Visible Satellites Research Articles

LEO-SOP Differential Doppler/INS Tight Integration Method Under Weak Observability

The utilization of low Earth orbit (LEO) satellites’ signals of opportunity (SOPs) for absolute positioning and navigation in global navigation satellite system (GNSS)-denied environments has emerged as a significant area of research. Among various methodologies, tightly integrated Doppler/inertial navigation system (INS) frameworks present a promising solution for achieving real-time LEO-SOP-based positioning in dynamic scenarios. However, existing integration schemes generally overlook the key characteristics of LEO opportunity signals, including the limited number of visible satellites and the random nature of signal broadcasts. These factors exacerbate the weak observability inherent in LEO-SoOP Doppler/INS positioning, resulting in difficulty in obtaining reliable solutions and degraded positioning accuracy. To address these issues, this paper proposes a novel LEO-SOP Doppler/INS tight integration method that incorporates trending information to alleviate the problem of weak observability. The method leverages a parallel filtering structure combining extended Kalman filter (EKF) and Rauch–Tung–Striebel (RTS) smoothing, extracting trend information from the quasi-real-time high-precision RTS filtering results to optimize the EKF positioning solution for the current epoch. This approach effectively avoids the overfitting problem commonly associated with directly using batch data to estimate the current epoch state. The experimental results validate the improved positioning accuracy and robustness of the proposed method.

A High-Resolution Satellite-Based Solar Resource Assessment Method Enhanced with Site Adaptation in Arid and Cold Climate Conditions

Due to the favorable condition of arid and cold climates for ever-increasing photovoltaic installations by supporting them to operate around their maximum power, it would be interesting to evaluate the solar potential of this climate. In this study, we proposed a simple, semi-empirical model to estimate the global horizontal irradiance (GHI) from the high-resolution visible channel satellite data provided by the Japanese meteorological satellite Himawari 8/9. The site adaptation procedure uses approximately 2–3 years of data recorded at four ground stations in Mongolia’s arid and cold regions to optimize the model parameters in a lookup table. Then, the model’s performance is evaluated using the independent test data of 1–2 years. The previous version of the proposed model and shortwave radiation product retrieved from the JAXA’s P-Tree system are also used for benchmarking as baselines. As a result, we found that the performance of the proposed model under a time granularity of 10 min surpassed them with an RMSE of 85 W/m2 in an arid desert to 114 W/m2 in a cold climate. A significant improvement was especially noticed in the capital city of Ulaanbaatar, where the resulting RMSE was 13 W/m2 and 131 W/m2 lower than the baseline models.

Development of an algorithm for constructing a model of light pollution at a location using high-resolution visible satellite images

Abstract. This article proposes a mathematical model for determining the relative level of light pollution in the night sky at a selected point in the area and the influence of a specific urban infrastructure object on it based on visible high-resolution satellite images. Statistical characteristics of the relative influence on light pollution of specific local objects were determined at an experimental local site. This model allows us to assess the degree of light pollution and its impact on various ecosystems, as well as develop strategies to reduce the negative impact of artificial lighting on nature.

A reliable NLOS error identification method based on LightGBM driven by multiple features of GNSS signals

In complicated urban environments, Global Navigation Satellite System (GNSS) signals are frequently affected by building reflection or refraction, resulting in Non-Line-of-Sight (NLOS) errors. In severe cases, NLOS errors can cause a ranging error of hundreds of meters, which has a substantial impact on the precision and dependability of GNSS positioning. To address this problem, we propose a reliable NLOS error identification method based on the Light Gradient Boosting Machine (LightGBM), which is driven by multiple features of GNSS signals. The sample data are first labeled using a fisheye camera to classify the signals from visible satellites as Line-of-Sight (LOS) or NLOS signals. We then analyzed the sample data to determine the correlation among multiple features, such as the signal-to-noise ratio, elevation angle, pseudorange consistency, phase consistency, Code Minus Carrier, and Multi-Path combined observations. Finally, we introduce the LightGBM model to establish an effective correlation between signal features and satellite visibility and adopt a multifeature-driven scheme to achieve reliable identification of NLOSs. The test results show that the proposed method is superior to other methods such as Extreme Gradient Boosting (XGBoost), in terms of accuracy and usability. The model demonstrates a potential classification accuracy of approximately 90% with minimal time consumption. Furthermore, the Standard Point Positioning results after excluding NLOSs show the Root Mean Squares are improved by 47.82%, 56.68%, and 36.68% in the east, north, and up directions, respectively, and the overall positioning performance is significantly improved.

Dual-Frequency Multi-Constellation Global Navigation Satellite System/Inertial Measurements Unit Tight Hybridization for Urban Air Mobility Applications

A global navigation satellite system (GNSS) for remotely piloted aircraft systems (RPASs) positioning is essential, thanks to the worldwide availability and continuity of this technology in the provision of positioning services. This makes the GNSS technology a critical element as malfunctions impacting on the determination of the position, velocity and timing (PVT) solution could determine safety issues. Such an aspect is particularly challenging in urban air mobility (UAM) scenarios, where low satellite visibility, multipath, radio frequency interference and cyber threats can dangerously affect the PVT solution. So, to meet integrity requirements, GNSS receiver measurements are augmented/fused with other aircraft sensors that can supply position and/or velocity information on the aircraft without relying on any other satellite and/or ground infrastructures. In this framework, in this paper, the algorithms of a hybrid navigation unit (HNU) for UAM applications are detailed, implementing a tightly coupled sensor fusion between a dual-frequency multi-constellation GNSS receiver, an inertial measurements unit and the barometric altitude from an air data computer. The implemented navigation algorithm is integrated with autonomous fault detection and exclusion of GPS/Galileo/BeiDou satellites and the estimation of navigation solution integrity/accuracy (i.e., protection level and figures of merit). In-flight tests were performed to validate the HNU functionalities demonstrating its effectiveness in UAM scenarios even in the presence of cyber threats. In detail, the navigation solution, compared with a real-time kinematic GPS receiver used as the reference centimetre-level position sensor, demonstrated good accuracy, with position errors below 15 m horizontally and 10 m vertically under nominal conditions (i.e., urban scenarios characterized by satellite low visibility and multipath). It continued to provide a valid navigation solution even in the presence of off-nominal events, such as spoofing attacks. The cyber threats were correctly detected and excluded by the system through the indication of the valid/not valid satellite measurements. However, the results indicate a need for fine-tuning the EKF to improve the estimation of figures of merit and protection levels associated to the navigation solution during the cyber-attacks. In contrast, solution accuracy and integrity indicators are well estimated in nominal conditions.

Towards Real-Time Integrated Water Vapor Estimates with Triple-Frequency Galileo Observations and CNES Products

Integrated water vapor (IWV) is a crucial parameter for tropospheric sounding and weather prediction applications. IWV is essentially calculated using observations from global navigation satellite systems (GNSS). Presently, the Galileo satellite system is further developed, including more visible satellites that transmit multi-frequency signals. This study aims to evaluate the accuracy of real-time IWV estimated from a triple-frequency Galileo-only precise point positioning (PPP) processing model utilizing E1, E5a, E5b, and E5 observations, which is not addressed by the previous studies. For this purpose, Galileo datasets from 10 global reference stations spanning various 4-week periods in the winter, spring, summer, and fall seasons are acquired. To process the acquired datasets, dual- and triple-frequency ionosphere-free PPP solutions are used, including E1E5a PPP, E1E5aE5b PPP, and E1E5E5b PPP solutions. The publicly available real-time products from the Centre National d’Etudes Spatiales (CNES) are utilized. The real-time IWV values are computed and then validated with the European Centre for Medium-Range Weather Forecasting (ECMWF) reanalysis products (ERA5) counterparts. The findings demonstrate that the root mean square error (RMSE) of the estimated IWV is less than 3.15 kg/m2 with respect to the ECMWF ERA5 counterparts. Furthermore, the E1E5aE5b PPP and E1E5E5b PPP models enhance the IWV’s accuracy by about 11% and 16%, respectively, compared with the E1E5a PPP model.

An Improved Velocity-Aided Method for Smartphone Single-Frequency Code Positioning in Real-World Driving Scenarios

The availability of Global Navigation Satellite System (GNSS) raw observations in smartphones has driven research into low-cost GNSS solutions, especially in challenging urban environments, which have garnered significant attention from scholars in recent years. This study proposes an improved smartphone-based velocity-aided positioning method and conducts vehicle-mounted experiments in urban roads representing typical scenarios. The results show that when transitioning from low- to high-multipath environments, the number of visible satellites and carrier phase observations are highly sensitive to environmental factors, with frequent multipath effects. The introduction of robust pre-fit and post-fit residual algorithms has proven to be an effective quality control method. Additionally, using more refined observation models and appropriate parameter estimation algorithms led to a slight 6% improvement in velocity performance. The improved Kalman filter position estimation model (KFSPP-P) strategy, by incorporating velocity uncertainty into the state estimation process, overcomes the limitations of conventional velocity-aided smartphone positioning methods (KFSPP-V) in complex urban environments. In low-multipath environments, the accuracy of the KFSPP-P strategy is comparable to that of KFSPP-V, with an approximate 8% improvement in horizontal accuracy. However, in more challenging environments, such as tree-lined roads and urban environments, the KFSPP-P strategy shows significant improvements, particularly enhancing horizontal positioning accuracy by approximately 50%. These advancements demonstrate the potential of using smartphones to provide reliable positioning services in complex urban environments.

Optimal design of the long-term LEO almanac by considering the atmosphere drag effects

ABSTRACT Almanac is an elegant design to help Global Navigation Satellite Systems (GNSS) signal acquisition, which has been adopted by all global satellite navigation systems. For the future low Earth orbit (LEO) navigation constellation, the almanac will be more critical due to larger constellation and signal Doppler variation, while the LEO orbit prediction is more challenging due to complex perturbation forces, especially atmospheric drag. This study introduces a novel 9-parameter almanac model for LEO constellation, leveraging the Empirical Mode Decomposition (EMD) method. Our innovative approach uniquely balances almanac accuracy and simplicity by considering the atmosphere drag effects, addressing a critical gap in existing models. The proposed almanac model is validated with four in-orbit LEO satellites at various altitudes. For the LEO with a 600 km orbit or lower, our model achieves a 50% improvement in 23-day orbit prediction accuracy compared to the classical GPS almanac model. This significant enhancement extends to visibility prediction and signal acquisition performance. Our design limits the maximum elevation angle error to less than 1.5 degrees, efficiently excluding invisible satellites during LEO navigation signal acquisition. Furthermore, for visible satellite, the proposed LEO almanac reduces the signal frequency searching range by about 20 times during acquisition, with Doppler prediction errors better than 0.5 kHz/0.3 kHz for 95% of cases. These advancements represent a substantial leap in LEO navigation technology, offering improved efficiency and accuracy for future satellite systems.

The Accuracy of RTK enabled Mobile Phones in RTK denied Areas

Abstract. Surveyors often encounter challenges when tasked with measuring points in regions where buildings disrupt satellite visibility. The precise surveying of points in these areas typically relies on employing total stations. This method effectively extends high precision from unaffected zones (where satellite visibility is assured) to those areas where disruption occurs due to buildings. Total stations achieve this by executing distance and angle measurements from a reference point coordinate to the location where satellite visibility is compromised. The process is notably time-consuming, thereby incurring significant costs. Moreover, the intricate nature of this surveying process necessitates specialized skills and expertise, contributing further to the overall expense. In this study, we investigate an approach based on RTK (Real-Time Kinematic) enabled mobile phones and photogrammetry to achieve the same. In principle, this approach has a similar philosophy. However, instead of working with a few points with good satellite visibility that are obtained by a GNSS (Global Navigation Satellite System) rover, the procedure works with hundreds of observations with varying accuracy. Those accuracies depend on the satellite visibility. Each observation captures an image from the mobile phone together with the GNSS signal and its accuracy. Angle and distance measurements that traditionally are observed by a total station are retrieved by photogrammetry on the total image collection.

Using space lidar to infer bubble cloud depth on a global scale

Visible and microwave satellite measurements can provide the global whitecap fraction. The bubble clouds are three-dimensional structures, and a space-based lidar can provide complementary observations of the bubble depth. Here, we use lidar measurements of the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite to quantify global bubble depth from the depolarization. The relationship between CALIPSO bubble depth and wind speed from the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) and AMSR2 is similar to a recently derived relationship based on buoy measurements. The CALIPSO-based bubble depth data show global distributions and seasonal variations consistent with the high wind speed (> 7 m/s) but with some variance. We also found similarities between the CALIPSO bubble depth and the whitecap fraction from AMSR2 and WindSat. Our findings support the use of spaceborne lidar measurements for advancing the understanding of the 3D bubble properties, and the ocean physics at high wind speeds.

Initiation of a Supercell by Convectively Generated Gravity Waves in the Simulated Kaiyuan Tornadic Event of 3 July 2019

Abstract An EF4-rated supercell tornado occurred on 3 July 2019 in Kaiyuan, China, causing heavy casualties. A three-level nested-grid high-resolution numerical simulation is used to investigate the initiation of the tornadic supercell. Automatic weather station (AWS) data, FY-4A visible satellite data, and Doppler radar data are used to verify the model simulation. The most important aspects of the simulated pre-supercell mesoscale convective system (MCS) and the initiation of the supercell agree with observations. Detailed investigation of the model results reveals that the initial cells form first above a convective boundary layer (CBL) on the dry side of a surface dryline. Above the CBL is a moist layer in terms of relative humidity and the layer is stable. Convectively generated gravity waves (GWs) emanating from the MCS and propagating southward along the stable layer above the CBL provide localized forcing for the actual triggering of initial cells at specific locations. The associated perturbation potential temperature and vertical velocity patterns confirm that the GWs trigger a series of cloud bands. The additional lifting by the updraft of a horizontal convective roll in the CBL underneath the GW updraft works together to promote faster growth of the initial cell that later becomes the supercell. Examination of the Scorer parameter profiles shows favorable conditions for vertical trapping of GWs along the wave guide in the stable layer, preventing the radiation of wave energy to the upper levels.

Routing cost-integrated intelligent handover strategy for multi-layer LEO mega-constellation networks

Low Earth Orbit (LEO) mega-constellation networks, exemplified by Starlink, are poised to play a pivotal role in future mobile communication networks, due to their low latency and high capacity. With the massively deployed satellites, ground users now can be covered by multiple visible satellites, but also face complex handover issues with such massive high-mobility satellites in multi-layer. The end-to-end routing is also affected by the handover behavior. In this paper, we propose an intelligent handover strategy dedicated to multi-layer LEO mega-constellation networks. Firstly, an analytic model is utilized to rapidly estimate the end-to-end propagation latency as a key handover factor to construct a multi-objective optimization model. Subsequently, an intelligent handover strategy is proposed by employing the Dueling Double Deep Q Network (D3QN)-based deep reinforcement learning algorithm for single-layer constellations. Moreover, an optimal cross-layer handover scheme is proposed by predicting the latency-jitter and minimizing the cross-layer overhead. Simulation results demonstrate the superior performance of the proposed method in the multi-layer LEO mega-constellation, showcasing reductions of up to 8.2% and 59.5% in end-to-end latency and jitter respectively, when compared to the existing handover strategies.

A Cumulant-Based Method for Acquiring GNSS Signals.

Global Navigation Satellite Systems (GNSS) provide positioning, velocity, and time services for civilian applications. A critical step in the positioning process is the acquisition of visible satellites in the sky. Modern GNSS systems, such as Galileo-developed and maintained by the European Union-utilize a new modulation technique known as Binary Offset Carrier (BOC). However, BOC signals introduce multiple side-peaks in their autocorrelation function, which can lead to significant errors during the acquisition process. In this paper, we propose a novel acquisition method based on higher-order cumulants that effectively eliminates these side-peaks. This method is capable of simultaneously acquiring both conventional ranging signals, such as GPS C/A code, and BOC-modulated signals. The effectiveness of the proposed method is demonstrated through the acquisition of simulated signals, with a comparison to traditional methods. Additionally, we apply the proposed method to real satellite signals to further validate its performance. Our results show that the proposed method successfully suppresses side-peaks, improves acquisition accuracy in weak signal environments, and demonstrates potential for indoor GNSS applications. The study concludes that while the method may increase computational load, its performance in challenging conditions makes it a promising approach for future GNSS receiver designs.

A deep learning GNSS spoofing and jamming detection method with dual-frequency C/N0 heatmap

Abstract The Global Navigation Satellite System (GNSS) is vulnerable to interference due to the open signal structure and low signal strength, posing a significant threat to the billions of terminals worldwide that rely on GNSS receivers for precise Positioning, Navigation, and Timing (PNT) services. In this paper, we propose a cloud-edge framwork for GNSS spoofing and jamming monitoring, comprising the data acquisition module, GNSS monitoring module, detecting and reporting module. In this framwork, we design a Deep Learning (DL) method for detecting GNSS interference through Dual-frequency Carrier-to-Noise density ratio (C/N0) heatmaps (DD-C/N0). This method involves extracting and correlating features from C/N0 heatmaps of visible navigation satellites operating in the GPS L1 and L2 frequency bands, allowing the identification of anomalous patterns. A U-BLOX receiver was utilized to capture the GNSS satellite signals, while commercial jammers and Software-Defined Radio (SDR) HackRF One kits were employed to simulate the interference sources. Experimental results demonstrate that the proposed method achieves significantly higher performance, with an accuracy of 99% and 98% on the public dataset and real-time testing data, compared to unsupervised, semi-supervised, and supervised detectors that rely solely on single-channel data (L1 frequency band). Integrated with the DD-C/N0 method, the online GNSS monitoring system will be improved and deployed to automate spoofing and jamming detection tasks in the next step.

An investigation of PPP-B2b coverage and its performance in ZTD estimation and positioning in different regions

This study comprehensively evaluates the Beidou navigation satellite system's PPP-B2b service for real-time positioning and zenith total delay (ZTD) estimation. Despite its operational status, PPP-b2b's effective coverage is underexplored. We define this coverage by analysing satellite visibility and geometry and evaluating ZTD and positioning accuracy at 48 stations. Results show PPP-B2b achieves ZTD Errors of 10–20 mm in China and 15–30 mm in Europe, with kinematic experiments in China and Japan showing consistent accuracy within 20 mm. This study highlights PPP-b2b's potential for enhancing atmospheric monitoring and precise positioning in its effective regions.

Design and Implementation of a Global Positioning System (GPS) for Constraint-Free Applications

The implementation and evaluation of a GPS/GSM module for positioning, tracking, and remote communication applications are examined in this work. The Global Navigation Satellite System (GNSS) provides precise location and timing information through a network of satellites. Specifically, the study focuses on the performance of a SIM808 GPS/GSM module in various operational conditions and constraints. Trilateration tests were conducted at twelve locations, revealing a time-to-first-fix (TTFF) ranging from 102.4 to 163.2 seconds; position differentiation accuracy, as determined by the Haversine Equation, indicated deviations of up to 7.52% in constrained scenarios; altitude measurements exhibited an error of about 4.14 meters, while lateral distance differentiation indicated deviations of up to 30.99% in specific instances. The deviations affecting the efficacy of the module are such as signal strength, satellite visibility, and receiver quality. Thus, the findings highlight the module's potential for applications requiring remote monitoring, control, and IoT deployments. Further improvements and considerations for practical implementations are suggested based on the outcomes of the evaluation.

Analysis of the gain factors of 5G-assisted BDS RTK positioning in urban environments

The joint utilization of the Fifth Generation Communications Technology (5G) and the Global Navigation Satellite System (GNSS) serves as a promising solution to address the challenges associated with insufficient visible satellites and lower observation quality in urban environments. 5G allows for the angle and distance measurements, augmenting the performance of Real-Time Kinematic (RTK) positioning. To quantify the improvement of 5G observations on RTK positioning, this paper proposes a float solution gain factor and the Ambiguity Dilution of Precision (ADOP) gain factor. Based on these gain factors, the theoretical analysis and simulation are performed. This study designs an extended Kalman filter for 5G-assisted BeiDou Navigation Satellite System (BDS) RTK positioning, employing both the Full Ambiguity Resolution (FAR) and Partial Ambiguity Resolution (PAR) modes. Our experiment verified the effectiveness of 5G-assisted BDS RTK positioning in mitigating outlier occurrences and improving the ambiguity fixing rate as well as the positioning accuracy. In the FAR and PAR modes, the Three-Dimensional (3D) spatial accuracy increased by 48% and 18.8%, respectively, and the results are consistent with theoretical analysis based on gain factors. The fixing rate of RTK increased from 11.11% to 13.93%, while it increased from 32.58% to 44.43% for the PAR mode. The assistance of 5G observations reduced the median error for the FAR mode from over 1.3m to 0.9 m, and the third quartile from 2.1m to 1.05 m. For the PAR mode, the median error decreased from 0.5m to 0.12 m, and the third and fourth quartiles decreased from 0.65m to 0.38 m.

An Enhanced Collaborative Localization Method Based on Belief Propagation Aided by 3D Terrain Modelling

Navigation system performance degrades significantly in complex environments. It is important to analyze satellite visibility through 3D terrain modelling and separate the satellite signals propagated by NLOS to suppress the NLOS error. However, the traditional 3D terrain modelling visibility analysis method based on the pure terrain cover angle is only suitable for determining the visibility of GNSS satellites and may incorrectly separate LOS propagate measurement signals from members with low relative ranges and elevation angles under air–ground swarm conditions. To this end, this paper proposes a belief-propagating cooperative navigation method based on air–ground visibility analysis, which avoids mistakenly separating close-range LOS cooperative navigation signals by simultaneously considering the distances, elevation angles, and azimuths of the signal sources relative to the air–ground swarm members. The simulation shows that the cooperative navigation NLOS identification method based on air–ground visibility analysis proposed in this paper can more accurately realize the separation of NLOS signals under cooperative conditions than the traditional pure angular 3D terrain modelling visibility analysis method can, and the localization error of the members to be assisted is significantly reduced.

Satellite Visibility Prediction for Constrained Devices in Direct-to-Satellite IoT Systems

Satellite Visibility Prediction for Constrained Devices in Direct-to-Satellite IoT Systems

Navigation performance analysis of Earth–Moon spacecraft using GNSS, INS, and star tracker

Global Navigation Satellite System (GNSS) can provide an approach for spacecraft autonomous navigation in earth–moon space to make up for the insufficiency of earth-based tracking, telemetry, and control systems. However, its weak power and poor observation geometry near the moon causes new problems. After the GNSS signal characteristics and satellite visibility were evaluated in Phasing Orbit and Lunar Transfer Orbit, we proposed an adaptive Kalman filter based on the Carrier-to-Noise ratio (C/N0) and innovation vector to weaken the influence of GNSS accuracy attenuation as much as possible. The experimental results show that the spacecraft position and velocity accuracy are better than 10 m and 0.1 m/s near the Earth, and better than 50 m and approximately 0.2 m/s near the moon use GNSS with the proposed adaptive algorithms. Additionally, because of the deterioration of navigation performance based on the orbit filter during orbital maneuvering, we used accelerometer data to compensate for the dynamic model to maintain navigation performance. The results of the experiment provide a reference for subsequent studies.