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Signal Processing for Satellite Navigation and Wireless Localization
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Signal Processing for Satellite Navigation and Wireless Localization

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

Deadline for manuscript submissions: 30 November 2025 | Viewed by 2513

Special Issue Editors

Dr. Xin Chen

E-Mail Website
Guest Editor
School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Interests: satellite positioning ; multi-sensor fusion positioning; satellite spoofing signal detection and source localization; modern navigation terminal testing system
Dr. Xinhua Tang

E-Mail Website
Guest Editor
School of Instrument Science and Engineering, Southeast University, Nanjing 211189, China
Interests: GNSS receiver techniques;GNSS/INS integrated navigation system; lidar/vision/IMU/odometry/GNSS integration system; the application of artificial intelligence in navigation area; autonomous systems

Special Issue Information

Dear Colleagues,

Positioning and navigation are critical for location-based applications. While Global Navigation Satellite Systems (GNSSs) have shown great success in many areas, their performance is lower in challenging environments, such as in urban, indoor, or interference scenarios, a problem which current researchers are endeavoring to overcome using new algorithms. Positioning can also be achieved via radio-based positioning by celluar, WiFi, DTV, or other signals of opportunities. Therefore, this Special Issue aims to bring the lastest advances in signal processing research for satellite navigation and wireless localization. We welcome submissions on methodological or technical aspects focused on (but not limited to) the following topics:

  • New GNSS algorithms and receiver design technologies;
  • Measurement error modeling and mitigation;
  • Interference (jamming, meaconing, and spoofing) coutermeasures;
  • Localization via wireless networks;
  • Localization via signals of opportunities;
  • Artificial intelligence in localization and navigation;
  • Indoor localization and navigation systems;
  • Multi-source cooperative localization and navigation;
  • Integration of LEO-PNT with GNSS;
  • LEO-PNT as augmentation for GNSS. 

Dr. Xin Chen
Dr. Xinhua Tang
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. Sensors 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 2600 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

  • positioning and navigation
  • global navigation satellite systems (GNSS)
  • signal processing
  • satellite navigation
  • wireless localization
  • LEO-PNT

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Published Papers (2 papers)

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Research

26 pages, 8427 KiB  
Article
Solving Integer Ambiguity Based on an Improved Ant Lion Algorithm
by Wuzheng Guo, Yuanfa Ji, Xiyan Sun and Xizi Jia
Sensors 2025, 25(4), 1212; https://doi.org/10.3390/s25041212 - 17 Feb 2025
Viewed by 503
Abstract
In GNSS, a double-difference carrier phase observation model is typically employed, and high-accuracy position coordinates can be obtained by resolving the integer ambiguity within the model through algorithmic processing. To address the challenge of a double-difference integer ambiguity resolution, an enhanced Simulated Annealing [...] Read more.
In GNSS, a double-difference carrier phase observation model is typically employed, and high-accuracy position coordinates can be obtained by resolving the integer ambiguity within the model through algorithmic processing. To address the challenge of a double-difference integer ambiguity resolution, an enhanced Simulated Annealing Ant Lion Optimizer (SAALO) is proposed. This algorithm is designed to efficiently resolve integer ambiguities. First, the performance of the SAALO algorithm was evaluated by comparing its solving speed and success rate with those of the Ant Lion Optimization Algorithm (ALO), the LAMBDA algorithm and the MLAMBDA algorithm. The results demonstrate that the SAALO algorithm achieved a solution success rate that was 0.0496 s and 0.01 s faster than the LAMBDA and M-LAMBDA algorithms, respectively. Second, to further validate the high-dimensional ambiguity resolution capability of the SAALO algorithm, integer ambiguity resolution tests were conducted in both 6-dimensional and 12-dimensional scenarios. The results indicate that the SAALO algorithm achieves a success rate exceeding 98%, confirming its robust performance in high-dimensional problem-solving. Finally, the practical application of the SAALO algorithm was tested in short- and medium-baseline scenarios using a single-frequency GPS system. With a baseline length of 42.7 km, the SAALO algorithm exhibited a slightly faster average solution time compared to the LAMBDA algorithm, while its solution success rate was 5.2% higher. These findings underscore the effectiveness and reliability of the SAALO algorithm in real-world GNSS applications. Full article
(This article belongs to the Special Issue Signal Processing for Satellite Navigation and Wireless Localization)
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39 pages, 11124 KiB  
Article
XAI GNSS—A Comprehensive Study on Signal Quality Assessment of GNSS Disruptions Using Explainable AI Technique
by Arul Elango and Rene Jr. Landry
Sensors 2024, 24(24), 8039; https://doi.org/10.3390/s24248039 - 17 Dec 2024
Cited by 2 | Viewed by 1622
Abstract
The hindering of Global Navigation Satellite Systems (GNSS) signal reception by jamming and spoofing attacks degrades the signal quality. Careful attention needs to be paid when post-processing the signal under these circumstances before feeding the signal into the GNSS receiver’s post-processing stage. The [...] Read more.
The hindering of Global Navigation Satellite Systems (GNSS) signal reception by jamming and spoofing attacks degrades the signal quality. Careful attention needs to be paid when post-processing the signal under these circumstances before feeding the signal into the GNSS receiver’s post-processing stage. The identification of the time domain statistical attributes and the spectral domain characteristics play a vital role in analyzing the behaviour of the signal characteristics under various kinds of jamming attacks, spoofing attacks, and multipath scenarios. In this paper, the signal records of five disruptions (pure, continuous wave interference (CWI), multi-tone continuous wave interference (MCWI), multipath (MP), spoofing, pulse, and chirp) are examined, and the most influential features in both the time and frequency domains are identified with the help of explainable AI (XAI) models. Different Machine learning (ML) techniques were employed to assess the importance of the features to the model’s prediction. From the statistical analysis, it has been observed that the usage of the SHapley Additive exPlanations (SHAP) and local interpretable model-agnostic explanations (LIME) models in GNSS signals to test the types of disruption in unknown GNSS signals, using only the best-correlated and most important features in the training phase, provided a better classification accuracy in signal prediction compared to traditional feature selection methods. This XAI model reveals the black-box ML model’s output prediction and provides a clear explanation of the specific signal occurrences based on the individual feature contributions. By using this black-box revealer, we can easily analyze the behaviour of the GNSS ground-station signals and employ fault detection and resilience diagnosis in GNSS post-processing. Full article
(This article belongs to the Special Issue Signal Processing for Satellite Navigation and Wireless Localization)
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