Search Results (71)

GNSS-based navigation can become unreliable when signals are blocked or deliberately interfered with. For small UAV platforms operating in complex environments, this limitation motivates the exploration of alternative positioning strategies such as opportunistic navigation (OpNav). Achieving reliable high-precision positioning under a fully non-cooperative setting remains difficult in practice where no infrastructure information is available. This mode is defined by three key constraints: unknown transmitter locations, unknown environmental topology and strictly asynchronous clocks. To address this limitation, we develop a lightweight sensing and navigation framework designed for UAV platforms operating under strict hardware constraints. We model static scattering centers as environmental anchors, proving that these features restore system observability even with a single unknown emitter. To ensure real-time performance on lightweight flight controllers, a hierarchical two-stage solver is designed: Stage I derives a robust closed-form initial estimate via an algebraic differencing method that is agnostic to reflection orders; Stage II performs manifold refinement using a Clock-Null Projection (CNP) to attain the CRLB. This framework is confirmed through experiments in urban areas using commercial LTE signals. The results show that it can map unknown RF topologies with meter-level accuracy and keep navigating without prior infrastructure, offering a strong solution for UAV autonomy in environments where GNSS is unavailable. Full article

Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) provides a low-cost, all-weather approach for continuous soil moisture content (SMC) retrieval. However, in single-constellation, multi-satellite applications, the optimal satellite number and the combined effects of multiple environmental factors on retrieval accuracy and stability remain insufficiently quantified. To address these issues, this study develops a dual-frequency GNSS-IR SMC retrieval framework that explicitly incorporates multiple environmental factors. Entropy-based fusion (EFM) is used to adaptively weight dual-frequency phase-delay observations, and a marginal-gain criterion is introduced to determine a suitable number of participating satellites. On this basis, univariate linear regression (ULR) and random forest (RF) models are established, and the Normalized Difference Vegetation Index (NDVI), temperature, and precipitation are incorporated into the RF model to improve retrieval robustness and quantify the relative contributions of environmental factors. The results show that multi-satellite combinations significantly improve SMC retrieval performance, while the incremental gain exhibits clearly diminishing returns and converges when the number of participating satellites reaches about 5–6 within a single constellation. Dual-frequency fusion consistently outperforms single-frequency schemes across different GNSS constellations, demonstrating the complementary value of multi-frequency information under multi-satellite conditions. In addition, the environmentally informed nonlinear model achieves higher accuracy and stability than the linear model, and the dominant environmental drivers differ across stations. Overall, this study provides quantitative support for configuring single-constellation multi-satellite GNSS-IR soil moisture monitoring schemes and for improving retrieval robustness under complex environmental conditions. Full article

This paper introduces an innovative radar-based navigation approach designed to address precision navigation challenges for Vertical Take-Off and Landing (VTOL) platforms. The key innovation is a unique extension of radar altimeter functionality when used with ground navigation beacons realized by Radio Frequency (RF) repeaters. The proposed solution is intended to support helicopter and Advanced Air Mobility (AAM) autonomous operations. Full article

It is well-known that GNSS high accuracy solutions are increasingly vulnerable to jamming and spoofing attacks, posing significant challenges to their reliability, security, and accuracy. In the past years, GNSS communities have witnessed an increase in the frequency and sophistication of these attacks, driven, among other factors, by the widespread availability of low-cost, off-the-shelf equipment capable of denying or even totally misleading GNSS-based positioning systems. On the one hand, jamming attacks aim at inhibiting signal reception by introducing high-power noise or interference, leading to degraded performance or complete failure in determining position. Jamming detection mechanisms need to be traced to GNSS receiver mitigation measures at signal processing level to analyze the radio frequency (RF) environment or receiver behavior. Signal-to-noise ratio (SNR) monitoring, power spectrum analysis, and signal power monitoring are commonly used to detect anomalies in signal characteristics. Jamming is often indicated with the presence of a combination of one or more dedicated indicators, opening space to characterize different levels of jamming attack allowing to optimize a response at user level. On the other hand, detecting spoofing attacks requires different advanced techniques to identify anomalies in satellite signals, receiver behavior, or consistency of computed position data. Indicators regarding internal consistency checks, as well as unexpected evolutions of GNSS signals, are typically suspicious behaviors to be analyzed as possible attacks. Additionally, ensuring trust in the received navigation information by including cryptographic authentication mechanisms is key to quickly detecting some kinds of spoofing. This paper presents the latest enhancements on jamming and spoofing detection and mitigation mechanisms for GMV GSharp^® high accuracy and safe positioning solution. This new method, based on fuzzy logic systems, allows us to distinguish between different levels of attack and adapt the reactions to reduce the impact on the final user as much as possible. Additionally, test results obtained from real GNSS attacks datasets will be shown. Full article

Cybersecurity in radio frequency (RF) technologies has become a critical concern, driven by the expansion of connected systems in urban and industrial environments. Although research on wireless networks and the Internet of Things (IoT) has advanced, comprehensive studies that provide a global and integrated view of cybersecurity development in this field remain limited. This work presents a scientometric and systematic review of international publications from 2009 to 2025, integrating the PRISMA protocol with semantic screening supported by a Large Language Model to enhance classification accuracy and reproducibility. The analysis identified two interdependent axes: one focusing on signal integrity and authentication in GNSS systems and cellular networks; the other addressing the resilience of IoT networks, both strongly associated with spoofing and jamming, as well as replay, relay, eavesdropping, and man-in-the-middle (MitM) attacks. The results highlight the relevance of RF cybersecurity in securing communication infrastructures and expose gaps in widely adopted technologies such as RFID, NFC, BLE, ZigBee, LoRa, Wi-Fi, and unlicensed ISM bands, as well as in emerging areas like terahertz and 6G. These gaps directly affect the reliability and availability of IoT and wireless communication systems, increasing security risks in large-scale deployments such as smart cities and cyber–physical infrastructures. Full article

Current passive microwave satellites like SMAP still face limitations in observational frequency and responsiveness in regions with frequent cloud cover, dense vegetation, or complex terrain, making it difficult to achieve continuous global monitoring with high spatio-temporal resolution. To enhance global high-frequency monitoring capabilities, this study utilizes global reflectivity data provided by the Tianmu-1 (TM-1) constellation since 2023, combined with multiple auxiliary variables, including NDVI, VWC, precipitation, and elevation, to develop a 9 km resolution soil moisture retrieval model. Several spatial clustering and temporal partitioning strategies are incorporated for systematic evaluation. Additionally, since the publicly available TM-1 L1 reflectivity data does not provide separable polarization channels, this study uses DDM/specular point reflectivity as the primary observable quantity for modeling and mitigates non-soil factor interference by introducing multi-source priors such as NDVI, VWC, precipitation, terrain, and roughness. Unlike SMAP’s “single orbit daily fixed local time” observation mode, TM-1, leveraging multi-constellation and multi-orbit reflection geometry, offers more balanced temporal sampling and availability in cloudy, rainy, and mid-to-high latitude regions. This enables temporal gap filling and rapid event response (such as moisture transitions within hours after precipitation events) during periods of SMAP’s quality masking or intermittent data loss. Results indicate that the model combining LC-cluster with seasonal partitioning delivers the best performance at the cluster level, achieving a correlation coefficient (R) of 0.8155 and an unbiased RMSE (ubRMSE) of 0.0689 cm³/cm³, with a particularly strong performance in barren and shrub ecosystems. Comparisons with SMAP and ISMN datasets show that TM-1 is consistent with mainstream products in trend tracking and systematic error control, providing valuable support for global and high-latitude studies of dynamic hydrothermal processes due to its more balanced mid- and high-latitude orbital coverage. Full article

Precise positioning is critical for autonomous uncrewed aerial vehicle (UAV) navigation, especially in GNSS-denied environments where radio-based signals are unreliable. This study presents an optical camera communication (OCC)-based positioning method that enables real-time 3D coordinate estimation using aviation obstruction light-emitting diodes (LEDs) as optical transmitters and a UAV-mounted camera as the receiver. In the proposed system, absolute positional identifiers are encoded into color-shift-keying-modulated optical signals emitted by fixed LEDs and captured by the UAV camera. The UAV’s 3D position is estimated by integrating the decoded LED information with geometric constraints through the Perspective-n-Point algorithm, eliminating the need for satellite or RF-based localization infrastructure. A virtual offshore wind farm, developed in Unreal Engine, was used to experimentally evaluate the feasibility and accuracy of the method. Results demonstrate submeter localization precision over a 50,000 cm flight path, confirming the system’s capability for reliable, real-time positioning. These findings indicate that OCC-based positioning provides a cost-effective and robust alternative for UAV navigation in complex or communication-restricted environments. The offshore wind farm inspection scenario further highlights the method’s potential for industrial operation and maintenance tasks and underscores the promise of integrating optical wireless communication into autonomous UAV systems. Full article

Global Navigation Satellite System (GNSS) meteorology has emerged as a valuable tool for atmospheric monitoring, providing high-resolution, near-real-time data that can significantly improve precipitation nowcasting. This study aims to enhance short-term precipitation forecasting by integrating GNSS-derived Precipitable Water Vapor (PWV)—a key indicator of atmospheric moisture—with traditional meteorological observations. A novel two-step machine learning framework is proposed that combines a Random Forest (RF) model and a Long Short-Term Memory (LSTM) neural network. The RF model first estimates current precipitation based on PWV, surface weather parameters, and auxiliary atmospheric variables. Then, the LSTM network leverages temporal dependencies within the data to predict precipitation for the subsequent hour. This hybrid method capitalizes on the RF’s ability to model complex nonlinear relationships and the LSTM’s strength in handling time series data. The results demonstrate that the proposed approach improves forecasting accuracy, particularly during extreme weather events such as intense rainfall and thunderstorms, outperforming conventional models. By integrating GNSS meteorology with advanced machine learning techniques, this study offers a promising tool for meteorological services, early warning systems, and disaster risk management. The findings highlight the potential of GNSS-based nowcasting for real-time decision-making in weather-sensitive applications. Full article

Inadequate screening of features and insufficient extraction of multi-source time-series data potentially result in insensitivity to historical noise and poor extraction of features for PM_2.5 concentration prediction models. Precipitable water vapor (PWV) data obtained from the Global Navigation Satellite System (GNSS), along with air quality and meteorological data collected in Suzhou city from February 2021 to July 2023, were employed in this study. The Spearman correlation analysis and Random Forest (RF) feature importance assessment were used to select key input features, including PWV, PM₁₀, O₃, atmospheric pressure, temperature, and wind speed. Based on RF, Long Short-Term Memory (LSTM), and Multilayer Perceptron (MLP) algorithms, four PM_2.5 concentration prediction models were developed using sliding window and fusion algorithms. Experimental results show that the root mean square error (RMSE) of the 1 h PM_2.5 concentration prediction model using the RF-LSTM fusion algorithm is 4.36 $\mathsf{\mu }\mathrm{g}/{\mathrm{m}}^3$, while its mean absolute error (MAE) and mean absolute percentage error (MAPE) values are 2.63 $\mathsf{\mu }\mathrm{g}/{\mathrm{m}}^3$ and 9.3%. Compared to the individual LSTM and MLP algorithms, the RMSE of the RF-LSTM PM_2.5 prediction model improves by 34.7% and 23.2%, respectively. Therefore, the RF-LSTM fusion algorithm significantly enhances the prediction accuracy of the 1 h PM_2.5 concentration model. As for the 2 h, 3 h, 6 h, 12 h, and 24 h PM_2.5 prediction models using the RF-LSTM fusion algorithm, their RMSEs are 5.6 $\mathsf{\mu }\mathrm{g}/{\mathrm{m}}^3$, 6.9 $\mathsf{\mu }\mathrm{g}/{\mathrm{m}}^3$, 9.9 $\mathsf{\mu }\mathrm{g}/{\mathrm{m}}^3$, 12.6 $\mathsf{\mu }\mathrm{g}/{\mathrm{m}}^3$, and 15.3 $\mathsf{\mu }\mathrm{g}/{\mathrm{m}}^3$, and their corresponding MAPEs are 13.8%, 18.3%, 28.3%, 38.2%, and 48.2%, respectively. Their prediction accuracy decreases with longer forecasting time, but they can effectively capture the fluctuation trends of future PM_2.5 concentrations. The RF-LSTM PM_2.5 prediction models are efficient and reliable for early warning systems in Suzhou city. Full article

Given the complexity of near-surface soil moisture retrieval, a single machine learning algorithm often struggles to capture the intricate relationships among multiple features, resulting in limited generalization and robustness. To address this issue, this study proposes a multi-satellite fusion GNSS-IR soil moisture retrieval method based on heterogeneous ensemble machine learning models. Specifically, two heterogeneous ensemble learning strategies (Bagging and Stacking) are combined with three base learners, Back Propagation Neural Network (BPNN), Random Forest (RF), and Support Vector Machine (SVM), to construct eight ensemble GNSS-IR soil moisture retrieval models. The models are validated using data from GNSS stations P039, P041, and P043 within the Plate Boundary Observatory (PBO) network. Their retrieval performance is compared against that of individual machine learning models and a deep learning model (Multilayer Perceptron, MLP), enabling an optimized selection of algorithms and model architectures. Results show that the Stacking-based models significantly outperform those based on Bagging in terms of retrieval accuracy. Among them, the Stacking (BPNN-RF-SVM) model achieves the highest performance across all three stations, with R of 0.903, 0.904, and 0.917, respectively. These represent improvements of at least 2.2%, 2.8%, and 2.1% over the best-performing base models. Therefore, the Stacking (BPNN-RF-SVM) model is identified as the optimal retrieval model. This work aims to contribute to the development of high-accuracy, real-time monitoring methods for near-surface soil moisture. Full article

This paper presents a novel harmonic-selective clocking scheme that facilitates concurrent downconversion of spectrally distant radio frequency (RF) signals using a single low-frequency local oscillator (LO) in an N-path receiver architecture. The proposed scheme selectively generates LO harmonics aligned with multiple RF bands, enabling simultaneous downconversion without modification of the passive mixer topology. The receiver employs a 4-path passive mixer configuration to enhance harmonic selectivity and provide flexible frequency planning.The architecture is implemented on a printed circuit board (PCB) and validated through comprehensive simulation and experimental measurements under continuous wave and modulated signal conditions. Measured results demonstrate a sensitivity of $-55\mathrm{dBm}$ and a conversion gain varying from $-2.5\mathrm{dB}$ to $-9\mathrm{dB}$ depending on the selected harmonic pair. The receiver’s performance is further corroborated by concurrent (dual band) reception of real-world signals, including a GPS signal centered at 1575 MHz and an LTE signal at 1179 MHz, both downconverted using a single 393 MHz LO. Signal fidelity is assessed via Normalized Mean Square Error (NMSE) and Error Vector Magnitude (EVM), confirming the proposed architecture’s effectiveness in maintaining high-quality signal reception under concurrent multiband operation. The results highlight the potential of harmonic-selective clocking to simplify multiband receiver design for wireless communication and global navigation satellite system (GNSS) applications. Full article

This study developed machine learning models using different algorithms, including support vector machine (SVM), random forest (RF), and backpropagation neural network (BPNN), to estimate the critical frequency of the F2 layer (foF2) and the maximum usable frequency of the F2 layer for a 3000 km circuit (MUF(3000)F2) based on the total electron content (TEC) observed by global navigation satellite system (GNSS) receivers. The ionospheric dataset used comprised TEC, foF2, and MUF(3000)F2 measurements from 11 stations in China during a solar activity period (2008–2020). The results indicate that all three machine learning models performed better than the IRI-2020 model, with varying levels of accuracy. For foF2 (MUF(3000)F2) estimation, the root mean square error (RMSE) values at Kunming and Xi’an stations were reduced by approximately 38% (26%) and 18% (11%), respectively, compared to IRI-2020. During geomagnetic disturbances, all three models were able to reproduce the variations in both foF2 and MUF(3000)F2 parameters. Nevertheless, the RF model showed significantly better performance in foF2 estimation compared to the SVM and BPNN models. Full article

Galileo Signal Authentication Service (SAS) is an assisted signal authentication capability under development by Galileo, designed to enhance the robustness of the European Global Navigation Satellite System (GNSS) against malicious attacks like spoofing. It operates by providing information about some fragments of the unknown spreading codes in the E6-C signal. Unlike other approaches, Galileo SAS uniquely employs Timed Efficient Stream Loss-tolerant Authentication (TESLA) keys provided by Open Service Navigation Message Authentication (OSNMA) in the E1-B signal for decryption, avoiding the need for key storage in potentially compromised receivers. The encrypted fragments are made available to the receivers before the broadcast of the E6-C signal, along with their broadcast time. However, if the receiver lacks an accurate time reference, searching for these fragments—which typically last for milliseconds and have periodicities extending to several seconds—can become impractical. In such cases, the probability of detection is severely diminished due to the excessively large search space that results. To mitigate this, initial estimates for the code phase delay and Doppler frequency can be obtained from the E1-B signal. Nevertheless, the alignment between E1-B and E6-C is not perfect, largely due to the intrinsic inter-frequency biases they exhibit. To mitigate this issue, we can leverage auxiliary signals like E6-B, processed by High Accuracy Service (HAS)-compatible receivers. This is a logical choice as E6-B shares the same carrier frequency as E6-C. This could help in obtaining more precise estimates of the location of the encrypted fragments and improving the probability of detection, resulting in enhanced robustness for the SAS authentication process. This paper presents a comparison of uncertainties associated with the use of the E1-B and E6-B signals, based on real data samples obtained with a custom-built Galileo SAS evaluation platform based on Software Defined Radio (SDR) boards. The results show the benefits of including E6-B in SAS processing, with minimal implementation cost. Full article

Global Navigation Satellite System (GNSS) reference receivers are an essential part of ground stations that make the operation of Satellite-Based Augmentation Systems (SBAS) possible. Recently, there has been increasing concern about spoofing and interference events, which may seriously hinder the operation of GNSS receivers in liability- and safety-critical applications and, in particular, SBAS ground stations. In this context, the goal of this paper is two-fold. On the one hand, a set of spoofing and interference detection techniques should be presented specifically tailored to operate with the outputs provided by a NovAtel G-III SBAS reference receiver. On the other hand, assessing these techniques with various tests conducted using a Safran Skydel GSG-8 GNSS RF simulator in order to validate their implementation and effectiveness is necessary. This work concludes with an analysis of the obtained results, providing insightful recommendations and guidelines. Full article

To address the challenge of collaborative nodes being unable to accurately perceive each other’s positions in global navigation satellite system (GNSS)-denied environments (such as after hostile interference or in urban canyons), we propose a GNSS-independent collaborative positioning radio frequency (RF) sensor. This sensor estimates inter-node distances and orientations using wireless measurements between nodes, without requiring pre-deployed anchor points. First, we designed a low-nanosecond latency ranging logic circuit on field-programmable gate array (FPGA) hardware, enabling relative distance estimation between nodes via a low-latency collaborative ranging (LLCR) algorithm without synchronization. Additionally, a synthetic aperture rotating antenna system was built to construct an echo space energy distribution matrix, based on dynamic–static dual-channel phase differences for high-precision, unambiguous azimuth measurement, followed by angle and distance data integration for localization. Then, a novel RF sensor hardware system was designed that was lightweight, low in cost, and high in performance. Finally, two generations of prototype models were developed and tested in both an anechoic chamber and mounted on unmanned vehicles outdoors in fields. The results demonstrate that the proposed sensor can achieve high-precision relative position estimation between collaborative nodes in the absence of GNSS, with a positioning error of within 0.4 m, indicating that it is suitable for mounting on unmanned vehicles and other autonomous systems for collaborative positioning. Full article