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GNSS Sensing and Imaging Based on Monitoring Applications
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GNSS Sensing and Imaging Based on Monitoring Applications

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensing and Imaging".

Deadline for manuscript submissions: closed (20 November 2023) | Viewed by 6881

Special Issue Editors

Prof. Dr. Feizhou Zhang

E-Mail Website
Guest Editor
Institute of Remote Sensing and Geographic Information System, School of Earth and Space Science, Peking University, Beijing 100871, China
Interests: satellite navigation; intelligent transportation systems; internet of things; smart city; intelligent processing of spatial information
Prof. Dr. Dongkai Yang

E-Mail Website
Guest Editor
School of Electronic Information Engineering, Beihang University, Beijing 100191, China
Interests: satellite navigation signal processing and application technology; wireless data transmission

Special Issue Information

Dear Colleagues,

The global navigation satellite system (GNSS) is a worldwide set of satellite navigation constellations, civil aviation augmentations, and user equipment. A global navigation satellite system is the space-based radio navigation and positioning system that can provide users with all-weather, three-dimensional coordinates, speed, and time information at any place on the Earth's surface or in near-Earth space. Satellite navigation and positioning technology has replaced ground-based radio navigation, traditional geodesy, and astronomical navigation and positioning technology and promoted the brand-new development of navigation and positioning. The GNSS is about to enter a new stage in the next few years. Abundant navigation data are able to improve satellite navigation availability, accuracy, and reliability. Still, at the same time, there are also many problems such as frequency resource competition, satellite navigation market competition, time and frequency dominance competition, and compatibility and interoperability debates.

The purpose of this topic is to further investigate global navigation satellite systems. Topics may include, but are not limited to, GNSS, GNSS positioning technology, GNSS-R technology, GNSS occultation (of stars) technology, integrated navigation technology, multi-frequency, multi-system joint positioning technology, etc.

Prof. Dr. Feizhou Zhang
Prof. Dr. Dongkai Yang
Guest Editors

Manuscript Submission Information

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Keywords

  • satellite navigation system
  • global positioning system
  • aircraft navigation
  • surveillance
  • alarm system
  • artificial satellite
  • satellite constellation
  • computerized monitoring

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Published Papers (4 papers)

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Research

11 pages, 2901 KiB  
Article
Development of a Contact Glass-Break Detector for the Highest Security Level
by Vaclav Mach, Ales Mizera, Pavel Stoklasek, Michaela Karhankova, Milan Adamek and Martin Bednarik
Sensors 2024, 24(1), 97; https://doi.org/10.3390/s24010097 - 24 Dec 2023
Viewed by 1628
Abstract
The main object of this research was to develop a security system to evaluate the intrusion into an object through a glass pane. More specifically, this study deals with sensing and evaluating signals from a contact glass-break detector, which is part of an [...] Read more.
The main object of this research was to develop a security system to evaluate the intrusion into an object through a glass pane. More specifically, this study deals with sensing and evaluating signals from a contact glass-break detector, which is part of an intruder alarm system. Each alarm detector in an alarm system must accomplish certain security level requirements that strictly describe the requirements for the area of use and the detector’s reliability. To date, no contact glass-break detector has been developed and fully tested to meet the stringent requirements of the highest security level. A contact glass-break detector was developed whose main part is an accelerometer that transmits signals from the glass pane. These signals were evaluated according to the developed methodology. It was verified that the proposed system can distinguish at the highest security level between false alarms and situations where the building has been intruded. Full article
(This article belongs to the Special Issue GNSS Sensing and Imaging Based on Monitoring Applications)
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25 pages, 6214 KiB  
Article
Research on Soil Moisture Estimation of Multiple-Track-GNSS Dual-Frequency Combination Observations Considering the Detection and Correction of Phase Outliers
by Xudong Zhang, Chao Ren, Yueji Liang, Jieyu Liang, Anchao Yin and Zhenkui Wei
Sensors 2023, 23(18), 7944; https://doi.org/10.3390/s23187944 - 17 Sep 2023
Viewed by 1356
Abstract
Soil moisture (SM), as one of the crucial environmental factors, has traditionally been estimated using global navigation satellite system interferometric reflectometry (GNSS-IR) microwave remote sensing technology. This approach relies on the signal-to-noise ratio (SNR) reflection component, and its accuracy hinges on the successful [...] Read more.
Soil moisture (SM), as one of the crucial environmental factors, has traditionally been estimated using global navigation satellite system interferometric reflectometry (GNSS-IR) microwave remote sensing technology. This approach relies on the signal-to-noise ratio (SNR) reflection component, and its accuracy hinges on the successful separation of the reflection component from the direct component. In contrast, the presence of carrier phase and pseudorange multipath errors enables soil moisture retrieval without the requirement for separating the direct component of the signal. To acquire high-quality combined multipath errors and diversify GNSS-IR data sources, this study establishes the dual-frequency pseudorange combination (DFPC) and dual-frequency carrier phase combination (L4) that exclude geometrical factors, ionospheric delay, and tropospheric delay. Simultaneously, we propose two methods for estimating soil moisture: the DFPC method and the L4 method. Initially, the equal-weight least squares method is employed to calculate the initial delay phase. Subsequently, anomalous delay phases are detected and corrected through a combination of the minimum covariance determinant robust estimation (MCD) and the moving average filter (MAF). Finally, we utilize the multivariate linear regression (MLR) and extreme learning machine (ELM) to construct multi-satellite linear regression models (MSLRs) and multi-satellite nonlinear regression models (MSNRs) for soil moisture prediction, and compare the accuracy of each model. To validate the feasibility of these methods, data from site P031 of the Plate Boundary Observatory (PBO) H2O project are utilized. Experimental results demonstrate that combining MCD and MAF can effectively detect and correct outliers, yielding single-satellite delay phase sequences with a high quality. This improvement contributes to varying degrees of enhanced correlation between the single-satellite delay phase and soil moisture. When fusing the corrected delay phases from multiple satellite orbits using the DFPC method for soil moisture estimation, the correlations between the true soil moisture values and the predicted values obtained through MLR and ELM reach 0.81 and 0.88, respectively, while the correlations of the L4 method can reach 0.84 and 0.90, respectively. These findings indicate a substantial achievement in high-precision soil moisture estimation within a small satellite-elevation angle range. Full article
(This article belongs to the Special Issue GNSS Sensing and Imaging Based on Monitoring Applications)
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20 pages, 9086 KiB  
Article
Sea-Level Estimation from GNSS-IR under Loose Constraints Based on Local Mean Decomposition
by Zhenkui Wei, Chao Ren, Xingyong Liang, Yueji Liang, Anchao Yin, Jieyu Liang and Weiting Yue
Sensors 2023, 23(14), 6540; https://doi.org/10.3390/s23146540 - 20 Jul 2023
Cited by 1 | Viewed by 1367
Abstract
The global navigation satellite system–interferometric reflectometry (GNSS-IR) technique has emerged as an effective coastal sea-level monitoring solution. However, the accuracy and stability of GNSS-IR sea-level estimation based on quadratic fitting are limited by the retrieval range of reflector height (RH range) and satellite-elevation [...] Read more.
The global navigation satellite system–interferometric reflectometry (GNSS-IR) technique has emerged as an effective coastal sea-level monitoring solution. However, the accuracy and stability of GNSS-IR sea-level estimation based on quadratic fitting are limited by the retrieval range of reflector height (RH range) and satellite-elevation range, reducing the flexibility of this technology. This study introduces a new GNSS-IR sea-level estimation model that combines local mean decomposition (LMD) and Lomb–Scargle periodogram (LSP). LMD can decompose the signal-to-noise ratio (SNR) arc into a series of signal components with different frequencies. The signal components containing information from the sea surface are selected to construct the oscillation term, and its frequency is extracted by LSP. To this end, observational data from SC02 sites in the United States are used to evaluate the accuracy level of the model. Then, the performance of LMD and the influence of noise on retrieval results are analyzed from two aspects: RH ranges and satellite-elevation ranges. Finally, the sea-level variation for one consecutive year is estimated to verify the stability of the model in long-term monitoring. The results show that the oscillation term obtained by LMD has a lower noise level than other signal separation methods, effectively improving the accuracy of retrieval results and avoiding abnormal values. Moreover, it still performs well under loose constraints (a wide RH range and a high-elevation range). In one consecutive year of retrieval results, the new model based on LMD has a significant improvement effect over quadratic fitting, and the root mean square error and mean absolute error of retrieval results obtained in each month on average are improved by 8.34% and 8.87%, respectively. Full article
(This article belongs to the Special Issue GNSS Sensing and Imaging Based on Monitoring Applications)
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13 pages, 6617 KiB  
Article
Multilayer Model in Soil Moisture Content Retrieval Using GNSS Interferometric Reflectometry
by Jie Li, Xuebao Hong, Feng Wang, Lei Yang and Dongkai Yang
Sensors 2023, 23(4), 1949; https://doi.org/10.3390/s23041949 - 9 Feb 2023
Cited by 2 | Viewed by 1873
Abstract
The global navigation satellite system–interferometric reflectometry (GNSS-IR) was developed more than a decade ago to monitor soil moisture content (SMC); a system that is essentially finished has emerged. The standard GNSS-IR model typically considers soil to be a single layer of medium and [...] Read more.
The global navigation satellite system–interferometric reflectometry (GNSS-IR) was developed more than a decade ago to monitor soil moisture content (SMC); a system that is essentially finished has emerged. The standard GNSS-IR model typically considers soil to be a single layer of medium and measures the average SMC between 1 and 10 cm below the soil surface. The majority of the SMC is not distributed uniformly along the longitudinal axis. This study is based on a simulation platform and suggests a SMC-stratified measurement model that can be used to recover the SMC at different depths in the sink and reverse osmosis to address the issue that conventional techniques cannot accurately measure soil moisture at different depths. The soil moisture of each layer was assessed by utilizing the GNSS signals reflected by various soil layers, and this study employed total transmission when the vertical linearly polarized component of the electromagnetic wave was conveyed by the GNSS signal reflected by the soil. This work employed the Hilbert transform to obtain the interference signal envelope, which increases the visibility of the interference signal’s “notch” and reduces the burr impact of the interference signal brought on by ambient noise. The accuracy of the SMC measurement at the bottom declines due to the soil’s attenuation of the GNSS signal power, but the correlation between the predetermined value and SMC retrieved by the GNSS-IR multilayer SMC measurement model similarly approached 0.92. Full article
(This article belongs to the Special Issue GNSS Sensing and Imaging Based on Monitoring Applications)
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