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Navigation Positioning, Low Orbit Satellites’ Signal of Opportunity and Remote...
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Navigation Positioning, Low Orbit Satellites’ Signal of Opportunity and Remote Sensing Applications: Cross Fusion and Innovative Development

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Microwave and Wireless Communications".

Deadline for manuscript submissions: 15 August 2025 | Viewed by 4360

Special Issue Editors

Dr. Jiajia Chen

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Guest Editor
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Interests: navigation positioning; spoofing detection; signal and information processing
Dr. Ying Xu

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Guest Editor
Aerospace Information Research Institute, Chinese Academy of Sciences, No.9 Dengzhuang South Road, Haidian District, Beijing 100094, China
Interests: navigation positioning; signal enhancement technology
Dr. Ming Gao

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Guest Editor
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Interests: remote sensing; geodesy and surveying

Special Issue Information

Dear Colleagues,

This Special Issue, "Navigation Positioning, Low Orbit Satellites’ Signal of Opportunity and Remote Sensing Applications: Cross Fusion and Innovative Development", aims to explore the intersection of navigation technology, low orbit satellite signals, and remote sensing applications. It focuses on advancements in the cross-fusion of these technologies, promoting innovative solutions for positioning accuracy, signal utilization, and remote sensing capabilities. The issue covers a range of topics, including but not limited to improved positioning algorithms, novel uses of low-orbit satellite signals, and remote sensing techniques that leverage these technologies. By bridging these fields, we seek to showcase cutting-edge research and foster the development of integrated systems that enhance navigation, imaging, and data acquisition for various applications. This Special Issue welcomes contributions demonstrating innovative practices, theoretical frameworks, and case studies highlighting the synergies between navigation positioning, low-orbit satellites, and remote sensing.

Dr. Jiajia Chen
Dr. Ying Xu
Dr. Ming Gao
Guest Editors

Manuscript Submission Information

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Keywords

  • satellite navigation positioning
  • low orbit satellites’ signal of opportunity
  • remote sensing applications
  • integrated communication and remote control
  • deep learning technology
  • satellite signal enhancement technology
  • navigation and positioning technology in complex scenarios
  • muti-information fusion method
  • collaborative navigation

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Published Papers (6 papers)

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Research

21 pages, 7328 KiB  
Article
Backpropagation Neural Network-Assisted Helmert Variance Model for Weighted Global Navigation Satellite System Localization in High Orbit
by Zhipu Wang, Xialan Chen, Zimin Huo, Zhibo Fang and Zhenting Xu
Electronics 2025, 14(8), 1529; https://doi.org/10.3390/electronics14081529 - 10 Apr 2025
Viewed by 161
Abstract
In high-orbit space missions, the significant attenuation of Global Navigation Satellite System (GNSS) signals due to long transmission distances and complex environmental interferences has led to a drastic degradation in the accuracy of traditional positioning models, which has attracted great attention in recent [...] Read more.
In high-orbit space missions, the significant attenuation of Global Navigation Satellite System (GNSS) signals due to long transmission distances and complex environmental interferences has led to a drastic degradation in the accuracy of traditional positioning models, which has attracted great attention in recent years. Although multi-system GNSS fusion positioning can alleviate the problem of insufficient satellite visibility, the existing methods are difficult to effectively cope with the challenges of multi-source noise coupling and inter-system error differences unique to high orbit. In this paper, we propose an adaptive GNSS positioning optimization framework for a high-orbit environment, which improves the orbiting reliability under complex signal conditions through dynamic weight allocation and a multi-system cooperative strategy. Different from the traditional weighting model, this method innovatively constructs a two-layer optimization mechanism: (1) Based on BP neural network, it evaluates the noise characteristics of pseudo-range observations in real time and realizes the adaptive suppression of receiver thermal noise, ionospheric delay, etc.; (2) it introduces Helmert variance component estimation to optimize the weighting ratio of GPS, GLONASS, BeiDou, and Galileo and reduces the impact of signal heterogeneity on the positioning solution of the multi-system. Simulation results show that the new method reduces the root-mean-square error of positioning by 32.8% compared with the traditional algorithm to 97.72 m in typical high-orbit scenarios and significantly improves the accuracy loss caused by the defective satellite geometrical configurations under multi-system synergy. Full article
(This article belongs to the Special Issue Navigation Positioning, Low Orbit Satellites’ Signal of Opportunity and Remote Sensing Applications: Cross Fusion and Innovative Development)
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19 pages, 3780 KiB  
Article
Local Batch Normalization-Aided CNN Model for RSSI-Based Fingerprint Indoor Positioning
by Houjin Lu, Shuzhi Liu and Seung-Hoon Hwang
Electronics 2025, 14(6), 1136; https://doi.org/10.3390/electronics14061136 - 13 Mar 2025
Cited by 1 | Viewed by 600
Abstract
Indoor positioning systems have become increasingly important due to the limitations of GPS in indoor environments, such as non-line-of-sight conditions and weak signal strength. Among the various indoor positioning techniques, fingerprinting-based approaches utilizing WiFi signals are highly regarded for their accessibility and convenience. [...] Read more.
Indoor positioning systems have become increasingly important due to the limitations of GPS in indoor environments, such as non-line-of-sight conditions and weak signal strength. Among the various indoor positioning techniques, fingerprinting-based approaches utilizing WiFi signals are highly regarded for their accessibility and convenience. However, existing convolutional neural network (CNN) models for fingerprinting often struggle to maintain consistent performance under diverse environmental conditions. To address these challenges, this study proposes a local batch normalization (LBN)-aided CNN model for received signal strength indicator (RSSI)-based indoor positioning. The LBN technique is designed to overcome the limitations of traditional batch normalization (BN) and layer normalization (LN) in managing location-dependent RSSI variations, thereby improving positioning accuracy. The proposed approach consists of two phases: an offline phase, where RSSI data are collected at reference points to train the model, and an online phase, where real-time RSSI data are used to estimate the device’s location. Experimental results demonstrate that the proposed LBN-aided CNN model achieves an accuracy of 92.9%, outperforming existing CNN-based methods. These findings confirm the effectiveness of LBN in enhancing CNN performance for indoor positioning, particularly in challenging environments with significant signal variability. Full article
(This article belongs to the Special Issue Navigation Positioning, Low Orbit Satellites’ Signal of Opportunity and Remote Sensing Applications: Cross Fusion and Innovative Development)
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22 pages, 5549 KiB  
Article
A Proposal of In Situ Authoring Tool with Visual-Inertial Sensor Fusion for Outdoor Location-Based Augmented Reality
by Komang Candra Brata, Nobuo Funabiki, Yohanes Yohanie Fridelin Panduman, Mustika Mentari, Yan Watequlis Syaifudin and Alfiandi Aulia Rahmadani
Electronics 2025, 14(2), 342; https://doi.org/10.3390/electronics14020342 - 17 Jan 2025
Viewed by 946
Abstract
In location-based augmented reality (LAR) applications, a simple and effective authoring tool is essential to create immersive AR experiences in real-world contexts. Unfortunately, most of the current tools are primarily desktop-based, requiring manual location acquisitions, the use of software development kits (SDKs), [...] Read more.
In location-based augmented reality (LAR) applications, a simple and effective authoring tool is essential to create immersive AR experiences in real-world contexts. Unfortunately, most of the current tools are primarily desktop-based, requiring manual location acquisitions, the use of software development kits (SDKs), and high programming skills, which poses significant challenges for novice developers and a lack of precise LAR content alignment. In this paper, we propose an intuitive in situ authoring tool with visual-inertial sensor fusions to simplify the LAR content creation and storing process directly using a smartphone at the point of interest (POI) location. The tool localizes the user’s position using smartphone sensors and maps it with the captured smartphone movement and the surrounding environment data in real-time. Thus, the AR developer can place a virtual object on-site intuitively without complex programming. By leveraging the combined capabilities of Visual Simultaneous Localization and Mapping(VSLAM) and Google Street View (GSV), it enhances localization and mapping accuracy during AR object creation. For evaluations, we conducted extensive user testing with 15 participants, assessing the task success rate and completion time of the tool in practical pedestrian navigation scenarios. The Handheld Augmented Reality Usability Scale (HARUS) was used to evaluate overall user satisfaction. The results showed that all the participants successfully completed the tasks, taking 16.76 s on average to create one AR object in a 50 m radius area, while common desktop-based methods in the literature need 1–8 min on average, depending on the user’s expertise. Usability scores reached 89.44 for manipulability and 85.14 for comprehensibility, demonstrating the high effectiveness in simplifying the outdoor LAR content creation process. Full article
(This article belongs to the Special Issue Navigation Positioning, Low Orbit Satellites’ Signal of Opportunity and Remote Sensing Applications: Cross Fusion and Innovative Development)
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18 pages, 7420 KiB  
Article
LEO-SOP Differential Doppler/INS Tight Integration Method Under Weak Observability
by Lelong Zhao, Ming Lei, Yue Liu, Yiwei Wang, Jian Ge, Xinnian Guo and Zhibo Fang
Electronics 2025, 14(2), 250; https://doi.org/10.3390/electronics14020250 - 9 Jan 2025
Viewed by 692
Abstract
The utilization of low Earth orbit (LEO) satellites’ signals of opportunity (SOPs) for absolute positioning and navigation in global navigation satellite system (GNSS)-denied environments has emerged as a significant area of research. Among various methodologies, tightly integrated Doppler/inertial navigation system (INS) frameworks present [...] Read more.
The utilization of low Earth orbit (LEO) satellites’ signals of opportunity (SOPs) for absolute positioning and navigation in global navigation satellite system (GNSS)-denied environments has emerged as a significant area of research. Among various methodologies, tightly integrated Doppler/inertial navigation system (INS) frameworks present a promising solution for achieving real-time LEO-SOP-based positioning in dynamic scenarios. However, existing integration schemes generally overlook the key characteristics of LEO opportunity signals, including the limited number of visible satellites and the random nature of signal broadcasts. These factors exacerbate the weak observability inherent in LEO-SoOP Doppler/INS positioning, resulting in difficulty in obtaining reliable solutions and degraded positioning accuracy. To address these issues, this paper proposes a novel LEO-SOP Doppler/INS tight integration method that incorporates trending information to alleviate the problem of weak observability. The method leverages a parallel filtering structure combining extended Kalman filter (EKF) and Rauch–Tung–Striebel (RTS) smoothing, extracting trend information from the quasi-real-time high-precision RTS filtering results to optimize the EKF positioning solution for the current epoch. This approach effectively avoids the overfitting problem commonly associated with directly using batch data to estimate the current epoch state. The experimental results validate the improved positioning accuracy and robustness of the proposed method. Full article
(This article belongs to the Special Issue Navigation Positioning, Low Orbit Satellites’ Signal of Opportunity and Remote Sensing Applications: Cross Fusion and Innovative Development)
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18 pages, 4369 KiB  
Article
A Novel Doppler Estimation Approach Using ORBCOMM Signals for High-Precision Positioning
by Mingchao Yang, Yiwei Wang, Zhibo Fang, Jiajia Chen, Yue Liu, Ming Lei and Ying Xu
Electronics 2024, 13(24), 4882; https://doi.org/10.3390/electronics13244882 - 11 Dec 2024
Cited by 1 | Viewed by 831
Abstract
Positioning based on Low Earth Orbit (LEO) satellite Signals of Opportunity (SOP) often relies on Doppler observations. Therefore, the accuracy of Doppler frequency measurements significantly impacts the positioning performance. Traditional frequency estimation methods for ORBCOMM satellite signals are typically implemented in the frequency [...] Read more.
Positioning based on Low Earth Orbit (LEO) satellite Signals of Opportunity (SOP) often relies on Doppler observations. Therefore, the accuracy of Doppler frequency measurements significantly impacts the positioning performance. Traditional frequency estimation methods for ORBCOMM satellite signals are typically implemented in the frequency domain and neglect the impact of the “frequency chirp” effect on measurement accuracy, which leads to low computational efficiency, poor noise resistance, and limited estimation accuracy. To address this issue, a high-precision frequency estimation method combining a “coarse and fine” process is proposed. In the coarse estimation process, ephemeris prior information is combined with matched filtering to effectively separate the Doppler rate, thereby mitigating the spectral broadening caused by the Doppler rate. In the fine estimation process, ORBCOMM signal characteristics are fully exploited. Single-sideband filtering is applied to improve noise resistance, followed by precise frequency discrimination of the delayed signal. Experimental results demonstrate that the proposed method outperforms the state-of-the-art “FFT + MLE” approach, achieving a frequency measurement accuracy on the order of 0.01 Hz while requiring fewer computational resources. Furthermore, this method improves estimation performance by approximately 12 dB without compromising frequency measurement accuracy. Full article
(This article belongs to the Special Issue Navigation Positioning, Low Orbit Satellites’ Signal of Opportunity and Remote Sensing Applications: Cross Fusion and Innovative Development)
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17 pages, 7428 KiB  
Article
Selected Aspects of Positioning with the GNSS Galileo
by Milan Džunda, Sebastián Čikovský and Lucia Melníková
Electronics 2024, 13(23), 4769; https://doi.org/10.3390/electronics13234769 - 2 Dec 2024
Viewed by 572
Abstract
The quality of Galileo system services is affected by the accuracy of distance determination from the user’s application to the individual satellite. The goal of our research was to find out what influence the accuracy of distance determination between the user’s application of [...] Read more.
The quality of Galileo system services is affected by the accuracy of distance determination from the user’s application to the individual satellite. The goal of our research was to find out what influence the accuracy of distance determination between the user’s application of the Galileo system and the cooperating satellites of the Galileo system has on the ability to determine the location of the user’s application. A solution based on Groebner’s algebraic approaches was used to determine the receiver user’s position. When creating distance measurement error models between the Galileo system user’s receiver and cooperating satellites, we assumed that the values of those errors considered all factors that affected the accuracy of those distance measurements. To evaluate the algorithms, we used a statistical set of 500 simulation results to determine the positioning of the user’s application of the Galileo system. If the distances between the user’s application and the individual satellite were measured accurately, then the user’s application coordinate errors had values between 1.86 × 10−9 m and −1.8 × 10−8 m. These errors should be equal to zero. The positioning error was caused by a numerical error in the calculation due to the software used. If the errors of distance determination from the user’s application to the individual satellite varied from −0.05 m to 0.09 m, then the error in determining the positioning of the user’s application of the Galileo system was from 0.0 m to 1.2 m. If the distances of the user’s receiver to the satellites were measured with errors greater than 0.09 m, the errors in determining its position were much larger. The simulation results confirmed the known fact that the satellites’ geometry influences the accuracy of determining the location of the user’s application. In the following research, we will solve the problem of how to reduce the sensitivity of the mentioned algorithms when determining the position of the satellite navigation system receiver due to errors in determining the distance from the user’s application to the individual satellite. Full article
(This article belongs to the Special Issue Navigation Positioning, Low Orbit Satellites’ Signal of Opportunity and Remote Sensing Applications: Cross Fusion and Innovative Development)
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