal and vertical, respectively. Finally, the experiment considering ‘correction latency’ suggested that there was only a litter effect on the convergence speed, the positioning accuracy after convergence is roughly the same.

2 citations

Journal ArticleDOI
02 Mar 2023-Forests
TL;DR: In this paper , a progressive plane detection filtering (PPDF) method was proposed for ground filtering in airborne light detection and ranging (LiDAR) point clouds for forestry applications.
Abstract: Ground filtering is necessary in processing airborne light detection and ranging (LiDAR) point clouds for forestry applications. This study proposes a progressive plane detection filtering (PPDF) method. First, the method uses multi-scale planes to characterize terrain, i.e., the local terrain with large slope variations is represented by small-scale planes, and vice versa. The planes are detected in local point clouds by the random sample consensus method with decreasing plane sizes. The reliability of the planes to represent local terrain is evaluated and the planes with optimal sizes are selected according to evaluation results. Then, ground seeds are identified by selecting the interior points of the planes. Finally, ground points are iteratively extracted based on the reference terrain, which is constructed using evenly distributed neighbor ground points. These neighbor points are identified by selecting the nearest neighbor points of multiple subspaces, which are divided from the local space with an unclassified point as center point. PPDF was tested in six sites with various terrain and vegetation characteristics. Results showed that PPDF was more accurate and robust compared to the classic filtering methods including maximum slope, progressive morphology, cloth simulation, and progressive triangulated irregular network densification filtering methods, with the smallest average total error and standard deviation of 3.42% and 2.45% across all sites. Moreover, the sensitivity of PPDF to parameters was low and these parameters can be set as fixed values. Therefore, PPDF is effective and easy-to-use for filtering airborne LiDAR data.

1 citations