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Remote Sensing in Navigation: State-of-the-Art

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Engineering Remote Sensing".

Deadline for manuscript submissions: closed (31 July 2022) | Viewed by 25585

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Prof. Dr. Yidong Lou

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GNSS Research Center, Wuhan University, 129 Luoyu Road, Wuhan 430079, China
Interests: GNSS precise data processing; navigation
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Dear Colleagues,

Navigation is an essential functionality for self-driving cars, robots, unmanned aircraft vehicles (UAV), autonomous underwater vehicles (AUV), etc. The development of multi-constellation global navigation satellite systems (GNSSs) and advances in real-time high-accuracy GNSS technologies have provided great opportunities for high-accuracy navigation applications. In addition to GNSS, various types of sensors (laser scanner, LiDAR, camera, etc.) and methods that were once used for remote sensing have attracted a new level of attention from both academia and industry in the field of navigation. These advancements have led to increased safety, efficiency, capabilities, and improved accuracies for localization-based services (LBS).

This Special Issue on “Remote Sensing in Navigation: State of the Art” encourages submissions on the state of the art of technologies, methodologies, reviews and applications for navigation.

Prospective authors are welcome to submit original research (not published or currently under consideration by any other publication or conference) and technical papers in the field. Using remote sensing technologies, the expected topics include but are not limited to the following:

GNSS real-time high-accuracy positioning methods and technologies;
GNSS/Vision/Radar/LiDAR based positioning, especially in challenging conditions;
Multi-modal and multi-sensor data fusion;
Advanced integrity methods and technologies of navigation;
The application of GNSS/sensors and technologies for navigation in unmanned ground vehicles (UGV), UAV, AUV, and advanced driver assistance systems (ADAS).

Prof. Dr. Yidong Lou
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

GNSS precise positioning
sensor technologies
sensor fusion
real-time positioning
integrity
autonomous navigation

Published Papers (11 papers)

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Research

24 pages, 11057 KiB

Open AccessArticle

GNSS Urban Positioning with Vision-Aided NLOS Identification

by Hexiong Yao, Zhiqiang Dai, Weixiang Chen, Ting Xie and Xiangwei Zhu

Remote Sens. 2022, 14(21), 5493; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14215493 - 31 Oct 2022

Cited by 3 | Viewed by 1838

Abstract

The global navigation satellite system (GNSS) has played an important role in a broad range of consumer and industrial applications. In particular, cities have become GNSS major application scenarios; however, GNSS signals suffer from blocking, reflection and attenuation in harsh urban environments, resulting in diverse received signals, e.g., non-line-of-sight (NLOS) and multipath signals. NLOS signals often cause severe deterioration in positioning, navigation, and timing (PNT) solutions, which should be identified and excluded. In this paper, we propose a vision-aided NLOS identification method to augment GNSS urban positioning. A skyward omnidirectional camera is installed on a GNSS antenna to collect omnidirectional images of the sky region. After being rectified, these images are processed for sky region segmentation, which is improved by leveraging gradient information and energy function optimization. Image morphology processing is further employed to smooth slender boundaries. After sky region segmentation, the satellites are projected onto the omnidirectional image, from which NLOS satellites are identified. Finally, the identified NLOS satellites are excluded from GNSS PNT estimation, promoting accuracy and stability. Practical test results show that the proposed sky region segmentation module achieves over 96% accuracy, and that completely accurate NLOS identification is achieved for the experimental images. We validate the performance of our method on public datasets. Compared with the raw measurements without screening, the vision-aided NLOS identification method enables improvements of 60.3%, 12.4% and 63.3% in the E, N, and U directions, respectively, as well as an improvement of 58.5% in 3D accuracy. Full article

(This article belongs to the Special Issue Remote Sensing in Navigation: State-of-the-Art)

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21 pages, 6476 KiB

Open AccessArticle

NLOS Identification- and Correction-Focused Fusion of UWB and LiDAR-SLAM Based on Factor Graph Optimization for High-Precision Positioning with Reduced Drift

by Zhijian Chen, Aigong Xu, Xin Sui, Yuting Hao, Cong Zhang and Zhengxu Shi

Remote Sens. 2022, 14(17), 4258; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14174258 - 29 Aug 2022

Cited by 7 | Viewed by 1772

Abstract

In this study, we propose a tightly coupled integrated method of ultrawideband (UWB) and light detection and ranging (LiDAR)-based simultaneous localization and mapping (SLAM) for global navigation satellite system (GNSS)-denied environments to achieve high-precision positioning with reduced drift. Specifically, we focus on non-line-of-sight (NLOS) identification and correction. In previous work, we utilized laser point cloud maps to identify and exclude NLOS measurements in real time to attenuate their severe effects on the integrated system. However, the complete exclusion of NLOS measurements will likely lead to deterioration in the dilution of precision (DOP) for the remaining line-of-sight (LOS) anchors, counterproductively introducing large positioning errors into the integrated system. Therefore, this study considers the ranging accuracy and geometric distribution of UWB anchors and innovatively proposes an NLOS correction method using a grey prediction model. For a poor line-of-sight (LOS) anchor geometric distribution, the grey prediction model is used to fill in the gaps by predicting the NLOS measurements based on historical measurements. Including the corrected measurements effectively improves the original poor geometric configuration, improving the system positioning accuracy. Since conventional filtering-based fusion methods are exceedingly sensitive to measurement outliers, we use state-of-the-art factor graph optimization (FGO) to tightly integrate the UWB measurements (LOS and corrected measurements) with LiDAR-SLAM. The temporal correlation between measurements and the redundant system measurements effectively enhance the robustness of the integrated system. Experimental results show that the tightly coupled integrated method combining NLOS correction and FGO improves the positioning accuracy under a poor geometric distribution, increases the system availability, and achieves better positioning than filtering-based fusion methods with a root-mean-square error of 0.086 m in the plane direction, achieving subdecimeter indoor high-precision positioning. Full article

(This article belongs to the Special Issue Remote Sensing in Navigation: State-of-the-Art)

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16 pages, 5168 KiB

Open AccessArticle

Single-Epoch Ambiguity Resolution of a Large-Scale CORS Network with Multi-Frequency and Multi-Constellation GNSS

by Shengyue Ji, Guofeng Liu, Duojie Weng, Zhenjie Wang, Kaifei He and Wu Chen

Remote Sens. 2022, 14(15), 3819; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14153819 - 08 Aug 2022

Viewed by 1342

Abstract

Ambiguity resolution at Continuously Operating Reference Station (CORS) network sites is the key step in the whole processing chain of Network Real Time Kinematic (NRTK). An appropriate ambiguity-resolution speed is important, and single-epoch ambiguity resolution has not been realized yet, especially for large-scale CORS. We attempt to realize single-epoch ambiguity resolution for a large-scale CORS network by neglecting tropospheric delay through forming difference between satellites with close mapping functions whether they belong to the same or different GNSSs. As only two frequency bands are shared among GPS, Galileo and BeiDou, the biggest challenge is how to get this single-epoch ambiguity solution for wide-lane combinations of L1 and L5 when the difference is formed between satellites of different GNSSs. The proposed method includes five steps for ambiguity resolution for different combinations: extra wide-lane, wide-lane, inter-GNSS wide-lane, subset narrow-lane and narrow-lane. The single-epoch ambiguity-resolution performance is assessed based on GNSS observations from two long-distance baselines formed with IGS stations, BRUX-REDU and BAUT-LEIJ, separated by distances of approximately 104 km and 151 km, respectively. The numerical results show that the fixing rate of the single-epoch ambiguity resolution can reach more than 90%, so for a large-scale CORS network, single-epoch ambiguity resolution is feasible and can be realized in the future. Full article

(This article belongs to the Special Issue Remote Sensing in Navigation: State-of-the-Art)

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19 pages, 8829 KiB

Open AccessArticle

Performance Analysis of BDS-3 SAIM and Enhancement Research on Autonomous Satellite Ephemeris Monitoring

by Lei Chen, Yongshan Dai, Weiguang Gao, Yueling Cao, Zhigang Hu, Qianyi Ren, Xin Nie, Jiaju Zheng, Ruiqiang Shao, Ling Pei and Lu Wang

Remote Sens. 2022, 14(15), 3543; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14153543 - 24 Jul 2022

Viewed by 1439

Abstract

Integrity is one of the key indicators used to characterize the performance of the global navigation satellite systems (GNSSs) and is closely related to user safety. In order to realize real-time global integrity monitoring, the BeiDou Global Navigation Satellite System (BDS-3) has realized the “satellite autonomous integrity monitoring” (SAIM) function in its satellites for the first time. BDS-3 SAIM has the monitoring functions of signal power, pseudo-range, satellite clock frequency and phase, but not the monitoring function of broadcast ephemeris. In this study, the long-term stability and distribution characteristics of BDS-3 SAIM monitoring data were analyzed by using the actual telemetry data for the first time. The results show that the SAIM monitoring data have good long-term stability and basically follow a normal distribution, which meets the design expectations. Meanwhile, in view of the fact that BDS-3 SAIM does not have the ability to independently monitor broadcast ephemerides, which may lead to the over-tolerance of BDS-3 to the probability risk of risks of integrity in the active space environment, a SAIM enhancement design for ephemeris monitoring is proposed, which integrates three relatively independent methods, with the ephemeris extrapolated from the previous cycle, and the ephemeris generated by autonomous orbit determination, inter-satellite link distance measurement data as reference data, respectively. The three methods are analyzed and verified. The results show that each of the three methods has advantages and disadvantages in terms of monitoring accuracy and resource dependence. The integration of the three methods can combine their complementary advantages and can also provide valuable as an important reference for engineering applications. Full article

(This article belongs to the Special Issue Remote Sensing in Navigation: State-of-the-Art)

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21 pages, 32561 KiB

Open AccessArticle

Native Smartphone Single- and Dual-Frequency GNSS-PPP/IMU Solution in Real-World Driving Scenarios

by Ding Yi, Sihan Yang and Sunil Bisnath

Remote Sens. 2022, 14(14), 3286; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14143286 - 08 Jul 2022

Cited by 14 | Viewed by 2064

Abstract

The Global Navigation Satellite System (GNSS) capability in smartphones has seen significant upgrades over the years. The latest ultra-low-cost GNSS receivers are capable of carrier-phase tracking and multi-constellation, dual-frequency signal reception. However, due to the limitations of these ultra-low-cost receivers and antennas, smartphone GNSS position solutions suffer significantly from urban multipath, poor signal reception, and signal blockage. This paper presents a novel sensor fusion technique using Precise Point Positioning (PPP) and the inertial sensors in smartphones, combined with a single- and dual-frequency (SFDF) optimisation scheme for smartphones. The smartphone is field-tested while attached to a vehicle’s dashboard and is driven in multiple real-world situations. A total of five vehicle experiments were conducted and the solutions show that SFDF-PPP outperforms single-frequency PPP (SF-PPP) and dual-frequency PPP (DF-PPP). Solutions can be further improved by integrating with native smartphone IMU measurements and provide consistent horizontal positioning accuracy of <2 m rms through a variety obstructions. These results show a significant improvement from the existing literature using similar hardware in challenging environments. Future work will improve optimising inertial sensor calibration and integrate additional sensors. Full article

(This article belongs to the Special Issue Remote Sensing in Navigation: State-of-the-Art)

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26 pages, 8291 KiB

Open AccessArticle

Improved-UWB/LiDAR-SLAM Tightly Coupled Positioning System with NLOS Identification Using a LiDAR Point Cloud in GNSS-Denied Environments

by Zhijian Chen, Aigong Xu, Xin Sui, Changqiang Wang, Siyu Wang, Jiaxin Gao and Zhengxu Shi

Remote Sens. 2022, 14(6), 1380; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14061380 - 12 Mar 2022

Cited by 17 | Viewed by 3700

Abstract

Reliable absolute positioning is indispensable in long-term positioning systems. Although simultaneous localization and mapping based on light detection and ranging (LiDAR-SLAM) is effective in global navigation satellite system (GNSS)-denied environments, it can provide only local positioning results, with error divergence over distance. Ultrawideband (UWB) technology is an effective alternative; however, non-line-of-sight (NLOS) propagation in complex indoor environments severely affects the precision of UWB positioning, and LiDAR-SLAM typically provides more robust results under such conditions. For robust and high-precision positioning, we propose an improved-UWB/LiDAR-SLAM tightly coupled (TC) integrated algorithm. This method is the first to combine a LiDAR point cloud map generated via LiDAR-SLAM with position information from UWB anchors to distinguish between line-of-sight (LOS) and NLOS measurements through obstacle detection and NLOS identification (NI) in real time. Additionally, to alleviate positioning error accumulation in long-term SLAM, an improved-UWB/LiDAR-SLAM TC positioning model is constructed using UWB LOS measurements and LiDAR-SLAM positioning information. Parameter solving using a robust extended Kalman filter (REKF) to suppress the effect of UWB gross errors improves the robustness and positioning performance of the integrated system. Experimental results show that the proposed NI method using the LiDAR point cloud can efficiently and accurately identify UWB NLOS errors to improve the performance of UWB ranging and positioning in real scenarios. The TC integrated method combining NI and REKF achieves better positioning effectiveness and robustness than other comparative methods and satisfactory control of sensor errors with a root-mean-square error of 0.094 m, realizing subdecimeter indoor positioning. Full article

(This article belongs to the Special Issue Remote Sensing in Navigation: State-of-the-Art)

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20 pages, 3771 KiB

Open AccessArticle

An Error Overbounding Method Based on a Gaussian Mixture Model with Uncertainty Estimation for a Dual-Frequency Ground-Based Augmentation System

by Zhen Gao, Kun Fang, Zhipeng Wang, Kai Guo and Yuan Liu

Remote Sens. 2022, 14(5), 1111; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14051111 - 24 Feb 2022

Cited by 2 | Viewed by 1902

Abstract

To ensure the integrity of a ground-based augmentation system (GBAS), an ionosphere-free (Ifree) filtering algorithm with dual-frequency measurements is employed to make the GBAS free of the first-order ionospheric influence. However, the Ifree algorithm outputs the errors of two frequencies. The protection level obtained via the traditional Gaussian overbound is overconservative. This conservatism may cause false alarms and diminish availability. An overbounding framework based on a Gaussian mixture model (GMM) is proposed to handle samples drawn from Ifree-based GBAS range errors. The GMM is employed to model the single-frequency errors that concern the uncertainty estimation. A Monte Carlo simulation is performed to determine the accuracy of the estimated GMM confidence level obtained by using the general estimation approach. Then, the final GMM used to overbound the Ifree error distribution is analyzed. Based on the convolution invariance property, vertical protection levels in the position domain are explicitly derived without introducing complex numerical calculations. A performance evaluation based on a real-world road test shows that the Ifree-based vertical protection levels are tightened with a small computational cost. Full article

(This article belongs to the Special Issue Remote Sensing in Navigation: State-of-the-Art)

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14 pages, 46606 KiB

Open AccessArticle

Real-Time BDS-3 Clock Estimation with a Multi-Frequency Uncombined Model including New B1C/B2a Signals

by Kaifa Kuang, Jian Wang and Houzeng Han

Remote Sens. 2022, 14(4), 966; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14040966 - 16 Feb 2022

Cited by 7 | Viewed by 1587

Abstract

The global system of BDS (BeiDou Navigation Satellite System), i.e., BDS-3, is characterized with a multi-frequency signal broadcasting capability, which was demonstrated as beneficial for GNSS (Global Navigation Satellite System) data processing. However, research on real-time BDS-3 clock estimation with multi-frequency signals is quite limited, especially for the new B1C and B2a signals. In this study, we developed models for BDS-3 multi-frequency real-time data processing, including the uncombined model for clock estimation and the GFIF (Geometry-Free Ionosphere-Free) combined model for IFCB (Inter-Frequency Clock Bias) determination. Based on the models, simulated real-time numerical experiments with about 80 global IGS (International GNSS Service) network stations are conducted for validation and analysis. The results indicate that: (1) the uncombined model with multi-frequency signals can achieve comparable accuracy with the traditional dual-frequency IF model in terms of clock estimation, and the double-differenced clock STDs (Standard Deviations) are generally less than 0.05 ns with post-processed clocks as a reference; (2) unlike the B1C and B1I/B3I signals, the satellite IFCBs generated from multi-frequency clock estimation show apparent temporal variations for B2a and B1I/B3I signals, further investigation with GFIF models confirm the variations mainly result from the errors of receiver antenna corrections. Therefore, we addressed the feasibility of the uncombined model and the importance of accurate antenna information in the multi-frequency data processing. Full article

(This article belongs to the Special Issue Remote Sensing in Navigation: State-of-the-Art)

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20 pages, 5110 KiB

Open AccessArticle

An eLoran Signal Cycle Identification Method Based on Joint Time–Frequency Domain

by Wenhe Yan, Ming Dong, Shifeng Li, Chaozhong Yang, Jiangbin Yuan, Zhaopeng Hu and Yu Hua

Remote Sens. 2022, 14(2), 250; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14020250 - 06 Jan 2022

Cited by 7 | Viewed by 2831

Abstract

The eLoran system is an international standardized positioning, navigation, and timing service system, which can complement global navigation satellite systems to cope with navigation and timing warfare. The eLoran receiver measures time-of-arrival (TOA) through cycle identification, which is key in determining timing and positioning accuracy. However, noise and skywave interference can cause cycle identification errors, resulting in TOA-measurement errors that are integral multiples of 10 μs. Therefore, this article proposes a cycle identification method in the joint time–frequency domain. Based on the spectrum-division method to determine the cycle identification range, the time–domain peak-to-peak ratio and waveform matching are used for accurate cycle identification. The performance of the method is analyzed via simulation. When the signal-to-noise ratio (SNR) ≥ 0 dB and skywave-to-groundwave ratio (SGR) ≤ 23 dB, the success rate of cycle identification is 100%; when SNR ≥ −13 dB and SGR ≤ 23 dB, the success rate exceeds 75%. To verify its practicability, the method was implemented in the eLoran receiver and tested at three test sites within 1000 km using actual signals emitted by an eLoran system. The results show that the method has a high identification probability and can be used in modern eLoran receivers to improve TOA-measurement accuracy. Full article

(This article belongs to the Special Issue Remote Sensing in Navigation: State-of-the-Art)

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25 pages, 14725 KiB

Open AccessArticle

A Seamless Navigation System and Applications for Autonomous Vehicles Using a Tightly Coupled GNSS/UWB/INS/Map Integration Scheme

by Changqiang Wang, Aigong Xu, Xin Sui, Yushi Hao, Zhengxu Shi and Zhijian Chen

Remote Sens. 2022, 14(1), 27; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14010027 - 22 Dec 2021

Cited by 12 | Viewed by 3486

Abstract

Seamless positioning systems for complex environments have been a popular focus of research on positioning safety for autonomous vehicles (AVs). In particular, the seamless high-precision positioning of AVs indoors and outdoors still poses considerable challenges and requires continuous, reliable, and high-precision positioning information to guarantee the safety of driving. To obtain effective positioning information, multiconstellation global navigation satellite system (multi-GNSS) real-time kinematics (RTK) and an inertial navigation system (INS) have been widely integrated into AVs. However, integrated multi-GNSS and INS applications cannot provide effective and seamless positioning results for AVs in indoor and outdoor environments due to limited satellite availability, multipath effects, frequent signal blockages, and the lack of GNSS signals indoors. In this contribution, multi-GNSS-tightly coupled (TC) RTK/INS technology is developed to solve the positioning problem for a challenging urban outdoor environment. In addition, ultrawideband (UWB)/INS technology is developed to provide accurate and continuous positioning results in indoor environments, and INS and map information are used to identify and eliminate UWB non-line-of-sight (NLOS) errors. Finally, an improved adaptive robust extended Kalman filter (AREKF) algorithm based on a TC integrated single-frequency multi-GNSS-TC RTK/UWB/INS/map system is studied to provide continuous, reliable, high-precision positioning information to AVs in indoor and outdoor environments. Experimental results show that the proposed scheme is capable of seamlessly guaranteeing the positioning accuracy of AVs in complex indoor and outdoor environments involving many measurement outliers and environmental interference effects. Full article

(This article belongs to the Special Issue Remote Sensing in Navigation: State-of-the-Art)

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23 pages, 4665 KiB

Open AccessArticle

Efficient and Safe Robotic Autonomous Environment Exploration Using Integrated Frontier Detection and Multiple Path Evaluation

by Yuxi Sun and Chengrui Zhang

Remote Sens. 2021, 13(23), 4881; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13234881 - 01 Dec 2021

Cited by 3 | Viewed by 2135

Abstract

Autonomous exploration and remote sensing using robots have gained increasing attention in recent years and aims to maximize information collection regarding the external world without human intervention. However, incomplete frontier detection, an inability to eliminate inefficient frontiers, and incomplete evaluation limit further improvements in autonomous exploration efficiency. This article provides a systematic solution for ground mobile robot exploration with high efficiency. Firstly, an integrated frontier detection and maintenance method is proposed, which incrementally discovers potential frontiers and achieves incremental maintenance of the safe and informative frontiers by updating the distance map locally. Secondly, we propose a novel multiple paths planning method to generate multiple paths from the robot position to the unexplored frontiers. Then, we use the proposed utility function to select the optimal path and improve its smoothness using an iterative optimization strategy. Ultimately, the model predictive control (MPC) method is applied to track the smooth path. Simulation experiments on typical environments demonstrate that compared with the benchmark methods, the proposed method reduce the path length by 27.07% and the exploration time by 27.09% on average. The real-world experimental results also reveal that our proposed method can achieve complete mapping with fewer repetitive paths. Full article

(This article belongs to the Special Issue Remote Sensing in Navigation: State-of-the-Art)

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