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Dr. Li-Ta Hsu
Interdisciplinary Division of Aeronautical and Aviation Engineering/ The Hong Kong Polytechnic University, Hung Hom, Kowloon 999077, Hong Kong

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Research Keywords & Expertise

0 Multipath
0 Navigation
0 Sensor Fusion
0 GNSS
0 autonomous systems

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GNSS
Navigation
Multipath
NLOS
Sensor Fusion
autonomous systems

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Short Biography

Dr. Li-Ta Hsu, born in Taiwan, is an assistant professor at Hong Kong Polytechnic University where he directs the Intelligent Positioning and Navigation Lab focused on the navigation for pedestrian and autonomous driving in urban canyons. He is currently a Technical Representative serving on the ION Council and an Associate Fellow in the RIN.

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Journal article
Published: 30 August 2021 in Advanced Engineering Informatics
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As the backbone of Communications, Navigation and Surveillance Systems for Air Traffic Management (CNS/ATM), Automatic Dependent Surveillance-Broadcast (ADS-B) is a surveillance technology and digital-technology enabler relying on the Global Navigation Satellite System (GNSS). The onboard ADS-B Out system broadcasts the aircraft’s real-time digital information such as position and ground speed periodically (every 0.5–2 s), which is more frequent than the radar system. Taking this advantage, situational awareness and flight efficiency can be highly improved. In this paper, a novel heuristic search method based on ADS-B is proposed for the Aircraft Landing Problem (ALP) with the objective of reducing flight time while maintaining the time separation standards mandated by the International Civil Aviation Organization (ICAO). The recorded ADS-B data in Shanghai Hongqiao and Pudong international airports are adopted to demonstrate the performance of the proposed method. Results show that there is an obvious decrease in the total flight time. Besides, the heuristic search method can achieve continuous and real-time ALP updates, satisfying the requirements for air traffic control. While highlighting ADS-B-based applications, this study also provides some basic implications for the updated model in air traffic management.

ACS Style

Dabin Xue; Li-Ta Hsu; Cheng-Lung Wu; Ching-Hung Lee; Kam K.H. Ng. Cooperative surveillance systems and digital-technology enabler for a real-time standard terminal arrival schedule displacement. Advanced Engineering Informatics 2021, 50, 101402 .

AMA Style

Dabin Xue, Li-Ta Hsu, Cheng-Lung Wu, Ching-Hung Lee, Kam K.H. Ng. Cooperative surveillance systems and digital-technology enabler for a real-time standard terminal arrival schedule displacement. Advanced Engineering Informatics. 2021; 50 ():101402.

Chicago/Turabian Style

Dabin Xue; Li-Ta Hsu; Cheng-Lung Wu; Ching-Hung Lee; Kam K.H. Ng. 2021. "Cooperative surveillance systems and digital-technology enabler for a real-time standard terminal arrival schedule displacement." Advanced Engineering Informatics 50, no. : 101402.

Journal article
Published: 17 June 2021 in Remote Sensing
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Accurate positioning and mapping are significant for autonomous systems with navigation requirements. In this paper, a coarse-to-fine loosely-coupled (LC) LiDAR-inertial odometry (LC-LIO) that could explore the complementariness of LiDAR and inertial measurement unit (IMU) was proposed for the real-time and accurate pose estimation of a ground vehicle in urban environments. Different from the existing tightly-coupled (TC) LiDAR-inertial fusion schemes which directly use all the considered ranges and inertial measurements to optimize the vehicle pose, the method proposed in this paper performs loosely-couped integrated optimization with the high-frequency motion prediction, which was produced by IMU integration based on optimized results, employed as the initial guess of LiDAR odometry to approach the optimality of LiDAR scan-to-map registration. As one of the prominent contributions, thorough studies were conducted on the performance upper bound of the TC LiDAR-inertial fusion schemes and LC ones, respectively. Furthermore, the experimental verification was performed on the proposition that the proposed pipeline can fully relax the potential of the LiDAR measurements (centimeter-level ranging accuracy) in a coarse-to-fine way without being disturbed by the unexpected IMU bias. Moreover, an adaptive covariance estimation method employed during LC optimization was proposed to explain the uncertainty of LiDAR scan-to-map registration in dynamic scenarios. Furthermore, the effectiveness of the proposed system was validated on challenging real-world datasets. Meanwhile, the process that the proposed pipelines realized the coarse-to-fine LiDAR scan-to-map registration was presented in detail. Comparing with the existing state-of-the-art TC-LIO, the focus of this study would be placed on that the proposed LC-LIO work could achieve similar or better accuracy with a reduced computational expense.

ACS Style

Jiachen Zhang; Weisong Wen; Feng Huang; Xiaodong Chen; Li-Ta Hsu. Coarse-to-Fine Loosely-Coupled LiDAR-Inertial Odometry for Urban Positioning and Mapping. Remote Sensing 2021, 13, 2371 .

AMA Style

Jiachen Zhang, Weisong Wen, Feng Huang, Xiaodong Chen, Li-Ta Hsu. Coarse-to-Fine Loosely-Coupled LiDAR-Inertial Odometry for Urban Positioning and Mapping. Remote Sensing. 2021; 13 (12):2371.

Chicago/Turabian Style

Jiachen Zhang; Weisong Wen; Feng Huang; Xiaodong Chen; Li-Ta Hsu. 2021. "Coarse-to-Fine Loosely-Coupled LiDAR-Inertial Odometry for Urban Positioning and Mapping." Remote Sensing 13, no. 12: 2371.

Original article
Published: 22 May 2021 in NAVIGATION
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Factor graph optimization (FGO) recently has attracted attention as an alternative to the extended Kalman filter (EKF) for GNSS-INS integration. This study evaluates both loosely and tightly coupled integrations of GNSS code pseudorange and INS measurements for real-time positioning, using both conventional EKF and FGO with a dataset collected in an urban canyon in Hong Kong. The FGO strength is analyzed by degenerating the FGO-based estimator into an “EKF-like estimator.” In addition, the effects of window size on FGO performance are evaluated by considering both the GNSS pseudorange error models and environmental conditions. We conclude that the conventional FGO outperforms the EKF because of the following two factors: (1) FGO uses multiple iterations during the estimation to achieve a robust estimation; and (2) FGO better explores the time correlation between the measurements and states, based on a batch of historical data, when the measurements do not follow the Gaussian noise assumption.

ACS Style

Weisong Wen; Tim Pfeifer; Xiwei Bai; Li‐Ta Hsu. Factor graph optimization for GNSS/INS integration: A comparison with the extended Kalman filter. NAVIGATION 2021, 68, 315 -331.

AMA Style

Weisong Wen, Tim Pfeifer, Xiwei Bai, Li‐Ta Hsu. Factor graph optimization for GNSS/INS integration: A comparison with the extended Kalman filter. NAVIGATION. 2021; 68 (2):315-331.

Chicago/Turabian Style

Weisong Wen; Tim Pfeifer; Xiwei Bai; Li‐Ta Hsu. 2021. "Factor graph optimization for GNSS/INS integration: A comparison with the extended Kalman filter." NAVIGATION 68, no. 2: 315-331.

Journal article
Published: 29 March 2021 in IEEE Transactions on Vehicular Technology
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LiDAR odometry algorithms are extensively studied for vehicular positioning. However, achieving high-precision positioning using low-cost 16-channel LiDAR in urban canyons remains a challenging problem due to the limited point cloud density from low-cost LiDAR and excessive dynamic surrounding objects. To fill this gap, this paper proposes enriching sparse 3D point clouds to denser clouds via a novel deep learning-based superresolution (SR) algorithm before its utilization in 3D LiDAR odometry. We validate the effectiveness of the proposed method using the KITTI dataset and a challenging dataset collected in an urban canyon (with complex environmental structures and dynamic objects) of Hong Kong. We conclude that significantly denser point clouds are achieved with considerable accuracy. In addition, significantly improved performance of 3D LiDAR odometry is obtained in the evaluated dataset collected in an urban canyon of Hong Kong.

ACS Style

Jiang Yue; Weisong Wen; Jin Han; Li-Ta Hsu. 3D Point Clouds Data Super Resolution-Aided LiDAR Odometry for Vehicular Positioning in Urban Canyons. IEEE Transactions on Vehicular Technology 2021, 70, 4098 -4112.

AMA Style

Jiang Yue, Weisong Wen, Jin Han, Li-Ta Hsu. 3D Point Clouds Data Super Resolution-Aided LiDAR Odometry for Vehicular Positioning in Urban Canyons. IEEE Transactions on Vehicular Technology. 2021; 70 (5):4098-4112.

Chicago/Turabian Style

Jiang Yue; Weisong Wen; Jin Han; Li-Ta Hsu. 2021. "3D Point Clouds Data Super Resolution-Aided LiDAR Odometry for Vehicular Positioning in Urban Canyons." IEEE Transactions on Vehicular Technology 70, no. 5: 4098-4112.

Original article
Published: 27 March 2021 in NAVIGATION
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The GNSS performance is significantly degraded in urban canyons because of the signal interferences caused by buildings. Besides the multipath and non‐line‐of‐sight (NLOS) receptions, the diffraction effect frequently occurs in urban canyons, which will severely attenuate the signal strength when the satellite line‐of‐sight (LOS) transmitting path is close to the building edge. It is essential to evaluate the performance of current diffraction models for GNSS before applying mitigation. The detailed steps of applying the knife‐edge model and the uniform geometrical theory of diffraction (UTD) model on GNSS are given, including the and pseudorange simulation of the diffracted signal. The performances of both models are assessed using real data from two typical urban scenarios. The result shows the UTD can adequately model the GNSS diffraction effect even in a complicated urban area. Compared with the knife‐edge model, the UTD achieves better modeling accuracy, whereas requiring higher computational loads.

ACS Style

Guohao Zhang; Li‐Ta Hsu. Performance assessment of GNSS diffraction models in urban areas. NAVIGATION 2021, 68, 369 -389.

AMA Style

Guohao Zhang, Li‐Ta Hsu. Performance assessment of GNSS diffraction models in urban areas. NAVIGATION. 2021; 68 (2):369-389.

Chicago/Turabian Style

Guohao Zhang; Li‐Ta Hsu. 2021. "Performance assessment of GNSS diffraction models in urban areas." NAVIGATION 68, no. 2: 369-389.

Journal article
Published: 03 February 2021 in Remote Sensing
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Accurate localization of road agents (GNSS receivers) is the basis of intelligent transportation systems, which is still difficult to achieve for GNSS positioning in urban areas due to the signal interferences from buildings. Various collaborative positioning techniques were recently developed to improve the positioning performance by the aid from neighboring agents. However, it is still challenging to study their performances comprehensively. The GNSS measurement error behavior is complicated in urban areas and unable to be represented by naive models. On the other hand, real experiments requiring numbers of devices are difficult to conduct, especially for a large-scale test. Therefore, a GNSS realistic urban measurement simulator is developed to provide measurements for collaborative positioning studies. The proposed simulator employs a ray-tracing technique searching for all possible interferences in the urban area. Then, it categorizes them into direct, reflected, diffracted, and multipath signal to simulate the pseudorange, , and Doppler shift measurements correspondingly. The performance of the proposed simulator is validated through real experimental comparisons with different scenarios based on commercial-grade receivers. The proposed simulator is also applied with different positioning algorithms, which verifies it is sophisticated enough for the collaborative positioning studies in the urban area.

ACS Style

Guohao Zhang; Bing Xu; Hoi-Fung Ng; Li-Ta Hsu. GNSS RUMS: GNSS Realistic Urban Multiagent Simulator for Collaborative Positioning Research. Remote Sensing 2021, 13, 544 .

AMA Style

Guohao Zhang, Bing Xu, Hoi-Fung Ng, Li-Ta Hsu. GNSS RUMS: GNSS Realistic Urban Multiagent Simulator for Collaborative Positioning Research. Remote Sensing. 2021; 13 (4):544.

Chicago/Turabian Style

Guohao Zhang; Bing Xu; Hoi-Fung Ng; Li-Ta Hsu. 2021. "GNSS RUMS: GNSS Realistic Urban Multiagent Simulator for Collaborative Positioning Research." Remote Sensing 13, no. 4: 544.

Journal article
Published: 19 January 2021 in IEEE Access
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The global navigation satellite system (GNSS) is widely used in smartphone positioning, but its performance can be degraded in urban environments because of signal reflections or blockages. To address these GNSS outages, pedestrian dead reckoning (PDR) is commonly used due to its significant improvements in both the stability and continuity of positioning, which are dependent on three key factors: continuous absolute position, heading and step information. Signals of opportunity are commonly used in positioning, whereas the installation of Bluetooth low energy (BLE) sensors on lampposts can provide an opportunity for positioning and heading estimation in urban canyons. In this article, a system integrating the GNSS, PDR, and BLE techniques is implemented in smartphones to provide a real-time positioning solution for pedestrians, which includes a new position correction method based on BLE heading, a reliable heading estimation integrating BLE and inertial sensors, an unconstrained step detection method with high accuracy, and an extended Kalman filter (EKF) to integrate multiple sensors and techniques. In several field experiments, with improvements in availability and robustness, the heading accuracy of the proposed fusion approach could reach approximately 3 degrees; the positioning accuracy achieved between 2.7 m and 4.2 m, compared with a 30 m error from the GNSS alone. Simultaneously, this system could achieve a high positioning accuracy of 2.4 m with unconstrained smartphones in a mixed environment. The proposed system has been demonstrated to perform well in urban canyons.

ACS Style

Huan Luo; Yaxin Li; Jingxian Wang; Duojie Weng; Junhua Ye; Li-Ta Hsu; Wu Chen. Integration of GNSS and BLE Technology With Inertial Sensors for Real-Time Positioning in Urban Environments. IEEE Access 2021, 9, 15744 -15763.

AMA Style

Huan Luo, Yaxin Li, Jingxian Wang, Duojie Weng, Junhua Ye, Li-Ta Hsu, Wu Chen. Integration of GNSS and BLE Technology With Inertial Sensors for Real-Time Positioning in Urban Environments. IEEE Access. 2021; 9 ():15744-15763.

Chicago/Turabian Style

Huan Luo; Yaxin Li; Jingxian Wang; Duojie Weng; Junhua Ye; Li-Ta Hsu; Wu Chen. 2021. "Integration of GNSS and BLE Technology With Inertial Sensors for Real-Time Positioning in Urban Environments." IEEE Access 9, no. : 15744-15763.

Original article
Published: 12 January 2021 in NAVIGATION
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This paper builds on the machine learning research to propose two new algorithms based on optimizing the Adaptive Neuro Fuzzy Inference System (ANFIS) with a dual‐polarization antenna to predict pseudorange errors by considering multiple variables including the right‐hand circular polarized (RHCP) signal strength, signal strength difference between the left‐hand circular polarized (LHCP) and RHCP outputs, satellites’ elevation angle, and pseudorange residuals. The final antenna position is calculated following the application of the predicted pseudorange errors to correct for the effects of non‐line‐of‐sight (NLOS) and multipath signal reception. The results show that the proposed algorithm results in a 30% improvement in the root mean square error (RMSE) in the 2D (horizontal) component for static applications when the training and testing data are collected at the same location. This corresponds to 13% to 20% when the testing data is from locations away from that of the training dataset.

ACS Style

Rui Sun; Linxia Fu; Guanyu Wang; Qi Cheng; Li‐Ta Hsu; Washington Yotto Ochieng. Using dual‐polarization GPS antenna with optimized adaptive neuro‐fuzzy inference system to improve single point positioning accuracy in urban canyons. NAVIGATION 2021, 68, 41 -60.

AMA Style

Rui Sun, Linxia Fu, Guanyu Wang, Qi Cheng, Li‐Ta Hsu, Washington Yotto Ochieng. Using dual‐polarization GPS antenna with optimized adaptive neuro‐fuzzy inference system to improve single point positioning accuracy in urban canyons. NAVIGATION. 2021; 68 (1):41-60.

Chicago/Turabian Style

Rui Sun; Linxia Fu; Guanyu Wang; Qi Cheng; Li‐Ta Hsu; Washington Yotto Ochieng. 2021. "Using dual‐polarization GPS antenna with optimized adaptive neuro‐fuzzy inference system to improve single point positioning accuracy in urban canyons." NAVIGATION 68, no. 1: 41-60.

Research article
Published: 28 December 2020 in Journal of Navigation
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Array antenna beam forming has high potential to improve the performance of the global navigation satellite system (GNSS) in urban areas. However, the widespread application of array antennas for GNSS multipath mitigation is restricted by many factors, such as the complexity of the system, the computation load and conflicts between required performance, cost budget and limited room for the antenna placement. The scope of this work is triplicate. (1) The pre-correlation beam forming structure is first suggested for multipath mitigation to decrease the system complexity. (2) With the pre-correlation structure, the equivalence of adaptive beam forming to quiescent beam forming is revealed. Therefore, the computational load for beam forming is greatly decreased. (3) A theoretical model is established to link the benefits of beam forming with GNSS performance improvement in terms of pseudorange quality. The model can be used by industry to balance the aforementioned restrictions. Numerical results with different array settings are given, and a 2 × 2 rectangle array with $0.4\lambda $ element spacing is suggested as a cost-effective choice in GNSS positioning applications in urban canyon areas.

ACS Style

Qiongqiong Jia; Li-Ta Hsu; Bing Xu; Renbiao Wu. A cost-effective beam forming structure for global navigation satellite system multipath mitigation and its assessment. Journal of Navigation 2020, 74, 425 -445.

AMA Style

Qiongqiong Jia, Li-Ta Hsu, Bing Xu, Renbiao Wu. A cost-effective beam forming structure for global navigation satellite system multipath mitigation and its assessment. Journal of Navigation. 2020; 74 (2):425-445.

Chicago/Turabian Style

Qiongqiong Jia; Li-Ta Hsu; Bing Xu; Renbiao Wu. 2020. "A cost-effective beam forming structure for global navigation satellite system multipath mitigation and its assessment." Journal of Navigation 74, no. 2: 425-445.

Preprint
Published: 25 December 2020
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Accurate localization of road agents is the basis of intelligent transportation systems, which is still difficult to achieve for GNSS positioning in urban areas due to the signal interferences from buildings. Various collaborative positioning techniques are recently developed to improve the positioning performance by the aid from neighboring agents. However, it is still challenging to study their performances comprehensively. The GNSS measurement error behavior is complicated in urban areas and unable to be represented by naive models. On the other hand, real experiment requiring numbers of devices is hard to be conducted, especially for a large-scale test. Therefore, a GNSS realistic urban measurement simulator is developed to provide measurements for collaborative positioning studies. The proposed simulator employs a ray-tracing technique searching for all possible interferences in the urban area. Then, it categorizes them into direct, reflected, diffracted, and multipath signal to simulate the pseudorange, carrier-phase, 〖C/N〗_0, and Doppler shift measurements correspondingly. The performance of the proposed simulator is validated through real experimental comparisons with different scenarios. The proposed simulator is also applied with different positioning algorithms, which verifies it is sophisticated enough for the collaborative positioning studies in the urban area.

ACS Style

Guohao Zhang; Bing Xu; Hoi-Fung Ng; Li-Ta Hsu. GNSS RUMS: GNSS Realistic Urban Multi-agent Simulator for Collaborative Positioning Research. 2020, 1 .

AMA Style

Guohao Zhang, Bing Xu, Hoi-Fung Ng, Li-Ta Hsu. GNSS RUMS: GNSS Realistic Urban Multi-agent Simulator for Collaborative Positioning Research. . 2020; ():1.

Chicago/Turabian Style

Guohao Zhang; Bing Xu; Hoi-Fung Ng; Li-Ta Hsu. 2020. "GNSS RUMS: GNSS Realistic Urban Multi-agent Simulator for Collaborative Positioning Research." , no. : 1.

Journal article
Published: 30 November 2020 in Aerospace Science and Technology
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Due to the widely use of the rotorcrafts in civil applications, the highly accurate positioning is paid more attentions. The ultra-wide band (UWB) receiver plays a crucial role in navigating the unmanned aerial vehicle (UAV) in indoor areas, because of its low cost and low power consumption. However, the positioning accuracy of UWB is drastically affected by the infamous multipath effect. Therefore, the Ultra Wideband (UWB)/Inertial Navigation System (INS) integrated indoor navigation is an effective approach to reduces the positioning errors. This paper proposes a tightly-coupled UWB/INS integration navigation based on factor graph optimization (FGO). For the loosely-coupled integration, the linear and nonlinear least square methods are employed to obtain the well-performed single point positioning. The Allan-variance analysis is used to estimate the process noise covariance of INS. Besides, to reduce the computational load of nonlinear optimization in factor graph, this paper employs the IMU preintegration factor. The location performance of the proposed FGO method is compared with an extended Kalman Filter (EKF). The results show that the proposed tightly-coupled UWB/INS integration method can realize the better positioning performance than that of the conventional EKF in Line-of-Sight (LOS) and Non-Line-of-Sight (NLOS) combined indoor environments.

ACS Style

Yang Song; Li-Ta Hsu. Tightly coupled integrated navigation system via factor graph for UAV indoor localization. Aerospace Science and Technology 2020, 108, 106370 .

AMA Style

Yang Song, Li-Ta Hsu. Tightly coupled integrated navigation system via factor graph for UAV indoor localization. Aerospace Science and Technology. 2020; 108 ():106370.

Chicago/Turabian Style

Yang Song; Li-Ta Hsu. 2020. "Tightly coupled integrated navigation system via factor graph for UAV indoor localization." Aerospace Science and Technology 108, no. : 106370.

Journal article
Published: 10 November 2020 in IEEE Internet of Things Journal
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The accuracy of location information, mainly provided by the Global Positioning System (GPS) sensor, is critical for internet of things applications in smart cities. However, built environments attenuate GPS signals by reflecting or blocking them resulting in some cases multipath and non-line-of-sight (NLOS) reception. These effects cause range errors that degrade GPS positioning accuracy. Enhancements in the design of antennae and receivers deliver a level of reduction of multipath. However, NLOS signal reception and residual effects of multipath are still to be mitigated sufficiently for improvements in range errors and positioning accuracy. Recent machine learning-based methods have shown promise in improving pseudorange based position solutions by considering multiple variables from raw GPS measurements. However, positioning accuracy is limited by low accuracy signal reception classification. Unlike the existing methods, which use machine learning to directly predict the signal reception classification, we use a gradient boosting decision tree (GBDT) based method to predict the pseudorange errors by considering the signal strength, satellite elevation angle and pseudorange residuals. With the predicted pseudorange errors, two variations of the algorithm are proposed to improve positioning accuracy. The first corrects pseudorange errors and the other either corrects or excludes the signals determined to contain the effects of multipath and NLOS signals. The results for a challenging urban environment characterized by high-rise buildings on one side, show that the 3D positioning accuracy of the pseudorange error correction-based positioning measured in terms of the root mean square error is 23.3 m, an improvement of more than 70% over the conventional methods.

ACS Style

Rui Sun; Guanyu Wang; Qi Cheng; Linxia Fu; Kai-Wei Chiang; Li-Ta Hsu; Washington Yotto Ochieng. Improving GPS Code Phase Positioning Accuracy in Urban Environments Using Machine Learning. IEEE Internet of Things Journal 2020, PP, 1 -1.

AMA Style

Rui Sun, Guanyu Wang, Qi Cheng, Linxia Fu, Kai-Wei Chiang, Li-Ta Hsu, Washington Yotto Ochieng. Improving GPS Code Phase Positioning Accuracy in Urban Environments Using Machine Learning. IEEE Internet of Things Journal. 2020; PP (99):1-1.

Chicago/Turabian Style

Rui Sun; Guanyu Wang; Qi Cheng; Linxia Fu; Kai-Wei Chiang; Li-Ta Hsu; Washington Yotto Ochieng. 2020. "Improving GPS Code Phase Positioning Accuracy in Urban Environments Using Machine Learning." IEEE Internet of Things Journal PP, no. 99: 1-1.

Letter
Published: 21 August 2020 in Sensors
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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.

ACS Style

Max Jwo Lem Lee; Shang Lee; Hoi-Fung Ng; Li-Ta Hsu. Skymask Matching Aided Positioning Using Sky-Pointing Fisheye Camera and 3D City Models in Urban Canyons. Sensors 2020, 20, 4728 .

AMA Style

Max Jwo Lem Lee, Shang Lee, Hoi-Fung Ng, Li-Ta Hsu. Skymask Matching Aided Positioning Using Sky-Pointing Fisheye Camera and 3D City Models in Urban Canyons. Sensors. 2020; 20 (17):4728.

Chicago/Turabian Style

Max Jwo Lem Lee; Shang Lee; Hoi-Fung Ng; Li-Ta Hsu. 2020. "Skymask Matching Aided Positioning Using Sky-Pointing Fisheye Camera and 3D City Models in Urban Canyons." Sensors 20, no. 17: 4728.

Journal article
Published: 07 August 2020 in Remote Sensing
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Global navigation satellite system (GNSS) is widely regarded as the primary positioning solution for intelligent transport system (ITS) applications. However, its performance could degrade, due to signal outages and faulty-signal contamination, including multipath and non-line-of-sight reception. Considering the limitation of the performance and computation loads in mass-produced automotive products, this research investigates the methods for enhancing GNSS-based solutions without significantly increasing the cost for vehicular navigation system. In this study, the measurement technique of the odometer in modern vehicle designs is selected to integrate the GNSS information, without using an inertial navigation system. Three techniques are implemented to improve positioning accuracy; (a) Time-differenced carrier phase (TDCP) based filter: A state-augmented extended Kalman filter is designed to incorporate TDCP measurements for maximizing the effectiveness of phase-smoothing; (b) odometer-aided constraints: The aiding measurement from odometer utilizing forward speed with the lateral constraint enhances the state estimation; the information based on vehicular motion, comprising the zero-velocity constraint, fault detection and exclusion, and dead reckoning, maintains the stability of the positioning solution; (c) robust regression: A weighted-least-square based robust regression as a measurement-quality assessment is applied to adjust the weightings of the measurements adaptively. Experimental results in a GNSS-challenging environment indicate that, based on the single-point-positioning mode with an automotive-grade receiver, the combination of the proposed methods presented a root-mean-square error of 2.51 m, 3.63 m, 1.63 m, and 1.95 m for the horizontal, vertical, forward, and lateral directions, with improvements of 35.1%, 49.6%, 45.3%, and 21.1%, respectively. The statistical analysis exhibits 97.3% state estimation result in the horizontal direction for the percentage of epochs that had errors of less than 5 m, presenting that after the intervention of proposed methods, the positioning performance can fulfill the requirements for road level applications.

ACS Style

Kai-Wei Chiang; Yu-Hua Li; Li-Ta Hsu; Feng-Yu Chu. The Design a TDCP-Smoothed GNSS/Odometer Integration Scheme with Vehicular-Motion Constraint and Robust Regression. Remote Sensing 2020, 12, 2550 .

AMA Style

Kai-Wei Chiang, Yu-Hua Li, Li-Ta Hsu, Feng-Yu Chu. The Design a TDCP-Smoothed GNSS/Odometer Integration Scheme with Vehicular-Motion Constraint and Robust Regression. Remote Sensing. 2020; 12 (16):2550.

Chicago/Turabian Style

Kai-Wei Chiang; Yu-Hua Li; Li-Ta Hsu; Feng-Yu Chu. 2020. "The Design a TDCP-Smoothed GNSS/Odometer Integration Scheme with Vehicular-Motion Constraint and Robust Regression." Remote Sensing 12, no. 16: 2550.

Journal article
Published: 03 August 2020 in Sustainability
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The rapid growth of air travel and aviation emissions in recent years has contributed to an increase in climate impact. Contrails have been considered one of the main factors of the aviation-induced climate impact. This paper deals with the formation of persistent contrails and its relationship with fuel consumption and flight time when flight altitude and true airspeed vary. Detailed contrail formation conditions pertaining to altitude, relative humidity and temperature are formulated according to the Schmidt–Appleman criterion. Building on the contrail formation model, the proposed model would minimise total travel time, fuel consumption and contrail length associated with a given flight. Empirical data (including pressure, temperature, relative humidity, etc.) collected from seven flight information regions in Chinese observation stations were used to analyse the spatial and temporal distributions of the persistent contrail formation area. The trade-off between flight time, fuel consumption and contrail length are illustrated with a real-world case. The results provided a valuable benchmark for flight route planning with environmental, flight time, sustainable flight trajectory planning and fuel consumption considerations, and showed significant contrail length reduction through an optimal selection of altitude and true airspeed.

ACS Style

Dabin Xue; Kam K. H. Ng; Li-Ta Hsu. Multi-Objective Flight Altitude Decision Considering Contrails, Fuel Consumption and Flight Time. Sustainability 2020, 12, 6253 .

AMA Style

Dabin Xue, Kam K. H. Ng, Li-Ta Hsu. Multi-Objective Flight Altitude Decision Considering Contrails, Fuel Consumption and Flight Time. Sustainability. 2020; 12 (15):6253.

Chicago/Turabian Style

Dabin Xue; Kam K. H. Ng; Li-Ta Hsu. 2020. "Multi-Objective Flight Altitude Decision Considering Contrails, Fuel Consumption and Flight Time." Sustainability 12, no. 15: 6253.

Journal article
Published: 01 July 2020 in IEEE Access
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Achieving accurate and reliable positioning in dynamic urban scenarios using low-cost vehicular onboard sensors, such as the global navigation satellite systems (GNSS), camera, and inertial measurement unit (IMU), is still a challenging problem. Multi-Agent collaborative integration (MCI) opens a new window for achieving this goal, by sharing the sensor measurements between multiple agents to further improve the accuracy of respective positioning. One of the major difficulties in MCI is to effectively connect all the sensor measurements arising from multiple independent agents. The popular approach is to find the overlapping areas between agents using active sensors, such as cameras. However, the performance of overlapping area detection is significantly degraded in outdoor urban areas due to the challenges arising from numerous unexpected moving objects and unstable illumination conditions. To fill this gap, this paper proposes to leverage both the camera-based overlapping area detection and the inter-ranging measurements to boost the cross-connection between multi-agents and brings the MCI to outdoor urban scenarios using low-cost onboard sensors. Moreover, a novel MCI framework is proposed to integrate the sensor measurements from the low-cost GNSS receiver, camera, IMU, and inter-ranging using state-of-the-art factor graph optimization (FGO) to fully explore their complementary properties. The proposed MCI framework is validated using two challenging datasets collected in urban canyons of Hong Kong. We conclude that the proposed MCI framework can effectively improve the positioning accuracy of the respective agents in the evaluated datasets. We believe that the proposed MCI framework has the potential to be prevalently adopted by the connected intelligent transportation systems (ITS) applications to provide robust positioning using low-cost onboard sensors in urban scenarios.

ACS Style

Weisong Wen; Xiwei Bai; Guohao Zhang; Shengdong Chen; Feng Yuan; Li-Ta Hsu. Multi-Agent Collaborative GNSS/Camera/INS Integration Aided by Inter-Ranging for Vehicular Navigation in Urban Areas. IEEE Access 2020, 8, 124323 -124338.

AMA Style

Weisong Wen, Xiwei Bai, Guohao Zhang, Shengdong Chen, Feng Yuan, Li-Ta Hsu. Multi-Agent Collaborative GNSS/Camera/INS Integration Aided by Inter-Ranging for Vehicular Navigation in Urban Areas. IEEE Access. 2020; 8 ():124323-124338.

Chicago/Turabian Style

Weisong Wen; Xiwei Bai; Guohao Zhang; Shengdong Chen; Feng Yuan; Li-Ta Hsu. 2020. "Multi-Agent Collaborative GNSS/Camera/INS Integration Aided by Inter-Ranging for Vehicular Navigation in Urban Areas." IEEE Access 8, no. : 124323-124338.

Journal article
Published: 30 June 2020 in Journal of Navigation
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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.

ACS Style

Hoi-Fung Ng; Guohao Zhang; Li-Ta Hsu. A Computation Effective Range-Based 3D Mapping Aided GNSS with NLOS Correction Method. Journal of Navigation 2020, 73, 1202 -1222.

AMA Style

Hoi-Fung Ng, Guohao Zhang, Li-Ta Hsu. A Computation Effective Range-Based 3D Mapping Aided GNSS with NLOS Correction Method. Journal of Navigation. 2020; 73 (6):1202-1222.

Chicago/Turabian Style

Hoi-Fung Ng; Guohao Zhang; Li-Ta Hsu. 2020. "A Computation Effective Range-Based 3D Mapping Aided GNSS with NLOS Correction Method." Journal of Navigation 73, no. 6: 1202-1222.

Journal article
Published: 11 June 2020 in Advances in Space Research
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The accurate global navigation satellite system (GNSS) positioning in the dense urban areas is still a challenge, especially for low-cost receivers. The multipath effects and non-line-of-sight (NLOS) receptions from surrounding buildings will significantly degrade the positioning performance. Due to the increasing channel number in GNSS chip, the low-cost receiver tends to be capable of acquiring multi-frequency signals, including the new L5-band signal. Because of the higher chipping rate, the GNSS L5-band measurement is less affected by the multipath effect, whereas the measurement number is limited in the current stage. On the contrary, the availability of the conventional L1-band measurement is sufficient to achieve a good dilution of precision (DOP). Based on the complementary characteristics, a GNSS L1/L5 bands integrated positioning algorithm is developed in this study to improve the positioning performance in urban areas. A modified weighting model based on carrier-to-noise ratio and satellite elevation angle is employed to assign proper weighting between L1-band and L5-band measurements. Meanwhile, the dMP5 feature from dual-frequency measurement and the consistency check algorithm are employed to detect and exclude outliers, which are possibly NLOS receptions. Experimental results and analyses indicate that the developed DFE-CCWLS method can significantly improve the positioning accuracy, achieving the root-mean-square error less than 10 m for most of the urban scenarios.

ACS Style

Hoi-Fung Ng; Guohao Zhang; Kai-Yuan Yang; Shi-Xian Yang; Li-Ta Hsu. Improved weighting scheme using consumer-level GNSS L5/E5a/B2a pseudorange measurements in the urban area. Advances in Space Research 2020, 66, 1647 -1658.

AMA Style

Hoi-Fung Ng, Guohao Zhang, Kai-Yuan Yang, Shi-Xian Yang, Li-Ta Hsu. Improved weighting scheme using consumer-level GNSS L5/E5a/B2a pseudorange measurements in the urban area. Advances in Space Research. 2020; 66 (7):1647-1658.

Chicago/Turabian Style

Hoi-Fung Ng; Guohao Zhang; Kai-Yuan Yang; Shi-Xian Yang; Li-Ta Hsu. 2020. "Improved weighting scheme using consumer-level GNSS L5/E5a/B2a pseudorange measurements in the urban area." Advances in Space Research 66, no. 7: 1647-1658.

Journal article
Published: 09 June 2020 in IEEE Intelligent Transportation Systems Magazine
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Positioning is a key function for autonomous vehicles that requires globally referenced localization information. Lidarbased mapping, which refers to simultaneous localization and mapping (SLAM), provides continuous positioning in diverse scenarios. However, SLAM error can accumulate through time. Besides, only relative positioning is provided by SLAM. The Global Navigation Satellite System (GNSS) receiver is one of the significant sensors for providing globally referenced localization, and it is usually integrated with lidar in autonomous driving. However, the performance of the GNSS is severely challenged due to the reflection and blockage caused by buildings in superurbanized cities, including Hong Kong, China; Tokyo; and New York, resulting in the notorious non-line-of-sight (NLOS) receptions. Moreover, the uncertainty of the GNSS positioning is ambiguous, leading to the incorrect tuning of its weight during GNSS?lidar integration. This article innovatively employs lidar to identify the NLOS measurement of the GNSS receiver using point-cloud-based object detection. Measurements from satellites suffering from NLOS reception will be excluded based on the proposed fault detection and exclusion (FDE) algorithm. Then, GNSS-weight least-square positioning is conducted based on the surviving measurements from FDE. The noise covariance of the GNSS positioning is calculated by considering the potential location errors caused by the NLOS and the remaining LOS measurements. The improved GNSS result and its corresponding noise covariance are integrated with lidar through a graph-based SLAM-integration framework. Experimental results indicate that the proposed GNSS?lidar integration can obtain improved positioning accuracy in a highly urbanized area in Hong Kong.

ACS Style

Weisong Wen; Guohao Zhang; Li-Ta Hsu. Object-Detection-Aided GNSS and Its Integration With Lidar in Highly Urbanized Areas. IEEE Intelligent Transportation Systems Magazine 2020, 12, 53 -69.

AMA Style

Weisong Wen, Guohao Zhang, Li-Ta Hsu. Object-Detection-Aided GNSS and Its Integration With Lidar in Highly Urbanized Areas. IEEE Intelligent Transportation Systems Magazine. 2020; 12 (3):53-69.

Chicago/Turabian Style

Weisong Wen; Guohao Zhang; Li-Ta Hsu. 2020. "Object-Detection-Aided GNSS and Its Integration With Lidar in Highly Urbanized Areas." IEEE Intelligent Transportation Systems Magazine 12, no. 3: 53-69.

Research article
Published: 29 May 2020 in IET Intelligent Transport Systems
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Robust and globally-referenced positioning is indispensable for autonomous driving vehicles. Global navigation satellite system (GNSS) is still an irreplaceable sensor. Satisfactory accuracy (about 1 m) can be obtained in sparse areas. However, the GNSS positioning error can be up to 100 m in dense urban areas due to the multipath effects and non-line-of-sight (NLOS) receptions caused by reflection and blockage from buildings. NLOS is currently the dominant factor degrading the performance of GNSS positioning. Recently, the camera has been employed to detect the NLOS and then to exclude the NLOS measurements from GNSS calculation. The exclusion of NLOS measurements can cause severe distortion of satellite distribution, due to the excessive NLOS receptions in deep urban canyons. Correcting the NLOS receptions with the aid of 3D light detection and ranging after detection of NLOS receptions using a fish-eye camera was proposed in this study. Finally, the GNSS positioning was improved by using the healthy and corrected NLOS pseudo-range measurements. The proposed method is evaluated through real road tests in typical highly urbanised canyons of Hong Kong. The evaluation results show that the proposed method can effectively improve the positioning performance.

ACS Style

Xiwei Bai; Weisong Wen; Li‐Ta Hsu. Using Sky‐pointing fish‐eye camera and LiDAR to aid GNSS single‐point positioning in urban canyons. IET Intelligent Transport Systems 2020, 14, 908 -914.

AMA Style

Xiwei Bai, Weisong Wen, Li‐Ta Hsu. Using Sky‐pointing fish‐eye camera and LiDAR to aid GNSS single‐point positioning in urban canyons. IET Intelligent Transport Systems. 2020; 14 (8):908-914.

Chicago/Turabian Style

Xiwei Bai; Weisong Wen; Li‐Ta Hsu. 2020. "Using Sky‐pointing fish‐eye camera and LiDAR to aid GNSS single‐point positioning in urban canyons." IET Intelligent Transport Systems 14, no. 8: 908-914.