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All-Source Position and Navigation: An Alternative Solution for Resilient PNT

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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (20 November 2022) | Viewed by 13218

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Special Issue Editors

Dr. Yuwei Chen

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Guest Editor

Department of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute, 02430 Masala, Finland
Interests: hyperspectral LiDAR; mobile mapping
Special Issues, Collections and Topics in MDPI journals

Dr. Changhui Jiang

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Guest Editor

Department of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute, 02430 Masala, Finland
Interests: hyperspectral LiDAR; GNSS; INS; Kalman filter; mobile mapping
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Dr. Qian Meng

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Guest Editor

School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China
Interests: GNSS; navigation integrity; resilient PNT; multi-sensor information fusion
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Dr. Bing Xu

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Guest Editor

Department of Aeronautical and Aviation Engineering, The Hong Kong Polytechnic University, Hong Kong, China
Interests: signal processing; satellite positioning and navigation; GNSS receiver design; parameter estimation
Special Issues, Collections and Topics in MDPI journals

Dr. Shoubin Chen

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Guest Editor

Shenzhen Key Laboratory of Spatial Smart Sensing and Service, College of Civil and Transportation Engineering, Shenzhen University, Shenzhen 518060, China
Interests: LiDAR SLAM; mobile mapping

Special Issue Information

Dear Colleagues,

Having assured PNT everywhere and at every moment is critical for many infrastructures and applications around the world, e.g., power grids, smartphones, and vehicle. Currently, GNSS is the primary source of PNT information. However, GNSS signals are weak, and they are susceptible to both unintentional and intentional interference and spoofing. Surrounded environments, i.e., urban canyons and tunnels with multipath, non-line-of-sight (NLOS), and signal blockages also negatively influence GNSS position results. The question of how to assure reliable PNT from GNSS in environments where signal interception is challenging or impossible has attracted much attention in the research community. The integration of GNSS with other sensors is widely recognized as the approach with the greatest prospects of providing more reliable PNT information. All-source navigation aims at providing a uniform method and framework for multiple navigation technology, supporting “plug and play” with rapid integration and reconfiguration of any combination of sensors.

This Special Issue of Applied Sciences aims to provide a platform for researchers to publish innovative work on all-source navigation and assured PNT technology for various applications, i.e., in smartphones, ground vehicles, and UAVs. Potential topics include, but are not limited to, the following:

Advanced technologies for MEMS IMU drift suppressing
Advanced sensor technologies, i.e., new gyroscope, new accelerometer, new LiDAR
Advanced method for multipath and NLOS signal classification, mitigation, or correction
Cooperative navigation
Environmental awareness-aided position and navigation with smartphone
Integrity monitor and quality control and assessment
LiDAR/visual SLAM
New concepts or methods for sensors integration under GNSS-denied environments
Opportunity signals processing for reliable position
Resilient framework for multiple sensor integration
Reliable PNT for UAVs, UGVs, and pedestrians in dense urban areas

Prof. Dr. Yuwei Chen
Dr. Changhui Jiang
Dr. Qian Meng
Dr. Bing Xu
Dr. Shoubin Chen
Guest Editors

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Keywords

GNSS
PNT
LiDAR
SLAM
sensor
IMU
gyroscope
accelerometer
smartphone
integrity monitor
multipath
NLOS

Published Papers (6 papers)

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Research

17 pages, 1343 KiB

Open AccessCommunication

LOS Signal Identification Based on Common Chord Intersection Point Position Deviation from MS in Target Localization

by Rui Luo, Hualing Zhao, Ping Deng and Yin Kuang

Appl. Sci. 2023, 13(4), 2566; https://doi.org/10.3390/app13042566 - 16 Feb 2023

Cited by 1 | Viewed by 866

Abstract

Target localization has been a popular research topic in recent years since it is the basis of all kinds of location-based applications. For GNSS-denied urban or indoor environments, the localization method based on time-of-arrival (TOA) is one of the most popular localization methods due to its high accuracy and simplicity. However, the Non-line-of-sight (NLOS) error is the major cause that degrades the accuracy of the TOA-based localization method. Identifying whether a received signal at a base station (BS) is due to a line-of-sight (LOS) transmission or NLOS is the key to TOA-based localization methods. In the popular LOS signal identification methods, compared with statistic signal methods and machine learning methods, the geometric constraint method has the advantages of simplicity and without requiring priori knowledge of signals and large amounts of training datasets. In this paper, we propose a geometric constraint two-step LOS signal identification method based on common chord intersection point position deviation from mobile stations (MS). In the first step, all BSs are divided into multiple BS combinations with every three BSs, the TOA distance error of each BS combination is estimated based on common chord intersection point position deviation from MS, the BS combinations whose TOA distance error satisfy Gaussian distribution are roughly identified as LOS BS combination and enter the second step, the other BS combinations are discarded as NLOS BS combination. In the second step, based on mutual distance threshold and discrimination result matrix, common chord intersection points of LOS BS combination, and corresponding LOS BS combinations are identified. The BSs of LOS BS combinations are identified as LOS BS and the signals received at LOS BS are identified as LOS signal ultimately. Compared with the other two geometric constraint methods, the proposed algorithm has better identification accuracy, and the setting of the identification threshold value has a theoretical basis, which facilitates the application of the proposed algorithm. Full article

(This article belongs to the Special Issue All-Source Position and Navigation: An Alternative Solution for Resilient PNT)

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18 pages, 3946 KiB

Open AccessCommunication

Hybrid TOA/AOA Virtual Station Localization Based on Scattering Signal Identification for GNSS-Denied Urban or Indoor NLOS Environments

by Rui Luo, Lili Yan, Ping Deng and Yin Kuang

Appl. Sci. 2022, 12(23), 12157; https://doi.org/10.3390/app122312157 - 28 Nov 2022

Cited by 1 | Viewed by 1292

Abstract

Accurate localization is the premise of many technologies and applications, such as navigation, emergency assistance and wireless sensor network. For Global Navigation Satellite System (GNSS)-denied urban or indoor environments, various localization technologies based on mobile communication networks or other wireless technologies have been designed and developed. The main challenge for these localization technologies is the presence of a non-line-of-sight (NLOS) propagation environment due to dense obstacles or buildings. The virtual station method is a promising high-accuracy target localization technique in NLOS environments, and the localization of the scatterer is key to the virtual station method. Once one-bounce scattering signals from the same scatterer are identified, the localization of the scatterer can be achieved easily with the existing localization algorithm of line-of-sight (LOS) scenario, and then the localization of NLOS scenarios is converted into a problem of LOS easily. In this paper, a hybrid time of arrival (TOA)/angle of arrival (AOA) virtual station localization algorithm based on scattering signal identification is proposed. Firstly, one-bounce scattering signals from the same scatterer are identified based on TOA/AOA measurements. Next, scatterers are located based on one-bounce scattering signals with the LOS localization algorithm, and then scatterers are regarded as virtual stations and used for mobile station (MS) localization. Compared with the existing research on the virtual station method, the proposed algorithm relies only on TOA/AOA measurements and does not require any assumption or prior knowledge about the scatterer, base station (BS) or MS, which provides a solid foundation for feasible target localization. Simulation results demonstrate, as far as we know, the proposed algorithm outperforms the state-of-the-art hybrid TOA/AOA algorithm in localization accuracy. Full article

(This article belongs to the Special Issue All-Source Position and Navigation: An Alternative Solution for Resilient PNT)

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16 pages, 3631 KiB

Open AccessArticle

Fault Detection of Resilient Navigation System Based on GNSS Pseudo-Range Measurement

by Kecheng Sun, Qinghua Zeng, Jianye Liu and Shouyi Wang

Appl. Sci. 2022, 12(11), 5313; https://doi.org/10.3390/app12115313 - 24 May 2022

Cited by 5 | Viewed by 2085

Abstract

Because of the resilient frame structure, the factor graph is often used in navigation systems to solve the sensor asynchrony problem and realize plug-and-play effectively in the navigation information fusion method. To improve the fault detection performance of resilient integrated navigation systems under complex interference environments, a fault detection method in factor graph navigation framework based on INS measurements and GNSS pseudo-range measurements is proposed in this paper. The proposed method can effectively locate the fault satellite pseudo-range information based on the Chi-square fault detection method. Due to the plug-and-play characteristic of the factor graph framework, this method can quickly isolate faults to improve navigation accuracy. Finally, the effect of the method is verified by simulation and experiment. Compared with the Chi-square fault detection method, positioning accuracy is improved by more than 40%. Full article

(This article belongs to the Special Issue All-Source Position and Navigation: An Alternative Solution for Resilient PNT)

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21 pages, 5735 KiB

Open AccessArticle

GNSS-RTK Adaptively Integrated with LiDAR/IMU Odometry for Continuously Global Positioning in Urban Canyons

by Jiachen Zhang, Weisong Wen, Feng Huang, Yongliang Wang, Xiaodong Chen and Li-Ta Hsu

Appl. Sci. 2022, 12(10), 5193; https://doi.org/10.3390/app12105193 - 20 May 2022

Cited by 10 | Viewed by 3768

Abstract

Global Navigation Satellite System Real-time Kinematic (GNSS-RTK) is an indispensable source for the absolute positioning of autonomous systems. Unfortunately, the performance of the GNSS-RTK is significantly degraded in urban canyons, due to the notorious multipath and Non-Line-of-Sight (NLOS). On the contrary, LiDAR/inertial odometry (LIO) can provide locally accurate pose estimation in structured urban scenarios but is subjected to drift over time. Considering their complementarities, GNSS-RTK, adaptively integrated with LIO was proposed in this paper, aiming to realize continuous and accurate global positioning for autonomous systems in urban scenarios. As one of the main contributions, this paper proposes to identify the quality of the GNSS-RTK solution based on the point cloud map incrementally generated by LIO. A smaller mean elevation angle mask of the surrounding point cloud indicates a relatively open area thus the correspondent GNSS-RTK would be reliable. Global factor graph optimization is performed to fuse reliable GNSS-RTK and LIO. Evaluations are performed on datasets collected in typical urban canyons of Hong Kong. With the help of the proposed GNSS-RTK selection strategy, the performance of the GNSS-RTK/LIO integration was significantly improved with the absolute translation error reduced by more than 50%, compared with the conventional integration method where all the GNSS-RTK solutions are used. Full article

(This article belongs to the Special Issue All-Source Position and Navigation: An Alternative Solution for Resilient PNT)

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12 pages, 2596 KiB

Open AccessArticle

Multi-Scale Factor Image Super-Resolution Algorithm with Information Distillation Network

by Yu Cheng, Shuai Chen, Zeyu Liao and Niujun Zhou

Appl. Sci. 2022, 12(9), 4131; https://doi.org/10.3390/app12094131 - 20 Apr 2022

Viewed by 1707

Abstract

Deep convolutional neural networks with strong expressive ability have achieved impressive performances in single-image super-resolution algorithms. However, excessive convolutions usually consume high computational cost, which limits the application of super-resolution technology in low computing power devices. Besides, super-resolution of arbitrary scale factor has been ignored for a long time. Most previous researchers have trained a specific network model separately for each factor, and taken the super-resolution of several integer scale factors into consideration. In this paper, we put forward a multi-scale factor network (MFN), which dynamically predicts the weights of the upscale filter by taking the scale factor as input, and generates HR images with corresponding scale factors from the weights. This method is suitable for arbitrary scale factors (integer or non-integer). In addition, we use an information distillation structure to gradually extract multi-scale spatial features. Extensive experiments suggest that the proposed method performs favorably against the state-of-the-art SR algorithms in term of visual quality, PSNR/SSIM evaluation indicators, and model parameters. Full article

(This article belongs to the Special Issue All-Source Position and Navigation: An Alternative Solution for Resilient PNT)

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19 pages, 5574 KiB

Open AccessArticle

Hyperspectral Image Classification with Imbalanced Data Based on Semi-Supervised Learning

by Xiaorou Zheng, Jianxin Jia, Jinsong Chen, Shanxin Guo, Luyi Sun, Chan Zhou and Yawei Wang

Appl. Sci. 2022, 12(8), 3943; https://doi.org/10.3390/app12083943 - 13 Apr 2022

Cited by 9 | Viewed by 2229

Abstract

Hyperspectral remote sensing image classification has been widely employed for numerous applications, such as environmental monitoring, agriculture, and mineralogy. During such classification, the number of training samples in each class often varies significantly. This imbalance in the dataset is often not identified because most classifiers are designed under a balanced dataset assumption, which can distort the minority classes or even treat them as noise. This may lead to biased and inaccurate classification results. This issue can be alleviated by applying preprocessing techniques that enable a uniform distribution of the imbalanced data for further classification. However, it is difficult to add new natural features to a training model by artificial combination of samples by using existing preprocessing techniques. For minority classes with sparse samples, the addition of sufficient natural features can effectively alleviate bias and improve the generalization. For such an imbalanced problem, semi-supervised learning is a creative solution that utilizes the rich natural features of unlabeled data, which can be collected at a low cost in the remote sensing classification. In this paper, we propose a novel semi-supervised learning-based preprocessing solution called NearPseudo. In NearPseudo, pseudo-labels are created by the initialization classifier and added to minority classes with the corresponding unlabeled samples. Simultaneously, to increase reliability and reduce the misclassification cost of pseudo-labels, we created a feedback mechanism based on a consistency check to effectively select the unlabeled data and its pseudo-labels. Experiments were conducted on a state-of-the-art representative hyperspectral dataset to verify the proposed method. The experimental results demonstrate that NearPseudo can achieve better classification accuracy than other common processing methods. Furthermore, it can be flexibly applied to most typical classifiers to improve their classification accuracy. With the intervention of NearPseudo, the accuracy of random forest, k-nearest neighbors, logistic regression, and classification and regression tree increased by 1.8%, 4.0%, 6.4%, and 3.7%, respectively. This study addresses research a gap to solve the imbalanced data-based limitations in hyperspectral image classification. Full article

(This article belongs to the Special Issue All-Source Position and Navigation: An Alternative Solution for Resilient PNT)

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