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Advanced Technologies for Position and Navigation under GNSS Signal Challenging or Denied Environments II

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

Deadline for manuscript submissions: closed (1 April 2023) | Viewed by 34258

Special Issue Editors

Dr. Changhui Jiang

E-Mail Website
Guest Editor
College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
Interests: hyperspectral LiDAR; GNSS; INS; Kalman filter; mobile mapping
Special Issues, Collections and Topics in MDPI journals
Dr. Yuwei Chen

E-Mail Website
Guest Editor
Finish Geospatial Research Institute in National Land Survey of Finland, Vuorimiehentie 5, 02150 Espoo, Finland
Interests: positioning and navigation technologies; multi-sensor fusion; LiDAR scanning
Special Issues, Collections and Topics in MDPI journals
Dr. Qian Meng

E-Mail Website
Guest Editor
School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China
Interests: GNSS; navigation integrity; resilient PNT; multi-sensor information fusion
Special Issues, Collections and Topics in MDPI journals
Dr. Panlong Wu

E-Mail Website
Guest Editor
School of Automation, Nanjing University of Science and Technology, Nanjing 210094, China
Interests: data fusion; target tracking; nonlinear filtering; integrated navigation
Special Issues, Collections and Topics in MDPI journals
Dr. Bing Xu

E-Mail Website
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. Lianwu Guan

E-Mail Website
Guest Editor
College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, China
Interests: intelligent navigation; integrated navigation; cross-media navigation
Special Issues, Collections and Topics in MDPI journals
Dr. Wang Gao

E-Mail
Guest Editor
School of Instrument Science and Engineering, Southesast University, Nanjing 210000, China
Interests: satellite geodesy; GNSS precise positioning; integrated navigation; multisensor fusion navigation; parameter estimation
Special Issues, Collections and Topics in MDPI journals
Dr. Zeyu Li

E-Mail Website
Guest Editor
College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China
Interests: simultaneous localization and mapping; quality control
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Currently, with the popularity of smart devices, assured position navigation and time (PNT) is critical for these devices and some fundamental infrastructures, e.g., the power grid. Global navigation satellite systems (GNSSs) are dominant in providing PNT information due to their coverage and high accuracy. However, their signals are weak, and they are vulnerable; multipath and non-line-of-sight (NLOS) signals are the major errors that occur with regard to GNSSs in applications in urban areas. Advanced signal processing methods are expected to improve their resilience and assurance. In addition, GNSSs are fragile to interference and spoofing, which should be emphasized for unmanned systems and smart devices.

Apart from improving GNSS resilience in signal-challenging environments, PNT without a GNSS is critical for many applications—i.e., indoors, tunnels, underground, etc. Advanced technologies on high-accuracy inertial sensors and timing devices—i.e., MEMS gyroscope, atomic interferometer gyroscope, nuclear magnetic resonance gyroscope, chip scale atomic clock, etc.—are key to supporting PNT in GNSS-denied environments. Multisensor integration is also a prospective solution. The previous Special Issue “Advanced Technologies for Position and Navigation under GNSS Signal Challenging or Denied Environments” was a great success. This second volume aims to provide a platform for researchers to publish innovative work on advanced technologies for position and navigation under GNSS signal-challenging or denied environments. Specifically, we invite contributions concerning the following topics:

  1. GNSS multipath and NLOS identification, mitigation or correction;
  2. Weak GNSS signal tracking and position determination;
  3. GNSS interference and spoofing detection;
  4. LiDAR/Visual SLAM;
  5. MEMS inertial measurement unit;
  6. Atomic Interferometer Gyroscope and Accelerometer;
  7. Indoor position;
  8. Multi-sensor integration and fusion;
  9. Micro-Technology for Positioning, Navigation, and Timing.
  10. Control theory and cooperative navigation.

Dr. Changhui Jiang
Dr. Yuwei Chen
Dr. Qian Meng
Dr. Panlong Wu
Dr. Bing Xu
Dr. Lianwu Guan
Dr. Wang Gao
Dr. Zeyu Li
Guest Editors

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

  • microtechnology PNT
  • micro-inertial sensors
  • GNSS
  • NLOS
  • multipath
  • signal processing
  • multisensor integration
  • LiDAR SLAM
  • visual SLAM
  • cooperative navigation
  • pedestrian dead reckoning
  • inertial navigation system
  • smartphone
  • autonomous driving
  • indoor position
  • RAIM (receiver autonomous integrity monitoring)
  • chip scale atomic clock

Related Special Issue

Published Papers (19 papers)

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26 pages, 10013 KiB  
Article
Pseudolite Multipath Estimation Adaptive Mitigation of Vector Tracking Based on Ref-MEDLL
by Bo Zhang, Qing Wang, Wenqing Xia, Yu Sun and Jinling Wang
Remote Sens. 2023, 15(16), 4041; https://doi.org/10.3390/rs15164041 - 15 Aug 2023
Cited by 1 | Viewed by 989
Abstract
Among many indoor positioning technologies, pseudolite positioning technology has become an important supplement to GNSS. In indoor open environments, pseudolite positioning technology can usually perform high-precision positioning. However, in a complex environment, the pseudolite receiver is seriously interfered by multipath and other interference [...] Read more.
Among many indoor positioning technologies, pseudolite positioning technology has become an important supplement to GNSS. In indoor open environments, pseudolite positioning technology can usually perform high-precision positioning. However, in a complex environment, the pseudolite receiver is seriously interfered by multipath and other interference signals, which will lead to a serious decline in the observation accuracy, signal lock loss or even no positioning results. Therefore, this work proposes a pseudolite indoor anti-multipath receiver based on reference multipath estimating delay lock loop (Ref-MEDLL). It adds the Ref-MEDLL multipath estimator module and the multipath mitigation module to the traditional receiver signal processing architecture. In the multipath mitigation module, the multipath estimation adaptive mitigation of vector tracking (MEAM-VT) method and the multipath estimation direct mitigation (MEDM) method for multipath mitigation is proposed. Experimental results show that the Ref-MEDLL multipath estimation method has good adaptability to multipath signals that have different time delays and different amplitudes; both the MEDM receiver and the MEAM-VT receiver have good multipath mitigation performance. The MEAM-VT method performs better than the MEDM method in multipath mitigation and tracking, but the stability of the pseudorange observations of the MEAM-VT method is not as good as that of the MEDM method. The positioning accuracy of the MEDM receiver and the MEAM-VT receiver has been improved to different degrees in static positioning experiments and dynamic positioning experiments. Full article
(This article belongs to the Special Issue Advanced Technologies for Position and Navigation under GNSS Signal Challenging or Denied Environments II)
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20 pages, 16867 KiB  
Article
Robust Pedestrian Dead Reckoning Integrating Magnetic Field Signals and Digital Terrestrial Multimedia Broadcasting Signals
by Xiaoyan Liu, Liang Chen, Zhenhang Jiao and Xiangchen Lu
Remote Sens. 2023, 15(13), 3229; https://doi.org/10.3390/rs15133229 - 22 Jun 2023
Cited by 1 | Viewed by 938
Abstract
Currently, many positioning technologies complementary to Global Navigation Satellite System (GNSS) are providing ubiquitous positioning services, especially the coupling positioning of Pedestrian Dead Reckoning (PDR) and other signals. Magnetic field signals are stable and ubiquitous, while Digital Terrestrial Multimedia Broadcasting (DTMB) signals have [...] Read more.
Currently, many positioning technologies complementary to Global Navigation Satellite System (GNSS) are providing ubiquitous positioning services, especially the coupling positioning of Pedestrian Dead Reckoning (PDR) and other signals. Magnetic field signals are stable and ubiquitous, while Digital Terrestrial Multimedia Broadcasting (DTMB) signals have strong penetration and stable transmission over a large range. To improve the positioning performance of PDR, this paper proposes a robust PDR integrating magnetic field signals and DTMB signals. In our study, the Spiking Neural Network (SNN) is first used to learn the magnetic field signals of the environment, and then the learning model is used to detect the magnetic field landmarks. At the same time, the DTMB signals are collected by the self-developed signal receiver, and then the carrier phase ranging of the DTMB signals is realized. Finally, robust pedestrian positioning is achieved by integrating position information from magnetic field landmarks and ranging information from DTMB signals through Extended Kalman Filter (EKF). We have conducted indoor and outdoor field tests to verify the proposed method, and the outdoor field test results showed that the positioning error cumulative distribution of the proposed method reaches 2.84 m at a 68% probability level, while that of the PDR only reaches 8.77 m. The proposed method has been validated to be effective and has good positioning performance, providing an alternative solution for seamless indoor and outdoor positioning. Full article
(This article belongs to the Special Issue Advanced Technologies for Position and Navigation under GNSS Signal Challenging or Denied Environments II)
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22 pages, 7575 KiB  
Article
A Novel Device-Free Positioning Method Based on Wi-Fi CSI with NLOS Detection and Bayes Classification
by Xingyu Zheng, Ruizhi Chen, Liang Chen, Lei Wang, Yue Yu, Zhenbing Zhang, Wei Li, Yu Pei, Dewen Wu and Yanlin Ruan
Remote Sens. 2023, 15(10), 2676; https://doi.org/10.3390/rs15102676 - 21 May 2023
Cited by 1 | Viewed by 1613
Abstract
Device-free wireless localization based on Wi-Fi channel state information (CSI) is an emerging technique that could estimate users’ indoor locations without invading their privacy or requiring special equipment. It deduces the position of a person by analyzing the influence on the CSI of [...] Read more.
Device-free wireless localization based on Wi-Fi channel state information (CSI) is an emerging technique that could estimate users’ indoor locations without invading their privacy or requiring special equipment. It deduces the position of a person by analyzing the influence on the CSI of Wi-Fi signals. When pedestrians block the signals between the transceivers, the non-line-of-sight (NLOS) transmission occurs. It should be noted that NLOS has been a significant factor restricting the device-free positioning accuracy due to signal reduction and abnormalities during multipath propagation. For this problem, we analyzed the NLOS effect in an indoor environment and found that the position error in the LOS condition is different from the NLOS condition. Then, two empirical models, namely, a CSI passive positioning model and a CSI NLOS/LOS detection model, have been derived empirically with extensive study, which can obtain better robustness identified results in the case of NLOS and LOS conditions. An algorithm called SVM-NB (Support Vector Machine-Naive Bayes) is proposed to integrate the SVM NLOS detection model with the Naive Bayes fingerprint method to narrow the matching area and improve position accuracy. The NLOS identification precision is better than 97%. The proposed method achieves localization accuracy of 0.82 and 0.73 m in laboratory and corridor scenes, respectively. Compared to the Bayes method, our tests showed that the positioning accuracy of the NLOS condition is improved by 28.7% and that of the LOS condition by 26.2%. Full article
(This article belongs to the Special Issue Advanced Technologies for Position and Navigation under GNSS Signal Challenging or Denied Environments II)
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19 pages, 36976 KiB  
Article
Constrained MEMS-Based INS/UWB Tightly Coupled System for Accurate UGVs Navigation
by Jing Mi, Qing Wang and Xiaotao Han
Remote Sens. 2023, 15(10), 2535; https://doi.org/10.3390/rs15102535 - 11 May 2023
Viewed by 1111
Abstract
To enhance the navigation performance and robustness of navigation system combining ultrawideband (UWB) and inertial navigation systems (INS) under complex indoor environments, an improved navigation method—Allan variance (AV) to assist a modified adaptive extended Kalman Filter based on the dynamic weight function (DWF-MAEFF)—is [...] Read more.
To enhance the navigation performance and robustness of navigation system combining ultrawideband (UWB) and inertial navigation systems (INS) under complex indoor environments, an improved navigation method—Allan variance (AV) to assist a modified adaptive extended Kalman Filter based on the dynamic weight function (DWF-MAEFF)—is proposed. Firstly, AV is used to improved INS error dynamics by modeling the stochastic noise of an inertial sensor; which can compensate for inertial sensor error caused by stochastic noise during integrated navigation. Secondly, the MAEKF is developed by designing the weight function to adjust the weight of measurement noise reasonably and dynamically, which can further improve the robustness of the AEKF algorithm. Field tests were conducted to verify the effectiveness of the proposed navigation method. The result indicated that an improvement of up to 60% over the existing integrated navigation method based on EKF and AEKF can be obtained by the proposed method. Full article
(This article belongs to the Special Issue Advanced Technologies for Position and Navigation under GNSS Signal Challenging or Denied Environments II)
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21 pages, 29512 KiB  
Article
Pedestrian Smartphone Navigation Based on Weighted Graph Factor Optimization Utilizing GPS/BDS Multi-Constellation
by Chen Chen, Jianliang Zhu, Yuming Bo, Yuwei Chen, Changhui Jiang, Jianxin Jia, Zhiyong Duan, Mika Karjalainen and Juha Hyyppä
Remote Sens. 2023, 15(10), 2506; https://doi.org/10.3390/rs15102506 - 10 May 2023
Cited by 4 | Viewed by 1729
Abstract
Many studies have focused on the smartphone-based global navigation satellite system (GNSS) for its portability. However, complex urban environments, such as urban canyons and tunnels, can easily interfere with GNSS signal qualities. Current smartphone-based positioning technologies using the GNSS signal still pose great [...] Read more.
Many studies have focused on the smartphone-based global navigation satellite system (GNSS) for its portability. However, complex urban environments, such as urban canyons and tunnels, can easily interfere with GNSS signal qualities. Current smartphone-based positioning technologies using the GNSS signal still pose great challenges. Since the last satellite of the BeiDou navigation system (BDS) was successfully launched on 23 June 2020, it is possible to use a low-cost Android device to realize the localization based on the BDS signals worldwide. This research focuses on smartphone-based outdoor pedestrian navigation utilizing the GPS/BDS multi-constellation system. To improve the localization accuracy, we proposed the Weighted Factor Graph Optimization localization model (W-FGO). In this paper, firstly, we evaluate the signal qualities of the BDS via the data collected by the static experiment. Then, we structure the cost function based on the pseudo-range and the time series data for the traditional Factor Graph Optimization (FGO). Finally, we design the weight model based on the signal quality of each satellite and the time fading factor to further improve the localization accuracy of the conventional FGO method. An Android smartphone is utilized to collect the GNSS data for the evaluation and the localization. The experiment results demonstrate the superior performance of the proposed method. Full article
(This article belongs to the Special Issue Advanced Technologies for Position and Navigation under GNSS Signal Challenging or Denied Environments II)
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20 pages, 5190 KiB  
Article
An ROI Optimization Method Based on Dynamic Estimation Adjustment Model
by Ziyue Li, Qinghua Zeng, Yuchao Liu and Jianye Liu
Remote Sens. 2023, 15(9), 2434; https://doi.org/10.3390/rs15092434 - 05 May 2023
Cited by 1 | Viewed by 1371
Abstract
An important research direction in the field of traffic light recognition of autonomous systems is to accurately obtain the region of interest (ROI) of the image through the multi-sensor assisted method. Dynamic evaluation of the performance of the multi-sensor (GNSS, IMU, and odometer) [...] Read more.
An important research direction in the field of traffic light recognition of autonomous systems is to accurately obtain the region of interest (ROI) of the image through the multi-sensor assisted method. Dynamic evaluation of the performance of the multi-sensor (GNSS, IMU, and odometer) fusion positioning system to obtain the optimum size of the ROI is essential for further improvement of recognition accuracy. In this paper, we propose a dynamic estimation adjustment (DEA) model construction method to optimize the ROI. First, according to the residual variance of the integrated navigation system and the vehicle velocity, we divide the innovation into an approximate Gaussian fitting region (AGFR) and a Gaussian convergence region (GCR) and estimate them using variational Bayesian gated recurrent unit (VBGRU) networks and a Gaussian mixture model (GMM), respectively, to obtain the GNSS measurement uncertainty. Then, the relationship between the GNSS measurement uncertainty and the multi-sensor aided ROI acquisition error is deduced and analyzed in detail. Further, we build a dynamic estimation adjustment model to convert the innovation of the multi-sensor integrated navigation system into the optimal ROI size of the traffic lights online. Finally, we use the YOLOv4 model to detect and recognize the traffic lights in the ROI. Based on laboratory simulation and real road tests, we verify the performance of the DEA model. The experimental results show that the proposed algorithm is more suitable for the application of autonomous vehicles in complex urban road scenarios than the existing achievements. Full article
(This article belongs to the Special Issue Advanced Technologies for Position and Navigation under GNSS Signal Challenging or Denied Environments II)
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22 pages, 4268 KiB  
Article
Compression of GNSS Data with the Aim of Speeding up Communication to Autonomous Vehicles
by Amnon Rakhmanov and Yair Wiseman
Remote Sens. 2023, 15(8), 2165; https://doi.org/10.3390/rs15082165 - 19 Apr 2023
Cited by 10 | Viewed by 1776
Abstract
Autonomous vehicles contain many sensors, enabling them to drive by themselves. Autonomous vehicles need to communicate with other vehicles (V2V) wirelessly and with infrastructures (V2I) like satellites with diverse connections as well, to implement safety, reliability, and efficiency. Information transfer from remote communication [...] Read more.
Autonomous vehicles contain many sensors, enabling them to drive by themselves. Autonomous vehicles need to communicate with other vehicles (V2V) wirelessly and with infrastructures (V2I) like satellites with diverse connections as well, to implement safety, reliability, and efficiency. Information transfer from remote communication appliances is a critical task and should be accomplished quickly, in real time and with maximum reliability. A message that arrives late, arrives with errors, or does not arrive at all can create an unsafe situation. This study aims at employing data compression to efficiently transmit GNSS information to an autonomous vehicle or other infrastructure such as a satellite with maximum accuracy and efficiency. We developed a method for compressing NMEA data. Furthermore, our results were better than other ones in current studies, while supporting error tolerance and data omission. Full article
(This article belongs to the Special Issue Advanced Technologies for Position and Navigation under GNSS Signal Challenging or Denied Environments II)
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23 pages, 5023 KiB  
Article
A Design of Differential-Low Earth Orbit Opportunistically Enhanced GNSS (D-LoeGNSS) Navigation Framework
by Muyuan Jiang, Honglei Qin, Yu Su, Fangchi Li and Jianwu Mao
Remote Sens. 2023, 15(8), 2136; https://doi.org/10.3390/rs15082136 - 18 Apr 2023
Cited by 5 | Viewed by 1520
Abstract
Considering the problem of GNSS service interruption caused by the insufficient number of available satellites in complex environments, Low Earth Orbit (LEO) satellites can supplement GNSS effectively. To eliminate the unknown satellite clock error and the atmospheric delay error with spatial correlation in [...] Read more.
Considering the problem of GNSS service interruption caused by the insufficient number of available satellites in complex environments, Low Earth Orbit (LEO) satellites can supplement GNSS effectively. To eliminate the unknown satellite clock error and the atmospheric delay error with spatial correlation in LEO observations, a Differential-Low Earth Orbit opportunistically enhancing GNSS (D-LoeGNSS) navigation framework is proposed. Firstly, because of the uncertainty of the LEO orbit, we derive the effect of the LEO orbit error on the differential measurement model. Secondly, aiming at the noise amplification and correlation in double-difference (DD), we propose a Householder-Based D-LoeGNSS (HB-DLG) algorithm, which suppresses noise by introducing an orthogonal matrix. Thirdly, in D-LoeGNSS, the typical measurement of LEO is Doppler, which is heterogeneous with the GNSS pseudorange, rendering the Dilution of Precision (DOP) evaluation method unsuitable. Given the unbiasedness of differential measurements, the Cramer Rao Lower Bound (CRLB) is derived as a metric to characterize the positioning accuracy and satellite spatial distribution. Finally, a field experiment using Orbcomm (ORB) and GPS is conducted. The experimental results show that the performance of the HB-DLG algorithm is superior to DD. Especially when the number of satellites is insufficient or the measurement redundancy is poor; the D-LoeGNSS framework has advantages of rapid convergence and high accuracy compared with a single constellation. Full article
(This article belongs to the Special Issue Advanced Technologies for Position and Navigation under GNSS Signal Challenging or Denied Environments II)
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22 pages, 8425 KiB  
Article
LSOS: An FG Position Method Based on Group Phase Ranging Ambiguity Estimation of BeiDou Pseudolite
by Heng Zhang and Shuguo Pan
Remote Sens. 2023, 15(7), 1924; https://doi.org/10.3390/rs15071924 - 03 Apr 2023
Viewed by 1458
Abstract
Due to the influence of indoor space environments, the carrier phase information obtained by the BeiDou pseudo-satellite often has the issue of cycle slips, which makes the user unable to carry out high-precision positioning. Aiming at the problem of ambiguity resolution (AR) and [...] Read more.
Due to the influence of indoor space environments, the carrier phase information obtained by the BeiDou pseudo-satellite often has the issue of cycle slips, which makes the user unable to carry out high-precision positioning. Aiming at the problem of ambiguity resolution (AR) and location in large-scale occluded space (LSOS), a factor graph (FG) position method based on group phase ranging ambiguity estimation of BeiDou pseudolite is proposed. Firstly, by introducing the principle of group phase period quantization and utilizing the multi-frequency characteristic of the BeiDou pseudo-satellite, the carrier phase propagation ambiguity of the BeiDou pseudo-satellite can be estimated quickly. On this basis, by introducing the shuffled frog leading algorithm (SFLA) assisted factor graph optimization location estimation method, the BeiDou pseudo-satellite positioning process in LSOS is realized. The experimental results show that the proposed method can solve the problem of fast estimation of ranging ambiguity of BeiDou pseudolite in LSOS, and the ranging accuracy can be improved to two wavelength ranges. In the further location experiment, it is found that the algorithm can not only guarantee the real-time location output but also improve the location precision to sub-meter level under the multi-frequency combination; the optimal location test precision is 9 cm, the maximum positioning error is 50 cm. This method successfully solves the problem wherein the BeiDou pseudo-satellite cannot provide real-time, continuous, and high-precision positioning in LSOS. Full article
(This article belongs to the Special Issue Advanced Technologies for Position and Navigation under GNSS Signal Challenging or Denied Environments II)
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25 pages, 4453 KiB  
Article
Intelligent Fusion Structure for Wi-Fi/BLE/QR/MEMS Sensor-Based Indoor Localization
by Yue Yu, Yi Zhang, Liang Chen and Ruizhi Chen
Remote Sens. 2023, 15(5), 1202; https://doi.org/10.3390/rs15051202 - 22 Feb 2023
Cited by 8 | Viewed by 2230
Abstract
Due to the complexity of urban environments, localizing pedestrians indoors using mobile terminals is an urgent task in many emerging areas. Multi-source fusion-based localization is considered to be an effective way to provide location-based services in large-scale indoor areas. This paper presents an [...] Read more.
Due to the complexity of urban environments, localizing pedestrians indoors using mobile terminals is an urgent task in many emerging areas. Multi-source fusion-based localization is considered to be an effective way to provide location-based services in large-scale indoor areas. This paper presents an intelligent 3D indoor localization framework that uses the integration of Wi-Fi, Bluetooth Low Energy (BLE), quick response (QR) code, and micro-electro-mechanical system sensors (the 3D-WBQM framework). An enhanced inertial odometry was developed for accurate pedestrian localization and trajectory optimization in indoor spaces containing magnetic interference and external acceleration, and Wi-Fi fine time Measurement stations, BLE nodes, and QR codes were applied for landmark detection to provide an absolute reference for trajectory optimization and crowdsourced navigation database construction. In addition, the robust unscented Kalman filter (RUKF) was applied as a generic integrated model to combine the estimated location results from inertial odometry, BLE, QR, Wi-Fi FTM, and the crowdsourced Wi-Fi fingerprinting for large-scale indoor positioning. The experimental results indicated that the proposed 3D-WBQM framework was verified to realize autonomous and accurate positioning performance in large-scale indoor areas using different location sources, and meter-level positioning accuracy can be acquired in Wi-Fi FTM supported areas. Full article
(This article belongs to the Special Issue Advanced Technologies for Position and Navigation under GNSS Signal Challenging or Denied Environments II)
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24 pages, 2011 KiB  
Article
A Single-Site Vehicle Positioning Method in the Rectangular Tunnel Environment
by Suying Jiang, Wei Wang and Peng Peng
Remote Sens. 2023, 15(2), 527; https://doi.org/10.3390/rs15020527 - 16 Jan 2023
Cited by 1 | Viewed by 1597
Abstract
Due to the satellite signals are blocked, it is difficult to obtain the vehicle position in the tunnels. We propose a single-site vehicle localization scheme for the rectangular tunnel environment, where most satellite-based positioning methods can not provide the required localization accuracy. In [...] Read more.
Due to the satellite signals are blocked, it is difficult to obtain the vehicle position in the tunnels. We propose a single-site vehicle localization scheme for the rectangular tunnel environment, where most satellite-based positioning methods can not provide the required localization accuracy. In the non-line-of-sight (NLOS) scenarios, we make use of the reflection paths as assistants for vehicle positioning. Specifically, first, the virtual stations are established based on the actual geometrical structure of the tunnel. Second, we use the direction-of-arrival (DOA) and time-of-arrival (TOA) information of reflection paths from two tunnel walls to achieve vehicle positioning. Especially, the Cramer-Rao lower bound (CRLB) of the joint TOA and DOA localization for NLOS propagations in a two-dimensional (2D) space is derived. In addition, based on the localization algorithms with and without filters, we assess the localization performance. In the line-of-sight (LOS) scenarios, we use the LOS path and two reflection paths from the tunnel walls to estimate the vehicle location. First, virtual base stations are established. Second, based on the obtained TOA information, different positioning algorithms are used to estimate the vehicle location. Simulation results illustrate that the proposed positioning approach can provide a small root mean square error. The localization algorithms using filters improve the localization accuracy, compared with the positioning algorithm without using filters, namely, the two-stage weighted least squares (TSWLS) algorithm. Moreover, the Unscented Particle Filter (UPF) algorithm achieves better positioning accuracy than other methods (i.e., Unscented Kalman Filter (UKF), Extended Kalman Filter (EKF), TSWLS algorithms). Full article
(This article belongs to the Special Issue Advanced Technologies for Position and Navigation under GNSS Signal Challenging or Denied Environments II)
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24 pages, 10388 KiB  
Article
Carrier Phase Ranging with DTMB Signals for Urban Pedestrian Localization and GNSS Aiding
by Zhenhang Jiao, Liang Chen, Xiangchen Lu, Zhaoliang Liu, Xin Zhou, Yuan Zhuang and Guangyi Guo
Remote Sens. 2023, 15(2), 423; https://doi.org/10.3390/rs15020423 - 10 Jan 2023
Cited by 12 | Viewed by 1961
Abstract
China developed its Digital Television (DTV) standard in 2006, known as Digital Television Terrestrial Multimedia Broadcasting (DTMB), which employs time-domain synchronous orthogonal frequency division multiplexing (TDS-OFDM) as the modulation method. In contrast to Global Navigation Satellite Systems (GNSSs), DTV signals have higher transmitting [...] Read more.
China developed its Digital Television (DTV) standard in 2006, known as Digital Television Terrestrial Multimedia Broadcasting (DTMB), which employs time-domain synchronous orthogonal frequency division multiplexing (TDS-OFDM) as the modulation method. In contrast to Global Navigation Satellite Systems (GNSSs), DTV signals have higher transmitting power, wider coverage, larger bandwidth, and fixed transmitter location. This paper explores the positioning performance of DTMB signals, and the potential to improve GNSS positioning accuracy in urban environments. Specifically, a solution is proposed, and a software-defined radio receiver is developed for wireless localization. Without changing the current signal structure, the pseudorandom noise (PN) sequences in the signal are used for signal acquisition and carrier phase ranging. The carrier phase of the first arrived path is extracted by the least squares matching pursuit method. Both static and dynamic field tests were conducted to verify the proposed ranging and positioning method. Centimeter-level ranging accuracy was achieved in the static scenario, while meter-level ranging accuracy was achieved in the dynamic scenario. As one possible application, the proposed ranging method was combined with GPS pseudorange measurements to achieve higher accuracy position results in an urban pedestrian scenario, especially when there is only a limited number of visible satellites. Full article
(This article belongs to the Special Issue Advanced Technologies for Position and Navigation under GNSS Signal Challenging or Denied Environments II)
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16 pages, 5492 KiB  
Article
Motion-Constrained GNSS/INS Integrated Navigation Method Based on BP Neural Network
by Ying Xu, Kun Wang, Changhui Jiang, Zeyu Li, Cheng Yang, Dun Liu and Haiping Zhang
Remote Sens. 2023, 15(1), 154; https://doi.org/10.3390/rs15010154 - 27 Dec 2022
Cited by 4 | Viewed by 1923
Abstract
The global navigation satellite system (GNSS) and inertial navigation system (INS) integrated navigation system have been widely used in Intelligent Transportation Systems (ITSs). However, the positioning error of integrated navigation systems is rapidly divergent when GNSS outages occur. Motion constraint and back propagation [...] Read more.
The global navigation satellite system (GNSS) and inertial navigation system (INS) integrated navigation system have been widely used in Intelligent Transportation Systems (ITSs). However, the positioning error of integrated navigation systems is rapidly divergent when GNSS outages occur. Motion constraint and back propagation (BP) neural networks can provide additional knowledge to solve this issue. However, the predictions of a neural network have outliers and motion constraint is difficult to adapt according to the motion states of vehicles and boats. Therefore, this paper fused a BP neural network with motion constraints, and proposed a motion-constrained GNSS/INS integrated navigation method based on a BP neural network (MC-BP method). The pseudo-measurement of the GNSS was predicted using a fitting model trained by the BP neural network. At the same time, the prediction outliers were detected and corrected using motion constraint. To assess the performance of the proposed method, simulated and real data experiments were conducted with a vehicle on land and a boat offshore. A classical GNSS/INS integration algorithm, a motion-constrained GNSS/INS algorithm, and the proposed method were compared through data processing. Compared with the classical GNSS/INS integration algorithm and the motion-constrained GNSS/INS algorithm, the positioning accuracies of the proposed method were improved by 90% and 64%, respectively, in the vehicle land experiment. Similar performances were found in the offshore boat experiment. Using the proposed MC-BP method, improved meter-level-positioning results can be achieved with the GNSS/INS integration algorithm when GNSS outages occur. Full article
(This article belongs to the Special Issue Advanced Technologies for Position and Navigation under GNSS Signal Challenging or Denied Environments II)
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19 pages, 7111 KiB  
Article
An Improved End-to-End Multi-Target Tracking Method Based on Transformer Self-Attention
by Yong Hong, Deren Li, Shupei Luo, Xin Chen, Yi Yang and Mi Wang
Remote Sens. 2022, 14(24), 6354; https://doi.org/10.3390/rs14246354 - 15 Dec 2022
Cited by 3 | Viewed by 1959
Abstract
Current multi-target multi-camera tracking algorithms demand increased requirements for re-identification accuracy and tracking reliability. This study proposed an improved end-to-end multi-target tracking algorithm that adapts to multi-view multi-scale scenes based on the self-attentive mechanism of the transformer’s encoder–decoder structure. A multi-dimensional feature extraction [...] Read more.
Current multi-target multi-camera tracking algorithms demand increased requirements for re-identification accuracy and tracking reliability. This study proposed an improved end-to-end multi-target tracking algorithm that adapts to multi-view multi-scale scenes based on the self-attentive mechanism of the transformer’s encoder–decoder structure. A multi-dimensional feature extraction backbone network was combined with a self-built raster semantic map which was stored in the encoder for correlation and generated target position encoding and multi-dimensional feature vectors. The decoder incorporated four methods: spatial clustering and semantic filtering of multi-view targets; dynamic matching of multi-dimensional features; space–time logic-based multi-target tracking, and space–time convergence network (STCN)-based parameter passing. Through the fusion of multiple decoding methods, multi-camera targets were tracked in three dimensions: temporal logic, spatial logic, and feature matching. For the MOT17 dataset, this study’s method significantly outperformed the current state-of-the-art method by 2.2% on the multiple object tracking accuracy (MOTA) metric. Furthermore, this study proposed a retrospective mechanism for the first time and adopted a reverse-order processing method to optimize the historical mislabeled targets for improving the identification F1-score (IDF1). For the self-built dataset OVIT-MOT01, the IDF1 improved from 0.948 to 0.967, and the multi-camera tracking accuracy (MCTA) improved from 0.878 to 0.909, which significantly improved the continuous tracking accuracy and reliability. Full article
(This article belongs to the Special Issue Advanced Technologies for Position and Navigation under GNSS Signal Challenging or Denied Environments II)
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27 pages, 25483 KiB  
Article
Research on UWB Indoor Positioning Algorithm under the Influence of Human Occlusion and Spatial NLOS
by Hao Zhang, Qing Wang, Chao Yan, Jiujing Xu and Bo Zhang
Remote Sens. 2022, 14(24), 6338; https://doi.org/10.3390/rs14246338 - 14 Dec 2022
Cited by 9 | Viewed by 2541
Abstract
Ultra-wideband (UWB) time-of-flight (TOF)-based ranging information in a non-line-of-sight (NLOS) environment can display significant forward errors, which directly affect positioning performance. NLOS has been a major factor limiting the improvement of UWB positioning accuracy and its application in complex scenarios. Therefore, in order [...] Read more.
Ultra-wideband (UWB) time-of-flight (TOF)-based ranging information in a non-line-of-sight (NLOS) environment can display significant forward errors, which directly affect positioning performance. NLOS has been a major factor limiting the improvement of UWB positioning accuracy and its application in complex scenarios. Therefore, in order to weaken the influence of the indoor complex environment on the NLOS environment of UWB and to further improve the performance of positioning, in this paper, we first analyze the factors and characteristics of NLOS formation in an indoor environment. The NLOS is divided into fixed NLOS influenced by spatial structure and dynamic random NLOS influenced by human occlusion. Then, the anchor LOS/NLOS information map is established by making full use of indoor spatial a priori information. On this basis, a robust adaptive extended Kalman filtering algorithm based on the anchor LOS/NLOS information map is designed, which is not only effectively able to exclude the influence of spatial NLOS, but can also optimize the random error. The proposed algorithm was validated in different experimental scenarios. The experimental results show that the positioning accuracy is better than 0.32 m in complex indoor NLOS environments. Full article
(This article belongs to the Special Issue Advanced Technologies for Position and Navigation under GNSS Signal Challenging or Denied Environments II)
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22 pages, 9749 KiB  
Article
GNSS RTK/UWB/DBA Fusion Positioning Method and Its Performance Evaluation
by Shengliang Wang, Xianshu Dong, Genyou Liu, Ming Gao, Gongwei Xiao, Wenhao Zhao and Dong Lv
Remote Sens. 2022, 14(23), 5928; https://doi.org/10.3390/rs14235928 - 23 Nov 2022
Cited by 4 | Viewed by 2102
Abstract
As a significant space–time infrastructure, the Global Navigation Satellite System (GNSS) provides high-precision positioning, navigation, and timing (PNT) information to users all over the world. However, GNSS real-time kinematic (RTK) mobile receiver signal attenuation is obvious in complex environments such as under trees, [...] Read more.
As a significant space–time infrastructure, the Global Navigation Satellite System (GNSS) provides high-precision positioning, navigation, and timing (PNT) information to users all over the world. However, GNSS real-time kinematic (RTK) mobile receiver signal attenuation is obvious in complex environments such as under trees, urban canyons, and indoors, among others, and it is incapable of meeting the demand of multi-level mass users for indoor and outdoor seamless positioning applications. The goal of this study was to address the limitations and vulnerabilities of the GNSS RTK positioning above-mentioned. First, we propose a GNSS RTK/UWB/DBA fusion positioning model and provide detailed algorithm steps for various types of observations. The performance of the GNSS RTK/UWB/DBA fusion positioning under various occlusion environments is then thoroughly evaluated using static and dynamic cart experiments. The experiment results show that as the elevation mask angle increases, the number of available GNSS satellites decreases and the ambiguity resolution success rate decreases; in comparison to GNSS RTK, the proposed GNSS RTK/UWB/DBA fusion positioning model can significantly improve the spatial geometry distribution of observations, reduce the position dilution of precision (PDOP) value, and improve the ambiguity resolution success rate. At an elevation mask angle of 50 degrees, GNSS RTK/UWB/DBA combination positioning can improve the ambiguity resolution success rate by 20% to 60%, and a positioning error less than 5 cm by 20% to 50%. It also indicates that the GNSS RTK/UWB/DBA fusion positioning model has higher positioning accuracy and can effectively improve the availability and reliability of GNSS RTK in a local harsh environment. Full article
(This article belongs to the Special Issue Advanced Technologies for Position and Navigation under GNSS Signal Challenging or Denied Environments II)
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17 pages, 5679 KiB  
Article
Combining Dilution of Precision and Kalman Filtering for UWB Positioning in a Narrow Space
by Yunjian Guo, Weihong Li, Guang Yang, Zhenhang Jiao and Jiachen Yan
Remote Sens. 2022, 14(21), 5409; https://doi.org/10.3390/rs14215409 - 28 Oct 2022
Cited by 12 | Viewed by 2036
Abstract
Affected by the spatial environment, the accuracy and stability of ultra-wideband (UWB) positioning in a narrow space are significantly lower than those in the general indoor environment, which limits navigation and positioning services in a complex scene. To improve the positioning accuracy and [...] Read more.
Affected by the spatial environment, the accuracy and stability of ultra-wideband (UWB) positioning in a narrow space are significantly lower than those in the general indoor environment, which limits navigation and positioning services in a complex scene. To improve the positioning accuracy and stability of a narrow space, this study proposed a positioning algorithm by combining Kalman filter (KF) and dilution of precision (DOP). Firstly, we calculated the DOP values of the target narrow space by changing the location of the test nodes throughout the space. Secondly, the initial coordinate values of the test nodes were calculated by the weighted least squares (WLS) positioning algorithm and were used as the observation values of KF. Finally, the DOP values were adaptively introduced into KF to update the coordinates of the nodes to be tested. The proposed algorithm was tested in two narrow scenes with different length–width ratios. The experimental results showed that the DOP values of the narrow space were much higher than that of the wide space. Furthermore, even if the ranging error was low, the positioning error was high in the narrow space. The proposed fusion positioning algorithm reported a higher positioning accuracy in the narrow space, and the higher DOP values of the scene, the greater the accuracy improvement of the algorithm. This study reveals that no matter how the base stations are configured, the DOP values of the narrow space are much higher than that of the wide space, thus causing larger positioning errors. The proposed positioning algorithm can effectively suppress the positioning error caused by the narrow spatial structure, so as to improve the positioning accuracy and stability. Full article
(This article belongs to the Special Issue Advanced Technologies for Position and Navigation under GNSS Signal Challenging or Denied Environments II)
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20 pages, 4172 KiB  
Article
Self-Calibrated Multi-Floor Localization Based on Wi-Fi Ranging/Crowdsourced Fingerprinting and Low-Cost Sensors
by Qiao Wan, Xiaoqi Duan, Yue Yu, Ruizhi Chen and Liang Chen
Remote Sens. 2022, 14(21), 5376; https://doi.org/10.3390/rs14215376 - 27 Oct 2022
Cited by 3 | Viewed by 1333
Abstract
Crowdsourced localization using geo-spatial big data has become an effective approach for constructing smart-city-based location services with the fast growing number of Internet of Things terminals. This paper presents a self-calibrated multi-floor indoor positioning framework using a combination of Wi-Fi ranging, crowdsourced fingerprinting [...] Read more.
Crowdsourced localization using geo-spatial big data has become an effective approach for constructing smart-city-based location services with the fast growing number of Internet of Things terminals. This paper presents a self-calibrated multi-floor indoor positioning framework using a combination of Wi-Fi ranging, crowdsourced fingerprinting and low-cost sensors (SM-WRFS). The localization parameters, such as heading and altitude biases, step-length scale factor, and Wi-Fi ranging bias are autonomously calibrated to provide a more accurate forward 3D localization performance. In addition, the backward smoothing algorithm and a novel deep-learning model are applied in order to construct an autonomous and efficient crowdsourced Wi-Fi fingerprinting database using the detected quick response (QR) code-based landmarks. Finally, the adaptive extended Kalman filter is adopted to combine the corresponding location sources using different integration models to provide a precise multi-source fusion based multi-floor indoor localization performance. The real-world experiments demonstrate that the presented SM-WRFS is proven to realize precise 3D indoor positioning under different environments, and the meter-level positioning accuracy can be acquired in Wi-Fi ranging supported indoor areas. Full article
(This article belongs to the Special Issue Advanced Technologies for Position and Navigation under GNSS Signal Challenging or Denied Environments II)
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Jump to: Research

15 pages, 6934 KiB  
Technical Note
Worst-Case Integrity Risk Sensitivity for RAIM with Constellation Modernization
by Liuqi Wang, Liang Li, Ruijie Li, Min Li and Li Cheng
Remote Sens. 2023, 15(12), 2979; https://doi.org/10.3390/rs15122979 - 07 Jun 2023
Viewed by 926
Abstract
The integrity improvement of receiver autonomous integrity monitoring (RAIM) can benefit from a combination of constellations. With the rapid development of constellation modernization, integrity parameters, including the probability of satellite fault (Psat) and user range accuracy (URA), have [...] Read more.
The integrity improvement of receiver autonomous integrity monitoring (RAIM) can benefit from a combination of constellations. With the rapid development of constellation modernization, integrity parameters, including the probability of satellite fault (Psat) and user range accuracy (URA), have improved. The integrity loss of RAIM needs to be accurately characterized to control the effect of the improved integrity parameters. To reveal the sensitivity of integrity risk with respect to Psat and URA, a conservative integrity risk estimation method is proposed based on the worst-case protection concept. Acceptable Psat and URA were derived by comparing the estimated worst-case integrity risk with the required integrity risk. The simulation results showed that RAIM can meet the integrity risk requirement of LPV-200 when Psat was 10−4 and URA was smaller than 0.88 m. Full article
(This article belongs to the Special Issue Advanced Technologies for Position and Navigation under GNSS Signal Challenging or Denied Environments II)
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