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Advances in Signal Processing for GNSS and Complementary PNT Technologies

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

Deadline for manuscript submissions: 30 June 2024 | Viewed by 11513

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Special Issue Editors

Dr. Beatrice Motella

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Guest Editor

Joint Research Centre European Commission, Ispra, Italy
Interests: signal processing for radio navigation receivers; GNSS signal authentication

Dr. Alex Minetto

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Guest Editor

Department of Electronics and Telecommunications (DET), Politecnico di Torino, 10129 Turin, Italy
Interests: navigation signal design and processing; advanced Bayesian estimation applied to Positioning and Navigation Technologies (PNT); applied Global Navigation Satellite System (GNSS)
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Special Issue Information

Dear Colleagues,

Many infrastructures, applications, and services that shape our lifestyle depend on the Global Navigation Satellite System (GNSS) and positioning, navigation, and timing (PNT) technologies. Even though GNSS technology has become pervasive and is widespread in our systems, several challenges still prevent robust navigation, especially in constrained environments or in the presence of interference. Users increasingly depend on GNSS-derived positions and timing, while demanding better accuracy, stringent integrity, and longer availability. To cope with these demands, auxiliary technologies such as the signal of opportunities (SoP) and the fusion of complementary low-earth orbit (LEO) PNT signals are showing innovative approaches and solutions worldwide. This Special Issue aims to present advances in GNSS signal processing, including algorithms for fast acquisition and robust tracking in harsh environments; algorithms for interference monitoring; new algorithms for GNSS/INS/visual sensors data fusion; hybridization of GNSS-based systems with terrestrial signals of opportunities, such as 5G. We welcome the submission of both preliminary and structured results of theoretical or experimental analyses, studies on the use of authenticated civilian signals to prevent spoofing, and pioneering studies of LEO signals proposed to augment positioning and navigation.

Dr. Beatrice Motella
Dr. Alex Minetto
Guest Editors

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Keywords

GNSS signal processing
complementary positioning navigation and timing (PNT) technologies
signal of opportunities (SoP)
low-earth orbit (LEO) PNT
robust tracking
interference monitoring
authenticated civilian signals

Published Papers (10 papers)

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Research

21 pages, 4079 KiB

Open AccessArticle

An Improved RAIM Availability Assessment Method Based on the Characteristic Slope

by Jing Zhao, Dan Song and Jitao Wang

Sensors 2024, 24(11), 3283; https://doi.org/10.3390/s24113283 - 21 May 2024

Viewed by 253

Abstract

The availability assessment is an important step for onboard application in Receiver Autonomous Integrity Monitoring (RAIM)s. It is commonly implemented using the protection level (PL)-based method. This paper analyzes the deficiencies of three kinds of PL-based methods: RAIM availability might be optimistically or conservatively assessed using the classic-PL-base method; might be conservatively assessed using the enhanced-PL-based method, and neither be optimistically nor conservatively assessed using the ideal-PL-based method with the cost of large calculation amount on-board. An improved slope-based RAIM availability assessment method is proposed, in which the characteristic slope is designed as the assessment basis, and its threshold that can exactly match the integrity risk requirement is derived. The slope-based method has the same RAIM availability assessment result as the ideal-PL-based method. Moreover, because the slope threshold can be calculated offline and searched online, the on-board calculation burden can be reduced using the slope-based method. Simulation is presented to verify the theoretical analysis of the RAIM availability assessment performances for the three PL-based and the slope-based methods. Full article

(This article belongs to the Special Issue Advances in Signal Processing for GNSS and Complementary PNT Technologies)

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28 pages, 3045 KiB

Open AccessArticle

LJCD-Net: Cross-Domain Jamming Generalization Diagnostic Network Based on Deep Adversarial Transfer

by Zhichao Zhang, Zhongliang Deng, Jingrong Liu, Zhenke Ding and Bingxun Liu

Sensors 2024, 24(11), 3266; https://doi.org/10.3390/s24113266 - 21 May 2024

Viewed by 247

Abstract

Global Navigation Satellite Systems (GNSS) offer comprehensive position, navigation, and timing (PNT) estimates worldwide. Given the growing demand for reliable location awareness in both indoor and outdoor contexts, the advent of fifth-generation mobile communication technology (5G) has enabled expansive coverage and precise positioning services. However, the power received by the signal of interest (SOI) at terminals is notably low. This can lead to significant jamming, whether intentional or unintentional, which can adversely affect positioning receivers. The diagnosis of jamming types, such as classification, assists receivers in spectrum sensing and choosing effective mitigation strategies. Traditional jamming diagnosis methodologies predominantly depend on the expertise of classification experts, often demonstrating a lack of adaptability for diverse tasks. Recently, researchers have begun utilizing convolutional neural networks to re-conceptualize a jamming diagnosis as an image classification issue, thereby augmenting recognition performance. However, in real-world scenarios, the assumptions of independent and homogeneous distributions are frequently violated. This discrepancy between the source and target distributions frequently leads to subpar model performance on the test set or an inability to procure usable evaluation samples during training. In this paper, we introduce LJCD-Net, a deep adversarial migration-based cross-domain jamming generalization diagnostic network. LJCD-Net capitalizes on a fully labeled source domain and multiple unlabeled auxiliary domains to generate shared feature representations with generalization capabilities. Initially, our paper proposes an uncertainty-guided auxiliary domain labeling weighting strategy, which estimates the multi-domain sample uncertainty to re-weight the classification loss and specify the gradient optimization direction. Subsequently, from a probabilistic distribution standpoint, the spatial constraint imposed on the cross-domain global jamming time-frequency feature distribution facilitates the optimization of collaborative objectives. These objectives include minimizing both the source domain classification loss and auxiliary domain classification loss, as well as optimizing the inter-domain marginal probability and conditional probability distribution. Experimental results demonstrate that LJCD-Net enhances the recognition accuracy and confidence compared to five other diagnostic methods. Full article

(This article belongs to the Special Issue Advances in Signal Processing for GNSS and Complementary PNT Technologies)

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20 pages, 3808 KiB

Open AccessArticle

A Post-Processing Multipath/NLoS Bias Estimation Method Based on DBSCAN

by Yihan Guo, Simone Zocca, Paolo Dabove and Fabio Dovis

Sensors 2024, 24(8), 2611; https://doi.org/10.3390/s24082611 - 19 Apr 2024

Viewed by 433

Abstract

Positioning based on Global Navigation Satellite Systems (GNSSs) in urban environments always suffers from multipath and Non-Line-of-Sight (NLoS) effects. In such conditions, the GNSS pseudorange measurements can be affected by biases disrupting the GNSS-based applications. Many efforts have been devoted to detecting and mitigating the effects of multipath/NLoS, but the identification and classification of such events are still challenging. This research proposes a method for the post-processing estimation of pseudorange biases resulting from multipath/NLoS effects. Providing estimated pseudorange biases due to multipath/NLoS effects serves two main purposes. Firstly, machine learning-based techniques can leverage accurately estimated pseudorange biases as training data to detect and mitigate multipath/NLoS effects. Secondly, these accurately estimated pseudorange biases can serve as a benchmark for evaluating the effectiveness of the methods proposed to detect multipath/NLoS effects. The estimation is achieved by extracting the multipath/NLoS biases from pseudoranges using a clustering algorithm named Density-Based Spatial Clustering of Applications with Noise (DBSCAN). The performance is demonstrated using two real-world data collections in multipath/NLoS scenarios for both static and dynamic conditions. Since there is no ground truth for the pseudorange biases due to the multipath/NLoS scenarios, the proposed method is validated based on the positioning performance. Positioning solutions are computed by subtracting the estimated biases from the raw pseudoranges and comparing them to the ground truth. Full article

(This article belongs to the Special Issue Advances in Signal Processing for GNSS and Complementary PNT Technologies)

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24 pages, 8154 KiB

Open AccessArticle

GNSS Radio Frequency Interference Monitoring from LEO Satellites: An In-Laboratory Prototype

by Micaela Troglia Gamba, Brendan David Polidori, Alex Minetto, Fabio Dovis, Emilio Banfi and Fabrizio Dominici

Sensors 2024, 24(2), 508; https://doi.org/10.3390/s24020508 - 13 Jan 2024

Cited by 1 | Viewed by 1404

Abstract

The disruptive effect of radio frequency interference (RFI) on global navigation satellite system (GNSS) signals is well known, and in the last four decades, many have been investigated as countermeasures. Recently, low-Earth orbit (LEO) satellites have been looked at as a good opportunity for GNSS RFI monitoring, and the last five years have seen the proliferation of many commercial and academic initiatives. In this context, this paper proposes a new spaceborne system to detect, classify, and localize terrestrial GNSS RFI signals, particularly jamming and spoofing, for civil use. This paper presents the implementation of the RFI detection software module to be hosted on a nanosatellite. The whole development work is described, including the selection of both the target platform and the algorithms, the implementation, the detection performance evaluation, and the computational load analysis. Two are the implemented RFI detectors: the chi-square goodness-of-fit (GoF) algorithm for non-GNSS-like interference, e.g., chirp jamming, and the snapshot acquisition for GNSS-like interference, e.g., spoofing. Preliminary testing results in the presence of jamming and spoofing signals reveal promising detection capability in terms of sensitivity and highlight room to optimize the computational load, particularly for the snapshot-acquisition-based RFI detector. Full article

(This article belongs to the Special Issue Advances in Signal Processing for GNSS and Complementary PNT Technologies)

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23 pages, 4428 KiB

Open AccessArticle

Satellite Navigation Message Authentication in GNSS: Research on Message Scheduler for SBAS L1

by Jiangyao Song, Ting Liu, Xiao Chen and Zhongwang Wu

Sensors 2024, 24(2), 360; https://doi.org/10.3390/s24020360 - 7 Jan 2024

Viewed by 745

Abstract

SBAS is mainly used in civil aviation and navigation, and will be applied to autonomous driving in the future. Given the open signal format of the Satellite Based Augmentation System (SBAS), which exposes security threats such as spoofing attacks, the utilization of SBAS navigation message authentication technology can improve the SBAS anti-spoofing ability from the system side. SBAS message authentication technology has become the future direction of SBAS system development. However, during the initial design of SBAS on L1, message authentication technology was not considered, and the addition of authentication messages will lead to further strain on existing message bandwidth resources. Therefore, in response to the issue of insufficient bandwidth resources after adding authentication messages to SBAS L1, a study on message scheduler for SBAS L1 authentication was conducted. A fixed time sequence dynamic message scheduler for incorporating authentication messages was proposed. This scheduler reduces the frequency of broadcasting clock error parameters to mitigate the impact of adding authentication messages. Furthermore, an optimized fixed time sequence dynamic message scheduler based on SBAS clock error messages was introduced. The results show that the message scheduler can not only improve the flexibility of SBAS message broadcasting, but also shorten the update interval of various types of messages under the premise of meeting the maximum update interval requirement. With minimal impact on the maximum message update interval, it improves the integrity, authenticity, and availability of messages. This approach can increase the effective message ratio in SBAS to over 91%, and the optimal solution reduces the initial user positioning time to 26 s. Full article

(This article belongs to the Special Issue Advances in Signal Processing for GNSS and Complementary PNT Technologies)

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15 pages, 3406 KiB

Open AccessArticle

Analysis of 5G and LTE Signals for Opportunistic Navigation and Time Holdover

by Adrian Winter, Aiden Morrison and Nadezda Sokolova

Sensors 2024, 24(1), 213; https://doi.org/10.3390/s24010213 - 29 Dec 2023

Cited by 1 | Viewed by 681

Abstract

The purpose of this study was to evaluate the stability and therefore suitability of available fifth generation (5G) and long-term evolution (LTE) signals for positioning navigation and timing (PNT) purposes with particular focus on answering questions around the time-scale-dependent stability of these sources, which, to our knowledge, has not been addressed in the context of the numerous publications within the PNT community to date. The methodology used directly measured the over-the-air signal phase stability to one or more of the cellular signal sources that were visible from the lab environment simultaneously while using a local atomic clock or differential measurements to isolate the time stability of the observable cellular downlink signals. This approach was taken since it does not require subscription or association with the networks under test. Instead, it exploits a ‘signal of opportunity’ (SoP) approach to signal use for PNT purposes. The somewhat surprising result is that the time domain instability of the sources was highly variable, dependent on the implementation choices of the operator, and that the stability of even the modernized towers was generally best at interrogation intervals of approximately 0.01 s, which indicates that the existing exploitation of these signals within the PNT community has substantial room for improvement through simple changes to the selected update rate used. Full article

(This article belongs to the Special Issue Advances in Signal Processing for GNSS and Complementary PNT Technologies)

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14 pages, 3563 KiB

Open AccessArticle

Skywave Detection and Mitigation for the MF R-Mode Continuously Operating Reference Station

by Pyo-Woong Son, Jongmin Park, Jaewon Yu, Suhui Jeong, Younghoon Han and Tae Hyun Fang

Sensors 2023, 23(11), 5046; https://doi.org/10.3390/s23115046 - 24 May 2023

Cited by 2 | Viewed by 1153

Abstract

There is an increasing need for an independent terrestrial navigation system, owing to the increasing reliance on global navigation satellite systems (GNSS). The medium-frequency range (MF R-Mode) system is considered a promising alternative; however, the skywave effect caused by ionospheric changes at night can degrade its positioning accuracy. To address this problem, we developed an algorithm to detect and mitigate the skywave effect on MF R-Mode signals. The proposed algorithm was tested using data collected from Continuously Operating Reference Stations (CORS) monitoring the MF R-Mode signals. The skywave detection algorithm is based on the signal-to-noise ratio (SNR) induced by the groundwave and skywave composition, whereas the skywave mitigation algorithm was derived from the I and Q components of the signals obtained through IQ modulation. The results demonstrate a significant improvement in the precision and standard deviation of the range estimation using CW1 and CW2 signals. The standard deviations decreased from 39.01 and 39.28 m to 7.94 and 9.12 m, respectively, while the precision (2-sigma) increased from 92.12 and 79.82 m to 15.62 and 17.84 m, respectively. These findings confirm that the proposed algorithms can enhance the accuracy and reliability of MF R-Mode systems. Full article

(This article belongs to the Special Issue Advances in Signal Processing for GNSS and Complementary PNT Technologies)

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20 pages, 1661 KiB

Open AccessArticle

GNSS Data/Pilot Combining with Extended Integrations for Carrier Tracking

by Daniele Borio

Sensors 2023, 23(8), 3932; https://doi.org/10.3390/s23083932 - 12 Apr 2023

Cited by 1 | Viewed by 1551

Abstract

Modern Global Navigation Satellite System (GNSS) signals are usually made of two components: a pilot and a data channel. The former is adopted to extend the integration time and improve receiver sensitivity, whereas the latter is used for data dissemination. Combining the two channels allows one to fully exploit the transmitted power and further improve receiver performance. The presence of data symbols in the data channel, however, limits the integration time in the combining process. When a pure data channel is considered, the integration time can be extended using a squaring operation, which removes the data symbols without affecting phase information. In this paper, Maximum Likelihood (ML) estimation is used to derive the optimal data-pilot combining strategy and extend the integration time beyond the data symbol duration. In this way, a generalized correlator is obtained as the linear combination of the pilot and data components. The data component is multiplied by a non-linear term, which compensates for the presence of data bits. Under weak signal conditions, this multiplication leads to a form of squaring, which generalizes the squaring correlator used in data-only processing. The weights of the combination depend on the signal amplitude and noise variance that need to be estimated. The ML solution is integrated into a Phase Lock Loop (PLL) and used to process GNSS signals with data and pilot components. The proposed algorithm and its performance are characterized from a theoretical point of view, using semi-analytic simulations and through the processing of GNSS signals generated using a hardware simulator. The derived method is compared with other data/pilot combining strategies with extended integrations showing the advantages and drawbacks of the different approaches. Full article

(This article belongs to the Special Issue Advances in Signal Processing for GNSS and Complementary PNT Technologies)

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15 pages, 9317 KiB

Open AccessArticle

Neustrelitz Total Electron Content Model for Galileo Performance: A Position Domain Analysis

by Ciro Gioia, Antonio Angrisano and Salvatore Gaglione

Sensors 2023, 23(7), 3766; https://doi.org/10.3390/s23073766 - 6 Apr 2023

Viewed by 1394

Abstract

Ionospheric error is one of the largest errors affecting global navigation satellite system (GNSS) users in open-sky conditions. This error can be mitigated using different approaches including dual-frequency measurements and corrections from augmentation systems. Although the adoption of multi-frequency devices has increased in recent years, most GNSS devices are still single-frequency standalone receivers. For these devices, the most used approach to correct ionospheric delays is to rely on a model. Recently, the empirical model Neustrelitz Total Electron Content Model for Galileo (NTCM-G) has been proposed as an alternative to Klobuchar and NeQuick-G (currently adopted by GPS and Galileo, respectively). While the latter outperforms the Klobuchar model, it requires a significantly higher computational load, which can limit its exploitation in some market segments. NTCM-G has a performance close to that of NeQuick-G and it shares with Klobuchar the limited computation load; the adoption of this model is emerging as a trade-off between performance and complexity. The performance of the three algorithms is assessed in the position domain using data for different geomagnetic locations and different solar activities and their execution time is also analysed. From the test results, it has emerged that in low- and medium-solar-activity conditions, NTCM-G provides slightly better performance, while NeQuick-G has better performance with intense solar activity. The NTCM-G computational load is significantly lower with respect to that of NeQuick-G and is comparable with that of Klobuchar. Full article

(This article belongs to the Special Issue Advances in Signal Processing for GNSS and Complementary PNT Technologies)

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19 pages, 793 KiB

Open AccessArticle

Deep Learning of GNSS Acquisition

by Parisa Borhani-Darian, Haoqing Li, Peng Wu and Pau Closas

Sensors 2023, 23(3), 1566; https://doi.org/10.3390/s23031566 - 1 Feb 2023

Cited by 4 | Viewed by 2732

Abstract

Signal acquisition is a crucial step in Global Navigation Satellite System (GNSS) receivers, which is typically solved by maximizing the so-called Cross-Ambiguity Function (CAF) as a hypothesis testing problem. This article proposes to use deep learning models to perform such acquisition, whereby the CAF is fed to a data-driven classifier that outputs binary class posteriors. The class posteriors are used to compute a Bayesian hypothesis test to statistically decide the presence or absence of a GNSS signal. The versatility and computational affordability of the proposed method are addressed by splitting the CAF into smaller overlapping sections, which are fed to a bank of parallel classifiers whose probabilistic results are optimally fused to provide a so-called probability ratio map from which acquisition is decided. Additionally, the article shows how noncoherent integration schemes are enabled through optimal data fusion, with the goal of increasing the resulting classifier accuracy. The article provides simulation results showing that the proposed data-driven method outperforms current CAF maximization strategies, enabling enhanced acquisition at medium-to-high carrier-to-noise density ratios. Full article

(This article belongs to the Special Issue Advances in Signal Processing for GNSS and Complementary PNT Technologies)

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