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International audience; Real-time globally consistent GPS tracking is critical for an accurate localization and is crucial for applications such as autonomous navigation or multi-robot mapping. However, under challenging environment conditions such as indoor/outdoor transitions, GPS signals are partially available or not consistent over time. In this paper, a real-time tracking system for continuously locating emergency response agents in challenging conditions is presented. A cooperative localization method based on Laser-Visual-Inertial (LVI) and GPS sensors is achieved by communicating optimization events between a LiDAR-Inertial-SLAM (LI-SLAM) and Visual-Inertial-SLAM (VI-SLAM) that operate simultaneously. The estimation of the pose assisted by multiple SLAM approaches provides the GPS localization of the agent when a stand-alone GPS fails. The system has been tested under the terms of the MALIN Challenge, which aims to globally localize agents across outdoor and indoor environments under challenging conditions (such as smoked rooms, stairs, indoor/outdoor transitions, repetitive patterns, extreme lighting changes) where it is well known that a stand-alone SLAM will not be enough to maintaining the localization. The system achieved Absolute Trajectory Error of 0.48%, with a pose update rate between 15 and 20 Hz. Furthermore, the system is able to build a global consistent 3D LiDAR Map that is post-processed to create a 3D reconstruction at different level of details.

A synthetic air data system (SADS) is an analytical redundancy technique that is crucial for unmanned aerial vehicles (UAVs) and is used as a backup system during air data sensor failures. Unfortunately, the existing state-of-the-art approaches for SADS require GPS signals or high-fidelity dynamic UAV models. To address this problem, a novel synthetic airspeed estimation method that leverages deep learning and an unscented Kalman filter (UKF) for analytical redundancy is proposed. Our novel fusion-based method only requires an inertial measurement unit (IMU), elevator control input, and airflow angles while GPS, lift/drag coefficients, and complex aircraft dynamic models are not required. Additionally, we demonstrate that our proposed temporal convolutional network (TCN) is a more efficient model for airspeed estimation than the renowned models, such as ResNet or bidirectional long short-term memory (LSTM). Our deep learning-aided UKF was experimentally verified on long-duration real flight data and has promising performance compared with the state-of-the-art methods. In particular, it is confirmed that our proposed method robustly estimates the airspeed even under dynamic flight conditions where the performance of conventional methods is degraded.

Cities offer extensive facilities to enrich the quality of life by utilizing smart devices and sensors. The Internet of Things and smart sensors connect various city services with the inhabitants. The services should be convenient and accessible to all, especially pedestrians and people with visual impairment. However, the lack of information about service locations often limits their availability and use. To this end, we developed FinderX, a Bluetooth beacon-based system to search for the nearest services and amenities. FinderX identifies the locations of nearby amenities in real-time using the signal from attached beacons. The system does not require Internet or other communication infrastructure and can function where the GPS signal is inaccessible. To demonstrate the feasibility of FinderX, we set up a testbed and evaluated the system in an urban environment. We show that FinderX has adequate usability and feasibility and reduces the time to find the amenities by 18.98\% on average. We also demonstrate that Bluetooth beacons have lower horizontal error compared to GPS in micro-positioning (where semi-indoor or surrounding infrastructure limits signal accessibility), which motivates the use of Bluetooth beacons for such applications.

Global Navigation Satellite System (GNSS) Reflectometry uses reflected GNSS signals for Earth remote sensing applications. Absolute calibration of a Delay Doppler Map (DDM) requires an accurate estimate of the effective isotropic radiated power (EIRP) of the GNSS transmitter, e.g., Global Positioning System (GPS). However, variable transmit power by numerous Block II Follow-on (IIF) and II Replenishment-Modernized (IIR-M) GPS space vehicles has been observed due to their flex power mode. Nonuniformity in the GPS antenna gain patterns further complicates EIRP estimation. A dynamic calibration approach is developed to address GPS EIRP variability. It uses measurements by the direct received GPS signal to estimate GPS EIRP in the specular reflected direction and then incorporates it into the calibration of normalized bistatic radar cross section (NBRCS). Error analyses using Monte Carlo simulations and a root sum of squares (RSS) approach show that the resulting error in NBRCS is about 0.32 dB. Dynamic EIRP calibration instantaneously detects and corrects for power fluctuations in the GPS transmitters and significantly reduces errors due to GPS antenna gain azimuthal asymmetry. It allows observations with the most variable Block IIF transmitters (approximately 37% of the GPS constellation) to be included in the standard data products and further improves the calibration quality of NBRCS and geophysical data products.

The ionospheric delay is one of the major error sources in GNSS precise data processing. In most scenarios, only the first-order ionosphere delay was fully considered, which contributes to about 99% of the total ionosphere delay. Recent research reveals that the higher-order ionospheric (HOI) delay can also introduce up to 2 cm biases for the GPS L1 observations. This bias is generally not considered in the underlying function model and consequently, it introduces unintentional biases, which may degrade the reliability of the ambiguity estimation in long-baseline RTK. This study examined the impact of the HOI on the observations and the ambiguity estimation procedure. We considered the HOI bias in the underlying model to evaluate the unintentional HOI bias on the success rate (SR) of ambiguity estimation. The numerical results showed that the double-differenced HOI effect reaches about 7 mm in the ionospheric active period, and can affect the ambiguity estimation success rate by up to 30% in the GNSS data processing and the HOI impact is more significant when a new satellite arose. The presence of the HOI biases may change about 0.3% ambiguity fixing results. In addition, the HOI bias can also introduce several millimeters biases in positioning solutions.

The dual-polarized array is considered as one of the key anti-jamming techniques due to twice counts of the jamming signals suppressed relative to a monopolar array. This paper proposes an adaptive anti-jamming dual-polarized ellipsoid minimum variance distortionless response (EMVDR) method to evaluate the carrier-to-noise ratio (Ca/N0) for global positioning system (GPS) coverage. Firstly in GPS L1 band, the dual-polarized gains obtained by controlled reception pattern antenna (CRPA) array are investigated as the signal model of GPS coverage evaluation. Secondly, a novel EMVDR method based the Hermitian tridiagonal loading technique is proposed to obtain the smooth anti-jamming weight matrix, which is against the system error by using the highpass filtering performance of Hermitian tridiagonal matrix. Finally, Ca/N0 based the proposed EMVDR is used to evaluate the coverage percentage of the GPS signal, namely Ca/N0 needs to be beyond a threshold of 28 dB∙Hz by recovering GPS signal. Through many Monte Carlo simulations, the results indicate that the anti-jamming performance of proposed EMVDR method outperforms the classic minimum variance distortionless response (MVDR) based the space adaptive processing (SAP) and the space time adaptive processing (STAP), and the GPS coverage ratio can keep from 60% to 70% until degrees of freedom (DOFs) are exhausted to zero.

A compact dual-band circularly polarized microstrip ring antenna for GPS L1 and L2 bands is proposed. The antenna comprises a two-ring radiator and a multi-ring-etched ground, forming two Yagi-Uda double-ring antennas, respectively. It operates in dual bands with a small ground size and unidirectional radiation. The proposed antenna yields right-hand circular polarization and fully covers GPS L1 and L2 bands.

The autonomous positioning of a vehicle predominantly relies on the global positioning system (GPS). However, in indoor environments, such as tunnels and indoor parking lots, the accuracy of GPS-based positioning can be significantly reduced due to weak GPS signals. To this end, we develop an accurate indoor vehicle positioning system using multiple fish-eye surveillance cameras. Our system first extracts vehicle segments from the top-view image of each fish-eye camera. These segments are then integrated into a common undistorted coordinate system. The center of the vehicle is finally determined using our simple but effective box fitting method. Moreover, a 1/18 scale indoor parking lot is designed to evaluate the performance of the proposed system. Throughout our experiments, we obtained average positioning errors of 30 or 24 cm in the regions covered by a single camera or multiple cameras, respectively.

Federated learning-based automotive navigation has recently received considerable attention, as it can potentially address the issue of weak global positioning system (GPS) signals under severe blockages, such as in downtowns and tunnels. Specifically, the data-driven navigation framework combines the position estimation offered by the high-sampling inertial measurement units and the position calibration provided by the low-sampling GPS signals. Despite its promise, the privacy preservation and flexibility of the participating users in the federated learning process are still problematic. To address these challenges, in this article, we propose an efficient, flexible, and privacy-preserving model aggregation scheme under a federated learning-based navigation framework named FedLoc . Specifically, our proposed scheme efficiently protects the locally trained model updates, flexibly supports the fluctuation of participants, and is robust against unregistered malicious users by exploiting a homomorphic threshold cryptosystem, together with the bounded Laplace mechanism and the skip list. We perform a detailed security analysis to demonstrate the security properties in terms of privacy preservation and dishonest user detection. In addition, we evaluate and compare the computational efficiency with two traditional schemes, and the simulation results show that our scheme greatly improves the computational efficiency during participant fluctuation. To validate the effectiveness of our scheme, we also show that only part of the model update is excluded from aggregation in the case of a dishonest user.

Patrol unmanned aerial vehicles (UAVs) in coal mines have high requirements for environmental perception. Because there are no GPS signals in a mine, it is necessary to use simultaneous localization and mapping (SLAM) to realize environmental perception for UAVs. Combined with complex coal mine environments, an integrated navigation algorithm for unmanned helicopter inertial measurement units (IMUs), light detection and ranging (LiDAR) systems, and depth cameras based on probabilistic membrane computing-based SLAM (PMC-SLAM) is proposed. First, based on an analysis of the working principle of each sensor, the mathematical models for the corresponding sensors are given. Second, an algorithm is designed for the membrane, and a probabilistic membrane system is constructed. The probabilistic SLAM map is constructed by sparse filtering. The experimental results show that PMC can further improve the accuracy of map construction. While adapting to the trend of intelligent precision mining in coal mines, this approach provides theoretical support and application practice for coal mine disaster prevention and control.

Flight safety and reliability improvement is an important research issue in aerial applications. Multi-rotor drones are vulnerable to motor failures leading to potentially unsafe operations or collisions. Therefore, researchers are working on autonomous landing systems to safely recover and land the faulty drone in on a desired landing area. In such a case, a suitable landing zone should be detected rapidly in for emergency landing. Majority of the works related with autonomous landing utilize a marker and GPS signals to detect landing site. In this work, we propose a landing system framework that involves only the processing of images taken from the onboard camera of the vehicle. First, the objects in the image are determined by filtering and edge detection algorithm, then the most suitable landing zone is searched. The area that is free from obstacles and closest to the center of the image is defined as the most immediate and suitable landing zone. The method has been tested on 25 images taken from different heights and its performance has been evaluated in terms runtime on a single board computer and detection precision and recall values. The average measured runtime is 2.4923 seconds and 100% of precision and recall values are achieved for the images taken from 1m and 2m. The smallest precision and recall values are 79.1% and 81.2%, respectively.

Textile antennas are currently being researched and developed so that they can be integrated into clothing. In this paper, two textile antennas with cotton and silk substrate are presented. The antennas operate at GPS L1 frequency, which is 1.575 GHz. This paper discusses the effect of antenna parameters on free space and on-body conditions, and the effect of antenna bending. Based on simulation and measurement results, the parameter values obtained indicate that the antenna can operate properly at a frequency of 1.575 GHz and is safe to use on the body.

Recent developments in GPS positioning show that a user with a standalone GPS receiver can obtain positioning accuracy comparable to that of carrier-phase-based differential positioning. Such a technique is commonly known as precise point positioning (PPP). A significant challenge of PPP, however, is that it typically requires a minimum of 30 minutes to achieve centimeter- to decimeter-level accuracy. This relatively long convergence time is the result of un-modeled GPS residual errors. This thesis addresses error mitigation techniques to achieve near real-time PPP. To explore the full advantage of the modernized GPS L2C signal, it is essential to determine its stochastic characteristics and code bias. GPS measurements were collected in order to study the stochastic characteristics of the modernized GPS L2C signal. As a byproduct, the stochastic characteristics of the legacy GPS signals, namely C/A and P2 codes, were also determined and then used to verify the developed stochastic model of the modernized signal. The differential code biases between P2 and C2, DCB P2-C2, were also estimated using the Bernese GPS software. A major residual error component, which affects the convergence of PPP solution, is the higherorder ionospheric delay. We rigorously modeled the second-order ionospheric delay, which represents the bulk of higher-order ionospheric delay, for our PPP model. First, we investigated the effect of second-order ionospheric delay on GPS satellite orbit and clock corrections. Second, we used the estimated satellite orbit and clock corrections to process the GPS data from several IGS stations after correcting the data for the effect of second-order ionospheric delay. The results demonstrated an improvement of up to 25% in the precision of the estimated coordinates with the second-order ionospheric delay, as well as reduction of the convergence time of the estimated parameters by about 15%, depending on the geographic location and ionospheric and geomagnetic conditions. Between-satellite single-difference PPP algorithms were developed to cancel out the receiver clock error, receiver initial phase bias, and receiver hardware delay. The decoupled clock corrections, provided by NRCan, were also applied to account for the satellite hardware delay and satellite initial phase bias. GPS data collected from several IGS stations were processed using the un-differenced model, un-differenced decoupled clock model, between-satellite singledifference (BSSD) model, and between-satellite single-difference using the decoupled clock (BSSD-DC) model. The results showed that the proposed BSSD model significantly improved the PPP convergence time by 50% and improved the solution precision by more than 60% over the traditional un-differenced PPP model.

Although vehicle location-based services are prevalent outdoors, we are back into darkness in many GPS blocked environments, such as tunnels, indoor parking garages, and multilevel flyovers. Existing smartphone-based solutions usually adopt inertial dead reckoning to infer the trajectory, but low-quality inertial sensors in phones are plagued by heavy noises, causing unbounded localization errors through double integrations for movements. In this article, we propose VeTorch , a smartphone inertial odometry that devises an inertial sequence learning framework to track vehicles in real time when GPS signal is not available. Specifically, we transform the inertial dynamics from the phone to the vehicle regardless of the arbitrary phone’s placement in the car and explore a temporal convolutional network to learn the vehicle’s moving dependencies directly from the inertial data. To tackle the heterogeneous smartphone properties and driving habits, we propose a federated learning-based active model training mechanism to produce customized models for individual smartphones, without incurring user privacy issues. We implement a highly efficient prototype and conduct extensive experiments on two large-scale real-world traffic data sets collected by a modern ride-hailing platform. Our results outperform the state-of-the-art vehicular inertial dead-reckoning solutions on both accuracy and efficiency.

The pre-seismic ionospheric anomalies coupled through well-proposed lithosphere–atmosphere–ionosphere processes are known to cause pre-seismic ionospheric disturbances (PIDs). The present paper investigates the regional variation of ionospheric densities in the Iran area with the purpose of anomalous ionospheric detection. The ionospheric reference model (IRI) is employed to examine the accuracy of such empirical models for typical TEC (total electron content) values in Iran to determine any deviation from the normal ionospheric state. Two strong consecutive earthquakes with a magnitude of larger than 6 in the northeast of Iran were selected along with the GPS data from 5 ground stations within 50 km of the epicenter. The local ionospheric plasma density mapping using the GPS signal for earthquake prediction is studied. The results show a very promising temporal variation of local ionospheric plasma between the stations from 4 weeks to almost a week leading to the event. The deviation of TEC from the mean value between the five stations shows no enhancement or suppression of local plasma rather than a plasma motion. A very quiet PID signature could validate the lithosphere–atmosphere–ionosphere coupling (LAIC) process associated with pre-earthquake geochemical and dynamical mechanisms. To validate that the analyzed time period falls on the minimum of solar activity, the observed positive anomalies in the regional TEC correspond to fluctuations to pre-earthquake activity and not to geomagnetic activity, and the $$K_p$$ and $$D_{st}$$ indices are taken into consideration.

Real-time monitoring of a wide-area power grid network has become feasible with time-synchronized Phasor Measurement Units (PMUs). PMUs use GPS signals for time synchronization and are susceptible to GPS Spoofing Attacks (GSAs) since civilian GPS signals are unencrypted and have low signal power of 10−16 Watts. We propose a novel Spoofing-Resilient State Estimator (SR-SE) that fuses time-varying GPS and PMU measurements using an Extended Kalman Filter (EKF). We design our measurement model that relates the coupling between GPS and PMU measurements to improve the performance of SR-SE. We perform software as well as Hardware-In-the-Loop (HIL) simulations to validate SR-SE against GSAs. We conduct 100 Monte Carlo (MC) simulations with IEEE 14, IEEE 39, and Illinois 200-bus test systems in software simulations. In HIL simulation, we simulate and generate GPS-PMU integrated datasets for the IEEE 14-bus test system using Real-Time Digital Simulator, PMUs, and GPS clock.

The installation of Phasor Measurement Units (PMUs) is a step towards achieving wide-area situational awareness for the power grid. PMUs utilize GPS signals for time synchronization and are vulnerable to GPS Spoofing Attacks (GSAs) as civilian GPS signals are unencrypted and have low signal power. We propose a novel Residual-based Spoofing Detection and Measurement Correction (RSDMC) algorithm for the power grid that detects GSAs and corrects spoofed PMU measurements. During one or multiple GSAs, RSDMC iteratively minimizes the residual norm to correct spoofed PMU measurements. We derive a necessary condition for our proposed algorithm to show that the residual norm increases during GSAs. We perform Monte Carlo simulations to verify our derived necessary condition. We further simulate the IEEE 14, IEEE 118, and Illinois 200 bus test cases to validate RSDMC against multiple GSAs.

A new adaptive unscented Kalman filter (AUKF) is proposed to estimate the radio navigation parameters of a GPS signal tracking system in noisy environments and on a highly dynamic object. The experimental results have shown that the proposed AUKFbased method improves the GPS tracking margin by approximately 8 dB and 3 dB as compared to the conventional algorithm and the KF-based tracking, respectively. At the same time, the accuracy of Doppler frequency measurements increases as well.

The comparative research of the influence of different types of auroral particle precipitation and polar cap patches (PCPs) on the global positioning system (GPS) signals disturbances in the polar ionosphere was done. For this purpose, we use the GPS scintillation receivers at Ny-Ålesund and Skibotn, operated by the University of Oslo. The presence of the auroral particle precipitation and polar cap patches was determined by using data from the EISCAT 42m radar on Svalbard. The optical aurora observations in 557.7 and 630.0 nm spectrum lines on Svalbard were used as well for the detection of ionospheric disturbances. The cusp identification was done with using SuperDARN (Hankasalmi) data. We consider events when the simultaneous EISCAT 42m and GPS data were available for the years 2010–2017, and in this paper we present, in detail, typical examples describing the overall picture, and we present the statistics for 120 events. We considered the dayside/cusp precipitation, substorm precipitation, daytime and nighttime PCPs, and precipitation associated with the interplanetary shock wave arrival. We demonstrate that substorm-associated precipitation (even without PCPs) can lead to a strong GPS phase (σϕ) scintillations up to ∼ 1.5–3 radians, which is much stronger than those usually produced by other types of considered ionosphere disturbances. The value of the substorm-phase scintillations in general correlate with the value of the geomagnetic field disturbance. But sometimes even a small geomagnetic substorm, when combined with the PCPs, produces quite strong phase scintillations. Cusp phase scintillations are lower than dayside PCPs scintillations. PCPs can lead to stronger ROT (rate of total electron content) variations than other types of ionosphere disturbances. So our observations suggest that the substorms and PCPs, being different types of the high-latitude disturbances, lead to the development of different types and scales of ionospheric irregularities.

Global Navigation Satellite Systems or GNSS is a space technology that has become an essential element nowadays for positioning, navigation & timing (PNT) with wide range of applications in many civilian sectors as well as across military. The reliability, accuracy and availability of GNSS are highly important especially for critical and precise positioning applications. However, the signals from space are weak and it can be easily blocked, disrupted or compromised by several other threats including intentional and unintentional interferences or jamming. GPS jammer is widely available off the shelf with an affordable price and capable of interfering the GPS signal, and many authorities worldwide have raised concerns and a lot of efforts and research have been put in place to reduce and mitigate the threats. In Malaysia, understanding and countering threats to GNSS/GPS based applications will be a new and unfamiliar discipline for public and organizations. This study intended to provide an overview of the GNSS interferences environment in a local study area, in terms of interference type and the number of activity pattern that were detected. A system called Detector V1 has been used in this study. The result showed that significant interference cases happened in the study area and some of the high power interferences may impact GNSS tracking and precision of the positioning output. The role objective of having this done is to create a public awareness regarding the threat of GNSS interferences to the local users. The content also includes the proposed initiative to overcome the issue.

In latest years, indoor positioning techniques have gained much attention, because of the absence GPS signal, so this paper shows a low priced mobile mapping system through using the advantages of integrating inertial navigation with smartphone sensors information concerned to a previous training phase with a magnetic map properly computed to more accurate positioning. Thus, areal online data sets were compiled through the use of ultra wide band to furnish an accurate positioning on the whole area of test and compute a trajectory used as a reference. Then, the use of the pedestrian dead reckoning based approach and IMU help to supply external information from the Wi-Fi signal that is used to exploit the received signal strength path loss, which is possibly used to assess the space between the device and access points. Furthermore, these real online data sets have been processed using Matlab to illustrate the different paths of the area of test. Also, using all RSS for every path line, different images were created. Finally, the positioning efficiency that is possible to be realized using information from IMU and UWB accelerated the fingerprinting training phase. So, the graphical analysis is used to summarize the results that match the closest path to the true path using mutual information.

Mobile robotics applications in outdoor environments now use intensively Global Positioning System (GPS). For localization or navigation operations, GPS has become an essential tool due to its ease of use, its precision, and its worldwide accessibility. The increase of autonomy level in mobile robotics implies a robust centimeter-level positioning, but the presence of natural (trees, mountains) or man-made obstacles (buildings) can degrade or prevent GPS signals reception. We present in this paper a solution for robots localization based on PELICAN microwave radar. PELICAN radar provides each second a panoramic image of the surrounding environment. These images are concatenated through a Simultaneous Localization And Mapping (SLAM) algorithm in order to build global maps of the traveled environments. The proposed solution computes the position and orientation of the robot through a real-time 3D matching between the current radar image and a pre-existing radar map constructed during an exploratory phase.

Road boundary detection is essential for autonomous vehicle localization and decision-making, especially under GPS signal loss and lane discontinuities. For road boundary detection in structural environments, obstacle occlusions and large road curvature are two significant challenges. However, an effective and fast solution for these problems has remained elusive. To solve these problems, a speed and accuracy tradeoff method for LiDAR-based road boundary detection in structured environments is proposed. The proposed method consists of three main stages: 1) a multi-feature based method is applied to extract feature points; 2) a road-segmentation-line-based method is proposed for classifying left and right feature points; 3) an iterative Gaussian Process Regression (GPR) is employed for filtering out false points and extracting boundary points. To demonstrate the effectiveness of the proposed method, KITTI datasets is used for comprehensive experiments, and the performance of our approach is tested under different road conditions. Comprehensive experiments show the road-segmentation-line-based method can classify left, and right feature points on structured curved roads, and the proposed iterative Gaussian Process Regression can extract road boundary points on varied road shapes and traffic conditions. Meanwhile, the proposed road boundary detection method can achieve real-time performance with an average of 70.5 ms per frame.

Prominent advances in the field of artificial intelligence during the past decade and the breakthrough of deep learning would be useful for investigating ionospheric weather using ground and space-based ionospheric sensors data. The significance of deep learning algorithms needs to be assessed in forecasting the low latitude ionospheric disturbances (delays) for the global positioning system (GPS) signals. Total electron content (TEC) data sets prepared by taking advantage of GPS satellite radio frequency (RF) signals. This letter provides the application of deep learning models, long short-term memory (LSTM), gated recurrent unit (GRU), and a hybrid model that consists of LSTM combined with convolution neural network (CNN) to forecast the ionospheric delays for GPS signals. The deep learning models implemented using the vertical TEC (VTEC) time-series data estimated from GPS measurements over Bengaluru, Guntur, and Lucknow GPS stations. The LSTM-CNN model performs well when compared to other ionospheric deep learning forecasting algorithms with minimum root-mean-square error (RMSE) of 1.5 TEC units (TECUs) and a high degree of $R^{2} = 0.99$ .

Nowadays a tremendous progress has been witnessed in Global Positioning System (GPS) and Inertial Navigation System (INS). The Global Positioning System provides information as long as there is an unobstructed line of sight and it suffers from multipath effect. To enhance the performance of an integrated Global Positioning System and Inertial Navigation System (GPS/INS) during GPS outages, a novel hybrid fusion algorithm is proposed to provide a pseudo position information to assist the integrated navigation system. A new model that directly relates the velocity, angular rate and specific force of INS to the increments of the GPS position is established. Combined with a Kalman filter the hybrid system is able to predict and estimate a pseud GPS position when GPS signal is unavailable. Field test data are collected to experimentally evaluate the proposed model. In this paper, the obtained GPS/INS datasets are pre-processed and semi-supervised machine learning technique has been used. These datasets are then passed into Kalman filtering for the estimation/prediction of GPS positions which were lost due to GPS outages. Hence, to bridge out the gaps of GPS outages Kalman Filter plays a major role in prediction. The comparative results of Kaman filter and extended Kalman filter are computed. The simulation results show that the GPS positions have been predicted taking into account some factors/measurements of a vehicle, the trajectory of the vehicle, the entire simulation was done using Anaconda (Jupyter Notebook).

Beginning from 1980s, the steady increase of total amount of deposited water or general amount of water vapor in the atmosphere almost above all regions of the Earth is observed. The changing of this parameter over the oceans strongly correlates with the temperature of sea water surface both in time and space. Obviously, the increase of total amount of deposited water or sum amount of water vapor leads to the growth of corresponding components of troposphere delay of GPS signals called “wet delay”. To achieve the goal set, foremost, the correlation properties of “wet delay” of GPS signals associated with the regularities of changing of total amount of deposited water in the atmosphere have been studied. Carried out analysis of actual materials of correlation properties of “wet” troposphere delay justified the presence of the aspects negatively affecting the correlation ratio of total amount of deposited water. Special correlation indexes allowing to regulate the balance of separate components of correlation function of “wet delay” are offered and their extreme nature shown as well. A new calculation technique for zenith wet delay by set value of integrated parameters of water vapor in the atmosphere has been developed.

A prominent security threat to unmanned aerial vehicle (UAV) is to capture it by GPS spoofing, in which the attacker manipulates the GPS signal of the UAV to capture it. This paper introduces an anti-spoofing model to mitigate the impact of GPS spoofing attack on UAV mission security. In this model, linear regression (LR) is used to predict and model the optimal route of UAV to its destination. On this basis, a countermeasure mechanism is proposed to reduce the impact of GPS spoofing attack. Confrontation is based on the progressive detection mechanism of the model. In order to better ensure the flight security of UAV, the model provides more than one detection scheme for spoofing signal to improve the sensitivity of UAV to deception signal detection. For better proving the proposed LR anti-spoofing model, a dynamic Stackelberg game is formulated to simulate the interaction between GPS spoofer and UAV. In particular, for GPS spoofer, it is worth mentioning that for the scenario that the UAV is cheated by GPS spoofing signal in the mission environment of the designated route is simulated in the experiment. In particular, UAV with the LR anti-spoofing model, as the leader in this game, dynamically adjusts its response strategy according to the deception’s attack strategy when upon detection of GPS spoofer’s attack. The simulation results show that the method can effectively enhance the ability of UAV to resist GPS spoofing without increasing the hardware cost of the UAV and is easy to implement. Furthermore, we also try to use long short-term memory (LSTM) network in the trajectory prediction module of the model. The experimental results show that the LR anti-spoofing model proposed is far better than that of LSTM in terms of prediction accuracy.

The satellite pseudo-range fault detection with the Receiver Autonomous Integrity Monitoring (RAIM) method is affected by several satellite observations and the geometric distribution of satellites. The poor geometry distribution of satellites will conceal the positioning error caused by the satellite pseudo-range fault, resulting in unreliable detection results. Therefore, the availability evaluation must be made before RAIM to ensure that the fault detection performance will not be affected. On June 23, 2020, China successfully launched the 30th (last) navigation satellite of BeiDou's third-generation navigation satellite system (BDS-3), which is also the 55th BeiDou navigation satellite. Combining all the available satellites of BDS-1, BDS-2 and BDS-3, the positioning performance of BDS can be greatly improved. In order to evaluate the RAIM availability of BeiDou Navigation Satellite System (BDS) and Global Positioning System (GPS) in China, this paper first deduces the mathematical models and their characteristics of the three RAIM availability evaluation methods. Then, the study area (N10°–70°, E60°–150°) is divided into 4536 grid points at intervals of 1 ° × 1 ° in latitude and longitude, and the elevations of these grid points are taken from the global terrain data file. The Horizontal Protection Level (HPL) values of these grid points are calculated during 8–15 June 2020 using BDS and GPS ephemeris data. The RAIM availability differences between the two systems are compared and analysed. The analysis shows the Horizontal Protection Level method (HPLM) based on single-satellite pseudo-range fault is the most practical and convenient. During the 8-day observation period, the HPL values of BDS are significantly smaller than those of GPS in terms of geographic location and observation time, and the variation of HPL time series of BDS is also smaller than that of GPS, which indicates that the RAIM availability of GPS in China is not as good as that of BDS. Most importantly, in the four flight stages of the aircraft's Oceanic/Continental low-density En-route, Continental En-route, Terminal En-route and Non-precision approach (NPA), BDS can completely satisfy its RAIM availability requirement, while GPS can only meet the availability requirement of the En-route (Oceanic/Continental low density) phase, and the availability of the other three phases can at least reach 99.714%.

Over the last 15 years, the satellite constellation of the global positioning system (GPS) has been modernized for more precise applications, with the introduction of the L2C and L5 signals. However, among other effects, they are susceptible to severe ionospheric effects, particularly in the equatorial and low-latitude regions. Equatorial plasma bubbles, resulting from the combination of the ionospheric electrodynamics with plasma instability mechanisms and thermospheric coupling, may generate irregularity structures with scale sizes ranging from hundreds of kilometers to a few meters (or less). Ionospheric irregularities may cause deep amplitude fades and phase shifts to transionospheric signals. That is, they are responsible for amplitude and phase scintillation, which degrade receiver operations and may cause failures and unavailability to positioning and navigation services under extreme conditions. The objective of the present work is to analyze ionospheric scintillation effects on the L2C and L5 GPS signals, to compare their vulnerabilities with those of the L1 signal. The data used in this analysis were collected between November 2014 and March 2015, during the maximum solar activity of cycle 24 (a period of great scintillation incidence), by scintillation monitors deployed at four different sites in the Brazilian territory: Fortaleza, Presidente Prudente, Sao Jose dos Campos, and Porto Alegre. Intensity fades will be analyzed, considering different thresholds, to reveal their empirical probability distributions of scintillation occurrence, average fading occurrences and durations. The results will show that greater probabilities of strong scintillation occurrences are present in the modernized signals, reaching up to five times more events in the L5 signal in comparison with those in the legacy L1 signal. It will be shown that the L5 average fade duration is distinctly longer than the corresponding ones for the other frequencies, considering the same site, threshold, and L1 amplitude scintillation level. The results will also show that the average fade duration decreases according to the average ratio 0.6 s/3 dB within the threshold range from − 6 to − 15 dB, considering the same amplitude scintillation level and location.

The accuracy of location information, mainly provided by the global positioning system (GPS) sensor, is critical for Internet-of-Things applications in smart cities. However, built environments attenuate GPS signals by reflecting or blocking them resulting in some cases multipath and non-line-of-sight (NLOS) reception. These effects cause range errors that degrade GPS positioning accuracy. Enhancements in the design of antennae and receivers deliver a level of reduction of multipath. However, NLOS signal reception and residual effects of multipath are still to be mitigated sufficiently for improvements in range errors and positioning accuracy. Recent machine learning-based methods have shown promise in improving pseudorange-based position solutions by considering multiple variables from raw GPS measurements. However, positioning accuracy is limited by low accuracy signal reception classification. Unlike the existing methods, which use machine learning to directly predict the signal reception classification, we use a gradient boosting decision tree (GBDT)-based method to predict the pseudorange errors by considering the signal strength, satellite elevation angle and pseudorange residuals. With the predicted pseudorange errors, two variations of the algorithm are proposed to improve positioning accuracy. The first corrects pseudorange errors and the other either corrects or excludes the signals determined to contain the effects of multipath and NLOS signals. The results for a challenging urban environment characterized by high-rise buildings on one side, show that the 3-D positioning accuracy of the pseudorange error correction-based positioning measured in terms of the root mean square error is 23.3 m, an improvement of more than 70% over the conventional methods.

The accurate localization of nodes is one of the most basic tasks in underwater acoustic sensor networks (UASNs). However, in large-scale UASNs, nodes are difficult to be accurately located because GPS signals cannot be received and because underwater nodes cannot establish one-hop communication with beacons due to the limited transmission range. With the development of self-sinking beacon technology, in this article, we propose a mobile-beacon-based iterative localization (MBIL) mechanism to realize node hierarchically positioning of large-scale multihop UASNs with an aim at increasing the percentage of localized nodes and reducing the localization error in the network. The proposed mechanism first uses mobile beacon nodes to localize adjacent sensor nodes. Once the location information is obtained, the sensor node will calculate its confidence value to determine whether it is a qualified reference node. Then, the unknown nodes select three adjacent reference nodes with the highest evaluation index for localization, and the remaining unknown nodes are iteratively localized. Simulation results show that the proposed mechanism can not only achieve a higher proportion of localized nodes in a shorter time, but also effectively reduce the localization error. In addition, MBIL effectively balances the energy consumption of sensor nodes, which can prolong the network’s lifetime.

Subject and Purpose. The paper investigates tropospherically caused fluctuations of signals from the GPS navigation system while the satellite is beyond the radio horizon. The intensity of GPS signal fluctuations under the influence of meteorological factors is studied, too. The purpose of the work is to determine relationship between the intensity of GPS signal fluctuations and the meteorological conditions when satellite elevation angles are small. Methods and Methodology. The methodology of the work is based on the search for the intensity of amplitude fluctuations of GPS satellite’s signals depending on the meteorological situation. The measurement approach is registration of the GPS satellite’s signal level just before the satellite crosses the horizon. The data processing after a series of experimental studies is based on the extraction of tropospheric fluctuations from the complete GPS signal by the moving average method. Results. Experimental studies of the turbulent component of signals from GPS satellites at low elevation angles have been carried out. It has been revealed that a conventional elevation boundary below which the tropospheric influence is predominant can be established due to the synchronism property which the orbits of the GPS satellites hold. It has been shown that variations in the mean square deviation (MSD) of GPS signal fluctuations caused by the troposphere and extracted from the complete signal are consistent with meteorological parameter changes. Conclusion. Analysis has been carried out to show that a meteorological dependence of the MSD statistics of GPS signal tropospheric fluctuations exists. The intensity of fluctuations rises with the convective activity in the troposphere. The conducted experiments suggest that periods of increased turbulence in the atmosphere can be detected with the use of GPS signals. For this purpose, the optical methods will not do.

The measurements of Montenbrook et al. to determine the water surface level of the Walchensee alpine lake in Bavaria (Germany) are a vivid example of the manifestation of the peculiarities of the relationship between the GPS satellite system as a source and remote sensing of the Earth’s surface. The experiments were conducted in 2007 in the framework the GORS (GPS Occultation, Reflectometry and Scatterometry) program. The authors described the observed features in detail but did not provide their physical justification. In this study, it is shown that the observed effects are caused by the resonant interaction of electromagnetic waves with a medium containing Rydberg molecular complexes. They are the main reason for the delay of satellite constellation signals at altitudes of 60 to 110 km.

The object of research is the process of controlling the trajectory of unmanned aerial vehicles (UAVs) in autonomous flight mode based on neural network algorithms. The study is based on the application of numerical-analytical approach to the selection of modern technical solutions for the construction of standard models of platformless inertial navigation systems (BINS) for micro and small UAVs, followed by support for assumptions. The results of simulation in the Matlab environment allowed to simulate the operation of the UAV control system based on MEMS technology (using microelectromechanical systems) and Arduino microcomputers. It was also possible to experimentally determine the nature of the influence of the structure of the selected neural network on the process of formation of navigation data during the disappearance of the GPS signal. Thus, to evaluate the effectiveness of the proposed solutions for the construction of BINS, a comparative analysis of the application of two algorithms ELM (Extreme Learning Machine)-Kalman and WANN (Wavelet Artificial Neural Network)-RNN (Recurrent Neural Network)-Madgwick in the form of two experiments. The purpose of the experiments was to determine: the study of the influence of the number of neurons of the latent level of the neural network on the accuracy of approximation of navigation data; determining the speed of the process of adaptive learning of neural network algorithms BINS UAV. The results of the experiments showed that the application of the algorithm based on ELM-Kalman provides better accuracy of learning the BINS neural network compared to the WANN-RNN-Madgwick algorithm. However, it should be noted that the accuracy of learning improved with the number of neurons in the structure of the latent level

This paper focuses on offline navigation in quadcopters (Return-to-Home), autonomously in GPS Denied Areas or in cases that the aircraft loses GPS Signal by using the Haversine’s Great circle formula to calculate distance and direction. The internal sensors are used to estimate offsets for distance and direction. A control system modification is proposed, resulting in a high success rate with the several tests using a quadcopter with a Pixhawk flight controller, equipped with PX4FLOW.

The advent of assisted GPS/GNSS can reduce the time to first fix (TTFF) dramatically and still provide sufficient positioning accuracy. However, many applications utilize GNSS for timing and assisted GPS/GNSS does not always provide the accurate nanosecond synchronization. Accurate timing is challenging for assisted GNSS due to the lack of diversity in the solution over short time periods. The relatively short ambiguities of the GPS L1 C/A code signal, one millisecond and twenty milliseconds, are too coarse to resolve accurate time reliably. In this paper, the impact of different ambiguity times has been derived/computed and validated. Experimental results show that when the ambiguity time is 200 milliseconds or longer, the least squares process will reliably converge and the typical nanosecond-level timing can be achieved. An example shows how the GPS L1 C/A data parity word (provided every 600 milliseconds) can be used, demonstrating nanosecond-level timing from assisted GPS.

A GPS spoofing attack broadcasts counterfeit signals to resemble standard GPS satellite signals to take control of the correlation peaks of GPS signals to force the victim receiver to estimate a false position. The proposed approach is based on an intelligent dynamic algorithm focusing on distinguishing authentic and counterfeit signals. This paper presents a method based on the Imperialist Competitive Algorithm to investigate correlation peaks of authentic and counterfeit signals. In this method, a feature vector is derived from the correlation function. When the spoofing signal tries to take control of the tracking loop, the feature values deviate. Accordingly, the cluster size of the spoofing signal increases until it becomes equal to or greater than the authentic signal size, and thus, the spoofing signal is detected. The performance of the presented detection approach is validated through simulations on real data. It reveals that the proposed method can detect spoofing attacks in 99.7% of cases.

Early one morning last May, a commercial airliner was approaching El Paso International Airport, in West Texas, when a warning popped up in the cockpit: “GPS Position Lost.” The pilot contacted the airline's operations center and received a report that the U.S. Army's White Sands Missile Range, in South Central New Mexico, was disrupting the GPS signal. “We knew then that it was not an aircraft GPS fault,” the pilot wrote later.

We analyze the behavior of Total Electron Content (TEC) in the Ionosphere using the GPS data between 2009 and 2017 towards the western part of India. The GPS data used in the present study consists of 22 Continuously Operating Reference Sites (CORS), bounded between 200- 240 N Latitude and 680 - 740 E Longitude. All the GPS data used in the study are generated using the Leica 1200 receivers and AX1202GG antennas with a sampling interval of 0.03 Hz. The TEC value of this study is calculated using the L-band GPS frequencies L1 (1575.42 MHz) and L2 (1227.36 MHz). The results reveal maximum TEC values during the daytime (03.30–04.30 IST) for all the years and a maximum of 120 TECu (TEC unit) was observed during the year 2014. The reflection of seasonal variation was visible in the time-series of TEC in all the years, while E season (March, April, September and October) observed maximum TEC values in all the stations. The mean monthly TEC values of the present study illustrate the solar cycle-24 and the coefficient of correlation (r2) of 0.74 indicates a close relationship between the sunspot number and TEC values. A fall of 35% TECu was observed on the annular solar eclipse day on 15 January 2010.