Global Navigation Satellite System Signals Research Articles

Analysis of Multipath Changes in the Polish Permanent GNSS Stations Network

This study examines the influence of multipath errors on Global Navigation Satellite System (GNSS) measurements collected at ASG-EUPOS reference stations between 2010 and 2021. Multipath occurs when GNSS signals reflect off surrounding objects before reaching the receiver antenna, leading to positioning errors. In the case of reference stations, all available mitigation techniques were used to minimize the impact of multipath. However, it is still detectable and affects the measurement results. For carrier phase differential positioning, it increases the ambiguous search space, which results in a decrease in determining rover—reference station vector accuracy. The study employs two linear combinations (Code-Minus-Carrier and Multipath Pseudorange Observable) to quantify the multipath effect on both pseudorange and carrier phase measurements. Based on the research, it was found that the multipath values changed depending on the change of the receiver and the terrain around the reference stations. The study observed a gradual decrease in multipath errors from 2010 to 2021, likely due to technological advancements in receiver design. No significant increase in multipath errors was observed due to environmental changes around the stations, suggesting a minimal influence from new reflecting objects nearby. Based on the analyses conducted, it is also recommended to perform periodic tests to detect incorrect receiver configuration or operation.

A Robust Vector-Tracking Loop Based on KF and RTS Smoothing for Shipborne Navigation

High-precision navigation systems are crucial for unmanned autonomous vessels. However, commonly used Global Navigation Satellite System (GNSS) signals are often severely affected by environmental obstruction, leading to reduced positioning accuracy or even the inability to locate. To address the issues caused by signal obstruction in high-precision navigation systems, the research presented in this paper proposes a vector-tracking loop (VTL) structure based on the forward Kalman Filter (KF) and the backward Rauch Tung Striebel (RTS) smoothing algorithm. The introduction of loop filters in the signal-tracking loop improves the tracking accuracy of the carrier and code, thereby enhancing the stability and robustness of the navigation system. The traditional scalar-tracking loop (STL), traditional VTL, and Kalman Filter (KF)−based VTL were compared through shipborne motion experiments, and the proposed method demonstrated superior signal-tracking capability and navigation accuracy. In the experiment, there were three blocking areas along the experimental path. The experimental results show that, when there are signal blockages of 12 s, 18 s, and 40 s, compared to the traditional VTL method, the proposed method can reduce the horizontal position error by 93.9%, 95.8%, and 94.5%, respectively, as well as the horizontal velocity error by 71.1%, 95.8%, and 97.6%, respectively.

Understanding Ionospheric and Geomagnetic Effects on Residual Biases in Radio Occultation Data for Stratospheric Climate Monitoring

AbstractHigher‐order residual ionospheric errors in Global Navigation Satellite System (GNSS) radio occultation (RO) data can induce a systematic residual ionospheric bias (RIB) in RO bending angles, which can impact stratospheric climate monitoring. The main RIB causes are the ray path splitting of dual‐frequency GNSS signals, the electron density distribution along the ray path and at the RO receiver location, and the geomagnetic field. In this study, we investigate the ionospheric and geomagnetic effects on RO‐retrieved stratospheric bending angle and temperature profiles by inspecting the kappa and bi‐local RIB correction methods, the current state‐of‐the‐art methods, using multiple RO satellite mission data in different ionospheric and geomagnetic conditions. We find that, globally, the layer mean/median RIBs of the kappa and bi‐local correction methods exhibit similar behaviors for different RO missions: the estimated bending angle RIBs reach about −0.025/−0.01 and −0.024/−0.008 μrad in the upper (40–45 km) and lower (30–35 km) stratosphere, respectively, while the temperature RIBs reach about −1.0/−0.3 and −0.2/−0.1 K in these layers. However, in the equatorial day time region, the RIB statistics of the mission results diverge. Both the kappa and bi‐local RIBs increase in magnitude with increasing ionization and geomagnetic field strength but show no increase with an increasing degree of ionospheric asymmetry. Overall, the more refined bi‐local method has higher capacity than the comparatively simple kappa method to represent the variability of the ionospheric and geomagnetic conditions that affect RO events. This is important for regional‐scale studies, where the geomagnetic term can be of key relevance.

Global Positioning System Signal Verification through Correlation Function Distortion and Received Power Tracking

Purpose: This study proposes a significantly improved method for detecting, classifying, and isolating Global Navigation Satellite System (GNSS) signals using the relationship between the measured power and the distortion of the correlation function to achieve the signal verification required for Global Positioning System (GPS) civil applications such as safe civil aircraft navigation. Methodology: The suggested approach uses power and distortion measurements in the received signal to identify it as jammed, multipath, spoofing, or no-interference. By adding an isolator scaling factor to the detector, the signal patterns will be induced with a unique temporary factor that will set it apart from the rest and make it possible to easily position each signal in its own zone. The detector divides the four signal types into distinct zones for verification. Findings: The sufficient signal data is analyzed and the extensive simulation conducted indicates that about 94% detection accuracy is achieved which is relatively high. Unique contribution to theory, practice and policy: This study is implemented through the development of relevant detection software tools with a user-friendly interface for GNSS signal detection, validation and analysis.

FGO-MFI: Factor Graph Optimization-based Multi-sensor Fusion and Integration for Reliable Localization

Abstract Reliable and precise information pertaining to the position, velocity, and attitude is essential for automated driving. This paper proposes FGO-MFI, a cost-effective and robust multi-sensor fusion and integration localization framework that utilizes factor graph optimization. Firstly, a tightly coupled GNSS (Global Navigation Satellite Systems)/on-board sensor fusion localization framework is established to estimate vehicle states, including position, velocity, and attitude. To address the large drift rate of the IMU (Inertial Measurement Unit), this study introduces a novel IMU/Dynamics pre-integration method based on the vehicle dynamics model. We establish a two-degree-of-freedom vehicle dynamics model utilizing measurements from the wheel speed sensor and steering wheel angle sensor. The IMU/Dynamics factor is devised through a close integration of the model output and IMU pre-integration, enabling the construction of precise odometry with low-cost on-board sensors. Then, to address the issue of the non-Gaussian distribution of GNSS pseudorange error, this paper employs a GMM (Gaussian Mixture Model) to characterize the pseudorange noise, which is then applied to further sensor fusion. Given the time-varying nature of pseudorange noise, the EM (expectation maximization) algorithm is utilized to estimate GMM parameters online, leveraging the pseudorange residuals within a sliding window. Comprehensive experiments, inclusive of challenging scenarios such as urban canyons, tunnels, and wooded areas, have been carried out. They affirm the superior performance of the proposed method. Experiments have shown that our method demonstrates reliable localization across different statuses of GNSS signal, exhibiting a 39.4% improvement in the root mean square error of position error when compared to the state-of-the-art. Additionally, this FGO-MFI is a general sensor data fusion framework and is able to incorporate diverse sensor measurements, for example, from cameras and LiDARs to provide more reliable and accurate localization information.

A Study on Anti-Jamming Algorithms in Low-Earth-Orbit Satellite Signal-of-Opportunity Positioning Systems for Unmanned Aerial Vehicles

Low-Earth-Orbit (LEO) satellite Signal-of-Opportunity (SOP) positioning technology has gradually matured to meet the accuracy requirements for unmanned aerial vehicle (UAV) positioning in daily scenarios. Advancements in miniaturization technology for positioning terminals have also made this technology’s application to UAV positioning crucial for UAV development. However, in the increasingly complex electromagnetic environment, there remains a significant risk of degradation in positioning performance for UAVs in LEO satellite SOP positioning due to unintentional or malicious jamming. Furthermore, there is a lack of in-depth research from scholars both domestically and internationally on the anti-jamming capabilities of LEO satellite SOP positioning technology. Due to significant differences in the downlink signal characteristics between LEO satellites and Global Navigation Satellite System (GNSS) signals based on Medium Earth Orbit (MEO) or Geostationary Earth Orbit (GEO) satellites, the anti-jamming research results of traditional satellite navigation systems cannot be directly applied. This study addresses the narrow bandwidth and high signal-to-noise ratio (SNR) characteristics of signals from LEO satellite constellations. We propose a Consecutive Iteration based on Signal Cancellation (SCCI) algorithm, which significantly reduces errors during the model fitting process. Additionally, an adaptive variable convergence factor was designed to simultaneously balance convergence speed and steady-state error during the iteration process. Compared to traditional algorithms, simulation and experimental results demonstrated that the proposed algorithm enhances the effectiveness of jamming threshold settings under narrow bandwidth and high-power conditions. In the context of LEO satellite jamming scenarios, it improves the frequency-domain anti-jamming performance significantly and holds high application value for drone positioning.

A Hopular based weighting scheme for improving kinematic GNSS positioning in deep urban canyon

Global navigation satellite system (GNSS) positioning performance in the urban dense environment experiences significant deterioration due to frequent non-line-of-sight (NLOS) and multipath errors. An accurate weighting scheme is critical for positioning, especially in urban environment. Traditional methods for determining the weights of observations typically rely on the carrier-to-noise density ratio (C/N0) and the elevations from satellites to receivers. Nevertheless, the performance of these methods is degraded in the dense urban settings, as C/N0 and elevation measurements fail to fully capture the intricacies of NLOS and multipath errors. In this paper, a novel GNSS observations weighting scheme based on Hopular GNSS signal classifier, which can accurately identify the LOS/NLOS signals using medium-sized training dataset, is proposed to improve the urban kinematic navigation solution in real-time kinematic positioning mode. Four GNSS features: C/N0, time-differenced code-minus-carrier, loss of lock indicator and satellite’s elevation, are employed in the training of the Hopular based signal classifier. The performance of the new method is validated using two urban kinematic datasets collected by a U-blox F9P receiver with a low-cost antenna, in downtown Calgary. For the first testing dataset, the results show that the Hopular based weighting scheme outperforms the three most commonly used GNSS observations weighting schemes: C/N0, elevation, and a combined C/N0-elevation approach. Approximately 10.089 m of horizontal root-mean-squared (RMS) positioning error and 12.592 m of vertical RMS error are achieved using the proposed method; with improvements of 78.83%, 46.82% and 43.27% on horizontal positioning accuracy and 54.00%, 47.51% and 49.69% on vertical positioning accuracy, compared to using C/N0, elevation and C/N0-elevation combined weighting schemes, respectively. For the second testing dataset, a similar performance is achieved with nearly 11.631 m of horizontal RMS error and 10.158 m of vertical RMS error; improvements of 64.58%, 32.90% and 22.40% on horizontal positioning accuracy and 71.99%, 65.24% and 55.88% on vertical positioning accuracy are achieved, compared to using C/N0, elevation and C/N0-elevation combined weighting schemes, respectively.

Ship-borne wave gauge using GNSS interferometric reflectometry

ABSTRACT To obtain wide coverage and frequent wave observations, low-cost methods to observe significant wave heights and wave periods from moving vessels were developed by using Global Navigation Satellite System (GNSS) interferometric reflectometry. Because GNSS signals reflected from the sea surface are always delayed respect to those received directly from the satellite, and this delay depends on the distance between the antenna and the sea surface, the amplitude of the received GNSS signals, in which the direct and reflected signals are mixed, shows interferometric variations in the presence of ocean waves. High sampling rate observations of the GNSS signal amplitude can be used to determine the significant wave period and wave height by referring to a look-up table estimated from simulated received GNSS signals based on a realistic wave spectrum. The methods were applied to actual 20-Hz GNSS observations on a ferryboat and qualitatively good estimations of both significant wave heights and wave periods were obtained. Because the look-up tables can be further modified using in situ wave observations, these estimations can be quantitatively improved in the future.

PAL-SLAM2: Visual and visual–inertial monocular SLAM for panoramic annular lens

This paper presents PAL-SLAM2, a visual and visual–inertial monocular simultaneous localization and mapping (SLAM) system for a panoramic annular lens (PAL) with an ultra-hemispherical field of view (FoV), overcoming the limitations of traditional frameworks in handling fast turns, nighttime conditions and rapid lighting changes. The system incorporates modules for initialization, tracking, local mapping, loop and map merging. To fully exploit information from the negative space (z<0), keypoints are projected onto the unit sphere for visual initialization and tightly-coupled visual–inertial optimization. Leveraging a multimap strategy, the feature-based PAL-SLAM2 is capable of detecting unidirectional and bidirectional public areas using PAL images, thereby enhancing the performance of loop correction and map merging. Testing indicates that it achieves an average accuracy of 7.1 cm on the PALVIO indoor dataset, surpassing similar state-of-the-art frameworks. Furthermore, we have collected a large-scale dataset comprising over 120,000 images with corresponding inertial measurement unit (IMU) data, demonstrating PAL-SLAM2’s robustness under challenging outdoor conditions. Relying solely on visual and inertial inputs, our system is particularly suited for environments where Global Navigation Satellite System (GNSS) signals are frequently obstructed, such as indoor spaces or dense urban areas. The dataset can be accessed at: https://github.com/wwendy233/Plaza-Dataset.

Machine learning algorithm partially reconfigured on FPGA for an image edge detection system

Unmanned aerial vehicles (UAVs) have been used for many applications including military, construction, image and video mapping, medical, search and rescue, wireless communications, and aerial surveillance. One key research topic involving UAVs is pose estimation in autonomous navigation. A standard procedure for this process for UAVs is to combine inertial navigation system (INS) sensor information with the global navigation satellite system (GNSS) signal. However, some factors can interfere with the GNSS signal, such as natural phenomena (ionospheric scintillation) or malicious attacks (jamming or spoofing). One alternative method to avoid using the GNSS signal is to apply an image processing approach by matching UAV images with georeferenced images. But a high effort is required for image edge extraction. In this paper, a support vector regression (SVR) model is proposed to reduce the computational load and processing time for image edge detection. The dynamic partial reconfiguration (DPR) of part of the SVR datapath was implementated to accelerate the process, reduce the area, and analyze its granularity aspect by increasing the grain size of the reconfigurable region. Results show that the implementation in hardware is 68 times faster than that in software. This kind of architecure with DPR also facilitates the low power consumption of 4 mW, leading to a reduction of 57 % compared with that without DPR, which is also the lowest power consumption compared with other machine learning (ML) hardware implementations. Besides, the circuitry area is 41 times smaller. SVR with Gaussian kernel showed a success rate of 99.18 % and minimum square error of 0.0146 for testing with the planning trajectory. This system is useful for adaptive applications where the user/designer can modify/reconfigure the hardware layout during its application, thus contributing to lower power consumption, smaller hardware area, and shorter execution time.

3D Galileo Reference Antenna Pattern for Space Service Volume Applications.

There is an increasing demand for navigation capability for space vehicles. The exploitation of the so-called Space Service Volume (SSV), and hence the extension of the Global Navigation Satellite System (GNSS) from terrestrial to space users, is currently considered a fundamental step. Knowledge of the constellation antenna pattern, including the side lobe signals, is the main input for assessing the expected GNSS signal availability and navigation performance, especially for high orbits. The best way to define and share this information with the final GNSS user is still an open question. This paper proposes a novel methodology for the definition of a high-fidelity and easy-to-use statistical model to represent GNSS constellation antenna patterns. The reconstruction procedure, based on antenna characterization techniques and statistical learning, is presented here through its successful implementation for the "Galileo Reference Antenna Pattern (GRAP)" model, which has been proposed as the reference model for the Galileo programme. The GRAP represents the expected Equivalent Isotropic Radiated Power (EIRP) variation for the Galileo FOC satellites, and it is obtained by processing the measurements retrieved during the characterization campaign performed on the Galileo FOC antennas. The mathematical background of the model is analyzed in depth in order to better assess the GRAP with respect to different objectives such as improved resolution, smoothness and proper representation of the antenna pattern statistical distribution. The analysis confirms the enhanced GRAP properties and envisages the possibility of extending the approach to other GNSSs. The discussion is complemented by a preliminary use case characterization of the Galileo performance in SSV. The accessibility, a novel indicator, is defined in order to represent in a quick and compact manner, the expected Galileo SSV quality for different altitudes and target mission requirements. The SSV characterization is performed to demonstrate how simply and effectively the GRAP model can be inserted into user analysis. The work creates the basis for an improved capability for assessing Galileo-based navigation in SSV according to the current knowledge of the antenna pattern.

A long short term memory network-based, global navigation satellite system/inertial navigation system for unmanned surface vessels

In recent years, research on unmanned surface vessels has entered a new phase, demanding the increased accuracy and reliability of integrated navigation and positioning systems. However, in complex environments, due to intermittent sensor failures or communication limitations, Global Navigation Satellite System (GNSS) signals are unreliable. Various errors that emerge in the system make it impossible to complete the measurement update process of filtering algorithms. This study provides a mechanism for improving the accuracy of combined GNSS/Shipborne Inertial Navigation System (SINS) integrated navigation when a GNSS outage occurs. Firstly, a SINS denoising method combining Complementary Ensemble Empirical Mode Decomposition (CEEMD) and an improved wavelet threshold algorithm is proposed. Secondly, a method based on Long Short-Term Memory (LSTM) network assisted GNSS/SINS integrated navigation is proposed to address the issue of poor information fusion performance when GNSS outages occur. The trained LSTM model is combined with SINS data to predict velocity and position during GNSS outages, providing high-precision velocity and position information. This ensures continuous, high-precision, navigation of unmanned surface vessels. Finally, to confirm the validity of the proposed methodology, a sea trial is performed to compare the different methods, and the results reveal the feasibility and effectiveness of the proposed methodology.

Design of Universal Code Generator for Multi-Constellation Multi-Frequency GNSS Receiver

A multi-constellation, multi-frequency Global Navigation Satellite System (GNSS) receiver is capable of simultaneously receiving signals from multiple satellite constellations across various frequency bands. This allows for increased observations, thereby enhancing navigation accuracy, continuity, effectiveness, and reliability. The spread spectrum code structures used in satellite navigation signals differ among systems. Compatible code generators are employed in multi-constellation, multi-frequency GNSS receivers to support tasks such as signal acquisition and tracking. There are three main types of spread spectrum code structures: Linear Feedback Shift Register (LFSR), Legendre sequences, and Memory codes. The Indian Regional Navigation Satellite System (IRNSS) released the L1-SPS (Standard Positioning Service) signal format in August 2023, which utilizes the Interleaved Z4-linear ranging code (IZ4 code) as its spread spectrum code. Currently, there is no universal code generator design compatible with the IZ4 code. In this paper, a proposed universal code generator is based on the hardware structure of the IRNSS IZ4 code generator. It achieves compatibility with all LFSR-based spread spectrum codes and enables parallel generation of multiple sets of GNSS signal spread spectrum codes, thereby improving hardware utilization efficiency. The proposed structure is implemented and validated using FPGA design, and resource consumption is provided as part of the validation results.

Global Navigation Satellite Systems Signal Vulnerabilities in Unmanned Aerial Vehicle Operations: Impact of Affordable Software-Defined Radio

Spoofing, alongside jamming of the Global Navigation Satellite System signal, remains a significant hazard during general aviation or Unmanned Aerial Vehicle operations. As aircraft utilize various support systems for navigation, such as INS, an insufficient Global Navigation Satellite System signal renders Unmanned Aerial Vehicles nearly uncontrollable, thereby posing increased danger to operations within airspace and to individuals on the ground. This paper primarily focuses on assessing the impact of the budget friendly Software-Defined Radio, HackRF One 1.0, on the safety of Unmanned Aerial Vehicles operations. Considering the widespread use of Software-Defined Radio devices today, with some being reasonably inexpensive, understanding their influence on Unmanned Aerial Vehicles safety is crucial. The generation of artificial interference capable of posing a potential threat in expanding Unmanned Aerial Vehicles airspace is deemed unacceptable.

Investigating Diurnal and Seasonal Cycles of Vegetation Optical Depth Retrieved From GNSS Signals in a Broadleaf Forest

AbstractVegetation Optical Depth (VOD) has emerged as a valuable metric to quantify water stress on vegetation's carbon uptake from a remote sensing perspective. However, existing spaceborne microwave remote sensing platforms face limitations in capturing the diurnal VOD variations and global products lack site‐level validation against plant physiology. To address these challenges, we leveraged the Global Navigation Satellite System (GNSS) L‐band microwave signal, measuring its attenuation by the canopy of a temperate broadleaf forest using a pair of GNSS receivers. This approach allowed us to collect continuous VOD observations at a sub‐hourly scale. We found a significant seasonal‐scale correlation between VOD and leaf water potential. The VOD diurnal amplitude is affected by soil moisture, plant transpiration and leaf surface water. Additionally, VOD can help independently estimate plant transpiration. Our findings pave the way for a deeper understanding of response of the vegetation to water stress at finer temporal scales.

Carrier frequency phase calibration method for global navigation satellite systems signal simulators

Measurements of carrier frequency phase differences of global navigation satellite system signals between multiple antennas are used in many coordinate and navigation applications. Global navigation satellite systems signal simulators are used for controlling global navigation satellite systems receiver’s instrumental errors. Therefore, it is necessary to determine the systematic error of the carrier frequency phase of generated signals for signal simulators. For this purpose a new calibration method of global navigation satellite systems simulators was developed. Method is based on refined errors model of generated phase differences of two radio frequency channels of the simulator. The proposed method was implemented using a wideband oscilloscope as an analog-to-digital converter. Approbation of the calibration method was carried out by comparison with the measurement results obtained using the State secondary standard of units of complex transmission coefficients in the range of 0...–60 dB and complex reflection coefficients in the range of 0.002–1 in the frequency range of 0.05–65 GHz (registration No. 2.1.ZZT.0210.2015). The calibration method allows one to determine carrier frequencies phase offsets of navigation signals for global navigation satellite systems simulators with expanded uncertainty (with a coverage factor of 3) of 1°. The proposed method provides the required accuracy of generating phase differences of the carrier frequencies of global navigation satellite systems signals is ensured for modern types of global navigation satellite systems simulators.

THE EFFECT OF SEASONAL VARIATION ON GNSS ZENITH TROPOSPHERIC DELAY

Abstract. The Global Navigation Satellite System (GNSS) signal experiences delays caused by the atmosphere, leading to the lengthening of the geometric path of the ray, commonly referred to as tropospheric delay. This delay is a significant source of error in GNSS positioning, contributing to a bias in the height component of several centimeters, even when meteorological data are simultaneously recorded and used in tropospheric models. In this study, considering seasonal variations, we investigated the impact of tropospheric delay on the GNSS height component. GNSS stations, part of the Turkish RTK CORS Network known as TUSAGA-Active (Turkish National Permanent GNSS Network Active), covered different heights over the 2014–2019 period. Daily coordinates of GNSS stations and tropospheric zenith delay were obtained through the GAMIT/GLOBK software solution.In the study, temperature, pressure, and relative humidity data of meteorological stations at different heights were converted to mean sea level. By using these values, interpolation estimates were made for the continuous GNSS stations in the same region with the IDW method. The most significant delay in GNSS signals occurs in July and August. This effect, which causes periodic changes in the zenith delay, varies inversely with the station's height. With the increase in the amount of water vapor in the atmosphere in parallel with the rise in the temperature in the summer months, it is seen that the stations at a low height are more exposed to the tropospheric effect than the stations at higher heights. In addition, GNSS stations' reduced meteorological values (temperature, pressure and relative humidity) show that the zenith delay values changed directly proportional to the temperature and inversely proportional to the pressure and relative humidity.

Flying Target Detection Technology Based on GNSS Multipath Signals.

In this study, a passive radar system that detects flying targets is developed in order to solve the problems associated with traditional flying target detection systems (i.e., their large size, high power consumption, complex systems, and poor battlefield survivability). On the basis of target detection, the system uses the multipath signal (which is usually eliminated as an error term in navigation and positioning), enhances it by supporting information, and utilizes the multi-source characteristics of ordinary omnidirectional global navigation satellite system (GNSS) signals. The results of a validation experiment showed that the system is able to locate a passenger airplane and obtain its flight trajectory using only one GNSS receiving antenna. The system is characterized by its light weight (less than 5 kg), low power consumption, simple system, good portability, low cost, and 24/7 and all-weather work. It can be installed in large quantities and has good prospects for development.

Virtual Validation of In-Flight GNSS Signal Reception during Jamming for Aeronautics Applications

Accurate navigation is a crucial asset for safe aviation operation. The GNSS (Global Navigation Satellite System) is set to play an always more important role in aviation but needs to cope with the risk of interference, possibly causing signal disruption and loss of navigation capability. It is crucial, therefore, to evaluate the impact of interference events on the GNSS system on board an aircraft, in order to plan countermeasures. This is currently obtained through expensive and time-consuming flight measurement campaigns. This paper shows on the other hand, a method developed to create a virtual digital twin, capable of reconstructing the entire flight scenario (including flight dynamics, actual antenna, and impact of installation on aircraft) and predicting the signal and interference reception at airborne level, with clear benefits in terms of reproducibility and easiness. Through simulations that incorporate jamming scenarios or any other interference scenarios, the effectiveness of the aircraft’s satellite navigation capability in the real environment can be evaluated, providing valuable insights for informed decision-making and system enhancement. By extension, the method shown can provide the ability to predict real-life outcomes even without the need for actual flight, enabling the analysis of different antenna-aircraft configurations in a specific interference scenario.

Dual-assisted high-precision tracking technique for wideband multiplexed signals in new generation GNSS

With the evolution of Global Navigation Satellite System (GNSS), new generation GNSS signals have adopted the dual-frequency multiplexing modulation techniques, which jointly modulate multiple signals located on multiple sub-frequencies into a Wideband Multiplexed Signal (WMS). Although WMSs were proposed initially to reduce the complexity of satellite transmitters and improve the transmission efficiency of signals, their multi-component structures and wide root mean square bandwidths introduced by high-frequency subcarriers also provide the possibility to improve the GNSS ranging precision. Therefore, this paper proposes a Dual-assisted Multi-component Tracking (DMT) technique, which can not only fully use high-frequency subcarriers in WMSs, but also effectively track carrier, subcarrier, and code by jointly utilizing all components in WMS. In this paper, the tracking and ranging performances of DMT are comprehensively analyzed theoretically and by simulation and real experiments. The results show that compared with existing WMS tracking methods, DMT can achieve tracking results with lower tracking jitters and ranging results with higher precision, providing a highly advantageous solution for new generation GNSS signal processing.