Search Results (28)

Time synchronization and positioning of bistatic radar transceivers is required to coordinate and meaningfully merge the measurements made between them. It simultaneously allows the radar transceivers to change their position throughout time. Despite their acknowledged vulnerabilities, Global Navigation Satellite Systems (GNSSs) are the preferred source for Positioning, Navigation and Timing (PNT) services. Because of these vulnerabilities however, research on possible signal sources to obtain alternative positioning, navigation and timing (A-PNT) is of interest. This present work proposes the use of a narrow-band transponder installed on a geostationary (GEO) satellite to be used as one anchor for a future time transfer. A channel calibration is made between the transceiver station and the chosen satellite. Diverse models are used to estimate the channel effects throughout the signal propagation path, estimate the time delay, and correct the measurements, accordingly. The available channel bandwidth on the proposed satellite is 2.7 kHz, limiting the accuracy of the time measurements. After integration of multiple pulses, a time accuracy of approximately 1 μs is obtained. The range measurements are compared against satellite positions propagated from publicly available two-line element sets (TLEs). The obtained results suggest that, after calibration, the expected accuracy and a good repeatability is obtained. Thus, making the QO-100 satellite a suitable anchor for the proposed technique. Full article

Location-based services (LBSs) and positioning systems have spread worldwide due to the emergence of Internet of Things (IoT) and other application domains that require real-time estimation of the position of a person, tag, or asset in general in order to provide users with services and apps with added value. Whereas Global Navigation Satellite Systems (GNSSs) are well-established solutions outdoors, positioning is still an open challenge indoors, where different sensory technologies may be considered for that purpose, such as radio frequency, infrared, or ultrasounds, among others. With regard to ultrasonic systems, previous works have already developed indoor positioning systems capable of achieving accuracies in the range of centimeters but limited to a few square meters of coverage and severely affected by the Doppler effect coming from moving targets, which significantly degrades the overall positioning performance. Furthermore, the actual bandwidth available in commercial transducers often constrains the ultrasonic transmission, thus reducing the position accuracy as well. In this context, this work proposes a novel excitation and processing method for an ultrasonic positioning system, which significantly improves the transmission capabilities between an emitter and a receiver. The proposal employs a superheterodyne approach, enabling simultaneous transmission and reception of signals across multiple channels. It also adapts the bandwidths and central frequencies of the transmitted signals to the specific bandwidth characteristics of available transducers, thus optimizing the system performance. Binary spread spectrum sequences are utilized within a multicarrier modulation framework to ensure robust signal transmission. The ultrasonic signals received are then processed using filter banks and matched filtering techniques to determine the Time Differences of Arrival (TDoA) for every transmission, which are subsequently used to estimate the target position. The proposal has been modeled and successfully validated using a digital twin. Furthermore, experimental tests on the prototype have also been conducted to evaluate the system’s performance in real scenarios, comparing it against classical approaches in terms of ranging distance, signal-to-noise ratio (SNR), or multipath effects. Experimental validation demonstrates that the proposed narrowband scheme reliably operates at distances up to 40 m, compared to the 34 m limit of conventional wideband approaches. Ranging errors remain below 3 cm at 40 m, whereas the wideband scheme exhibits errors exceeding 8 cm. Furthermore, simulation results show that the narrowband scheme maintains stable operation at SNR as low as $-32$ dB, whereas the wideband one only achieves up to $-17$ dB, highlighting the significant performance advantages of the proposed approach in both experimental and simulated scenarios. Full article

Today, unmanned aerial vehicles (UAVs) are heavily dependent on Global Navigation Satellite Systems (GNSSs) for positioning and navigation. However, GNSS signals are vulnerable to jamming and spoofing attacks. This poses serious security risks, especially for military operations and critical civilian missions. In order to solve this problem, an image-based geolocation system has been developed that eliminates GNSS dependency. The proposed system estimates the geographical location of the UAV by matching the aerial images taken by the UAV with previously georeferenced high-resolution satellite images. For this purpose, common visual features were determined between satellite and UAV images and matching operations were carried out using methods based on the homography matrix. Thanks to image processing, a significant relationship has been established between the area where the UAV is located and the geographical coordinates, and reliable positioning is ensured even in cases where GNSS signals cannot be used. Within the scope of the study, traditional methods such as SIFT, AKAZE, and Multiple Template Matching were compared with learning-based methods including SuperPoint, SuperGlue, and LoFTR. The results showed that deep learning-based approaches can make successful matches, especially at high altitudes. Full article

Nowadays, information obtained through Global Navigation Satellite Systems (GNSSs) is widely employed in modern geodesy. The Precise Point Positioning (PPP) approach, which leverages signals from multiple GNSS constellations (e.g., GPS, GLONASS, Galileo, and BeiDou), enables high-precision positioning—crucial for seismic monitoring and early tsunami warning systems (EEWs). Recent advances, such as increased satellite availability and additional frequency bands, have significantly improved PPP performance, particularly in terms of positioning accuracy and convergence time. This study focuses on the Rete Integrata Nazionale GNSS (RING) network, managed by the Istituto Nazionale di Geofisica e Vulcanologia (INGV), which comprises dual-frequency GNSS receivers distributed across the Italian peninsula and parts of the Mediterranean Basin. We evaluate the performance of the RING data (GPS and GNSS) acquired in a period of three weeks between 19 January 2024 and 9 February 2024 and analyzed in real time by using different PPP strategies: standard PPP and PPP with Regional Augmentation (PPP-RA). The preliminary results show that the PPP-RA approach enhances positioning accuracy and reduces convergence time, especially when comparing GPS-only datasets with those incorporating full multi-GNSS configurations. For the daily solution, in the optimal setup (i.e., full GNSS with RA), real-time solutions exhibit average accuracies of 2.05, 1.73, and 4.35 cm for the North, East, and vertical components, respectively. Sub-daily accuracies’ analysis, using 300 s sliding windows, showed even better uncertainties, exhibiting median values of 0.41, 0.32, and 0.9 cm for the North, East and vertical components, respectively. Based on the outcomes for network-wide sub-daily accuracies, 84% of the stations demonstrate average errors within 2 cm for North and East components and 3 cm for the vertical one. The analysis on the convergence time after data gaps occurred during the investigation period shows that 87% of the RING stations experienced convergence times lower than five minutes in the GNSS PPP-RA solution. These findings underscore the potential of RT-GNSS RING data for enhancing seismic monitoring and early warning systems, particularly in tectonically active regions. Full article

Global navigation satellite systems (GNSSs) are an integral part of modern society and are used in various industries, providing users with positioning, navigation, and timing (PNT). However, their effectiveness is vulnerable to signal interference, since GNSSs are based on received satellite signals from space, and that can severely impact applications that rely on continuous and accurate data. Interference can pose significant risks to sectors dependent on GNSSs, including transportation, telecommunications, finance, geodesy, and others. For this reason, in parallel with the development of GNSSs, various interference protection techniques are being developed to enable users to receive GNSS signals without the risk of interference, which can cause various effects, such as reducing the accuracy of positioning, as well as completely blocking signal reception and making it impossible to obtain positioning. There are various sources and methods of interfering with GNSS signals, and the greatest consequences are caused by intentional interference, which includes jamming, spoofing, and meaconing. This study investigates the effects of jamming devices on static GNSS observations using high-accuracy devices through multiple controlled experiments using both single-frequency (SF) and multi-frequency (MF) jammers. The aim was to identify the distances within which signal interference devices disrupt GNSS signal reception and position accuracy. The research conducted herein was divided into several phases where zones within which the jammer completely blocked the reception of the GNSS signal were determined (blackout zones), as were zones within which it was possible to obtain the position (but the influence of the jammer was present) and the influence of the jammer from different directions/azimuths in relation to the GNSS receiver. Various statistical indicators of the jammer’s influence, such as DOP (dilution of precision), SNR (signal-to-noise-ratio), RMS (root mean square), and others, were obtained through research. The results of this study indicate that commercially available, low-cost jamming devices, when operated within manufacturer-specified distances, completely disrupt the reception of GNSS signals. Their impact is also evident at greater distances, where they significantly reduce SNR values, increase DOP, and decrease the number of visible satellites, leading to reduced measurement reliability and integrity. These results underline the necessity of developing effective protection mechanisms against GNSS interference and strategies to ensure reliable signal reception in GNSS-dependent applications, particularly as the use of jamming devices becomes more prevalent. Full article

Signal-of-Opportunity (SOP) positioning based on Low-Earth-Orbit (LEO) constellations has gradually become a research hotspot. Due to their large quantity, wide spectral coverage, and strong signal power, LEO satellite SOP positioning exhibits robust anti-jamming capabilities. However, no in-depth studies have been conducted on their anti-jamming performance, particularly regarding the most common type of interference faced by ground receivers—Periodic Frequency Modulation (PFM) interference. Due to the significant differences in signal characteristics between LEO satellite downlink signals and those of Global Navigation Satellite Systems (GNSSs) based on Medium-Earth-Orbit (MEO) or Geostationary-Earth-Orbit (GEO) satellites, traditional interference suppression techniques cannot be directly applied. This paper proposes a Signal Adaptive Iterative Optimization Resampling (SAIOR) algorithm, which leverages the periodicity of PFM jamming signals and the characteristics of LEO constellation signals. The algorithm enhances the concentration of jamming energy by appropriately resampling the data, thereby reducing the overlap between LEO satellite signals and interference. This approach effectively minimizes the damage to the desired signal during anti-jamming processing. Simulation and experimental results demonstrate that, compared to traditional algorithms, this method can effectively eliminates single/multiple-component PFM interference, improve the interference suppression performance under the conditions of narrow bandwidth and high signal power, and holds a high application value in LEO satellite SOP positioning. Full article

Advanced receiver autonomous integrity monitoring (ARAIM) is an integrity technique for a global navigation satellite system (GNSS), centered on the multiple hypothesis solution separation (MHSS) test, which assesses the consistency between a subset and the all-in-view solution. Successful fault exclusion (FE) in ARAIM relies on identifying exclusion candidates that ensure no faults among the remaining satellites, a process requiring computationally expensive MHSS tests. The existing methods guide exclusion candidate searches based on the size of the normalized solution separation statistics, i.e., the normalized absolute difference between the subset solution and the all-in-view solution. However, in scenarios involving more than one satellite fault, these statistics can become unreliable due to fault diversity and interactions, perhaps misleading the FE process and causing its failure. To overcome this issue, our study proposes employing sparse estimation to simply identify satellite faults in one go, leveraging the sparsity of faulty satellites compared to the total number of observations in civil aviation GNSSs. Unlike the existing methods, which infer the fault likelihood indirectly through solution separation statistics, our approach represents an improvement that directly indicates potential exclusion candidates. Our experiments demonstrate that this method is fast and accurate. As a fundamentally different approach, it serves as a valuable complement or an alternative to the existing methods, enhancing the success and efficiency of the ARAIM FE process. Full article

Utilizing reliable and accurate positioning and navigation systems is crucial for saving the lives of rescue personnel and accelerating rescue operations. However, Global Navigation Satellite Systems (GNSSs), such as GPS, may not provide stable signals in dense forests. Therefore, integrating multiple sensors like GPS and Inertial Measurement Units (IMUs) becomes essential to enhance the availability and accuracy of positioning systems. To accurately estimate rescuers’ positions, this paper employs the Adaptive Unscented Kalman Filter (AUKF) algorithm with measurement noise variance matrix adaptation, integrating IMU and GPS data alongside barometric altitude measurements for precise three-dimensional positioning in complex environments. The AUKF enhances estimation robustness through the adaptive adjustment of the measurement noise variance matrix, particularly excelling when GPS signals are interrupted. This study conducted tests on two-dimensional and three-dimensional road scenarios in forest environments, confirming that the AUKF-algorithm-based integrated navigation system outperforms the traditional Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF), and Adaptive Extended Kalman Filter (AEKF) in emergency rescue applications. The tests further evaluated the system’s navigation performance on rugged roads and during GPS signal interruptions. The results demonstrate that the system achieves higher positioning accuracy on rugged forest roads, notably reducing errors by 18.32% in the north direction, 8.51% in the up direction, and 3.85% in the east direction compared to the EKF. Furthermore, the system exhibits good adaptability during GPS signal interruptions, ensuring continuous and accurate personnel positioning during rescue operations. Full article

Lane-level localization is critical for autonomous vehicles (AVs). However, complex urban scenarios, particularly tunnels, pose significant challenges to AVs’ localization systems. In this paper, we propose a fusion localization method that integrates multiple mass-production sensors, including Global Navigation Satellite Systems (GNSSs), Inertial Measurement Units (IMUs), cameras, and high-definition (HD) maps. Firstly, we use a novel electronic horizon module to assess GNSS integrity and concurrently load the HD map data surrounding the AVs. This map data are then transformed into a visual space to match the corresponding lane lines captured by the on-board camera using an improved BiSeNet. Consequently, the matched HD map data are used to correct our localization algorithm, which is driven by an extended Kalman filter that integrates multiple sources of information, encompassing a GNSS, IMU, speedometer, camera, and HD maps. Our system is designed with redundancy to handle challenging city tunnel scenarios. To evaluate the proposed system, real-world experiments were conducted on a 36-kilometer city route that includes nine consecutive tunnels, totaling near 13 km and accounting for 35% of the entire route. The experimental results reveal that 99% of lateral localization errors are less than 0.29 m, and 90% of longitudinal localization errors are less than 3.25 m, ensuring reliable lane-level localization for AVs in challenging urban tunnel scenarios. Full article

In recent years, low-Earth-orbit (LEO) constellations have been proposed for Positioning, Navigation and Timing (PNT) applications. Moreover, a couple of test satellites have already been launched and many more are scheduled in the near future. LEO constellations are characterised by their rapid change in geometry in comparison to the current medium-Earth-orbit (MEO) Global Navigation Satellite Systems (GNSSs). In this study, we analyse the impact of this high geometry change rate on the code-range error induced by multipaths. We develop a simulation environment with a static receiver and a nearby large building. We track the multipath signal using classical delay- and phase-locked loops (DLL and PLL). Multiple scenarios are simulated and analysed, comparing different orbit heights, MEO and LEO, and carrier frequencies (L-, S- and C-band). The LEO scenarios show up to 96% less code-range error for fast-changing multipath components. We show that this phenomenon is linked to the large phase delay rate between the direct signal and the multipath components, which is up to 75 times higher for LEO satellites when compared to MEO satellites. The phase delay rate reaches values higher than the DLL bandwidth. As a result, the DLL filters out the errors induced by fast-changing reflected signals, partially eliminating the multipath-induced code-range errors. The presented effect is coupled to the wavelength of the used carrier frequency. Our simulations show a reduction in multipath-induced code-range error for S- and C-band LEO-PNT signals in comparison to L-band signals. Full article

A concept that has been explored as a means to obtain decimeter-level positioning accuracy with global navigation satellite systems (GNSSs) is meta-signal processing (MSP), which treats several stand-alone GNSS signals as a single composite wideband signal. BeiDou Navigation Satellite System (BDS) III already offers with the B1I+B1C signal a meta-signal, while the forthcoming Galileo (GAL) E1D could be combined with E1B or E1C if the E1D signal is broadcast with a frequency offset to the L1/E1 carrier frequency. This would boost the ranging performance of GAL open service (OS) in the upper L-band through MSP. However, the cross-correlation function (CCF) of meta-signals contain numerous high side-maxima which can, when wrongly identified as the main peak, lead to significant pseudo-range errors of multiple meters. The probability of such a false lock is known to increase with decreasing signal-to-noise ratios but can significantly increase even further due to imperfections in the analog hardware components (e.g., linear and non-linear effects of a high-power amplifier (HPA), an output multiplexer (OMUX), a transmitter filter, and a front-end receiver), as these can distort the CCF. One remaining question is whether meta-signals are a well-suited approach to reliably increase ranging performance in the presence of payload and receiver distortions. This study presents the first systematic assessment of the robustness of several potential meta-signal options enabled by a forthcoming GAL E1D signal for different levels of distortion. The results show significant performance gains but also indicate constraints regarding the choice of signals when considering MSP under the influence of distortions. Full article

Hub-and-Spoke (H&S) network modeling is a form of transport topology optimization in which network joins are connected through intermediate hub nodes. The Short Sea Shipping (SSS) problem aims to efficiently disperse passenger flows involving multiple vessel routes and intermediary hubs through which passengers are transferred to their final destination. The problem contains elements of the Hub-and-Spoke and Travelling Salesman, with different levels of passenger flows among islands, making it more demanding than the typical H&S one, as the hub selection within nodes and the shortest routes among islands are internal optimization goals. This work introduces a multi-objective tri-level optimization algorithm for the General Network of Short Sea Shipping (GNSSS) problem to reduce travel distances and transportation costs while improving travel quality and user satisfaction, mainly by minimizing passenger hours spent on board. The analysis is performed at three levels of decisions: (a) the hub node assignment, (b) the island-to-line assignment, and (c) the island service sequence within each line. Due to the magnitude and complexity of the problem, a genetic algorithm is employed for the implementation. The algorithm performance has been tested and evaluated through several real and simulated case studies of different sizes and operational scenarios. The results indicate that the algorithm provides rational solutions in accordance with the desired sub-objectives. The multi-objective consideration leads to solutions that are quite scattered in the solution space, indicating the necessity of employing formal optimization methods. Typical Pareto diagrams present non-dominated solutions varying at a range of 30 percent in terms of the total distance traveled and more than 50 percent in relation to the cumulative passenger hours. Evaluation results further indicate satisfactory algorithm performance in terms of result stability (repeatability) and computational time requirements. In conclusion, the work provides a tool for assisting network operation and transport planning decisions by shipping companies in the directions of cost reduction and traveler service upgrade. In addition, the model can be adapted to other applications in transportation and in the supply chain. Full article

The identification of atmospheric and ionospheric variations through multiple remote sensing and global navigation satellite systems (GNSSs) has contributed substantially to the development of the lithosphere-atmosphere-ionosphere coupling (LAIC) phenomenon over earthquake (EQ) epicenters. This study presents an approach for investigating the Petrolia EQ (Mw 6.2; dated 20 December 2021) and the Monte Cristo Range EQ (Mw 6.5; dated 15 May 2020) through several parameters to observe the precursory signals of various natures. These parameters include Land Surface Temperature (LST), Air Temperature (AT), Relative Humidity (RH), Air Pressure (AP), Outgoing Longwave Radiations (OLRs), and vertical Total Electron Content (TEC), and these are used to contribute to the development of LAIC in the temporal window of 30 days before and 15 days after the main shock. We observed a sharp increase in the LST in both the daytime and nighttime of the Petrolia EQ, but only an enhancement in the daytime LST for the Monte Cristo Range EQ within 3–7 days before the main shock. Similarly, a negative peak was observed in RH along with an increment in the OLR 5–7 days prior to both impending EQs. Furthermore, the Monte Cristo Range EQ also exhibited synchronized ionospheric variation with other atmospheric parameters, but no such co-located and synchronized anomalies were observed for the Petrolia EQ. We also applied machine learning (ML) methods to confirm these abrupt variations as anomalies to further aid certain efforts in the development of the LAIC in order to forecast EQs in the future. The ML methods also make prominent the variation in the different data. Full article

Multi Global Navigation Satellite System (GNSS) Precise Point Positioning (PPP) has become the mainstream of PPP technology. Due to the differences in the coordinates and time references of each GNSS, multi-GNSS PPP must include additional Inter-System Bias (ISB) parameters to ensure compatibility between different GNSSs. Therefore, research on the characteristics of ISB is also essential. To analyze the short- and long-term time characteristics of multi-GNSS ISBs, as well as their relationship with receiver type and receiver antenna type, the Undifferenced and Uncombined (UDUC) PPP model of Global Positioning System (GPS), BeiDou navigation satellite system (BDS), and Galileo satellite navigation system (Galileo) was rigorously derived, and the physical of ISBs was elaborated in depth. ISB parameters were estimated and analyzed using 31 days of data from the 31 Multi-GNSS Experimental stations (MGEX). The results indicate that: (1) the ISB value is dependent on the station receiver type, receiver antenna type, analysis center product utilized, and GNSS system. (2) The short-term time characteristics of ISB-COM, ISB-WUM, and ISB-GBM are similar for the same station but not for the long term. In addition, ISBs are more stable in the short term. (3) There is little correlation between the ISB time characteristics, the receiver type, and the receiver antenna type, and the day-boundary discontinuity(DBD) on the ISB can be ignored for the concussive days’ process. Full article

An inter-system model for multi-GNSSs (global navigation satellite systems) makes the interoperability among different GNSS constellations possible. In recent years, inter-system models for geodetic receivers have been extensively studied. However, the precise positioning of smartphones suffers from various problems, and the current research mostly focuses on how to achieve the GNSS ambiguity resolution. Based on the research of receiver channel-dependent bias, in this study, we will research the temporal behaviors of differential inter-system bias (DISB) and implement an inter-system model for smartphones. A representative Huawei P40 (HP40) smartphone was used in the experiments, and the results show the following: (1) For the HP40, the frequencies of Code Division Multiple Access (CDMA) systems are free of receiver channel-dependent phase bias, which provides the chances for further interoperability among these systems. However, the code observations of the HP40 are influenced by receiver channel-dependent code bias; it is therefore suggested to set a large initial standard deviation (STD) value for code observations in the positioning. (2) GPS L1/QZSS L1 and BDS-2 B1I /BDS-3 B1I are free of phase DISB, and there is obvious phase DISB between GPS L1 and Galileo E1; even so, the valuations are sufficiently stable and the STD is close to 0.005 cycles. However, the phase DISB of GPS L1/BDS B1I is unstable. (3) For kinematic positioning, when the stable phase DISB is introduced, a 3–38.9% improvement in the N/E/U directions of the positioning accuracies in the inter-system differencing is achieved compared with the intra-system differencing. Full article