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Navigation of land or self-driving vehicles is essential for safe and accurate travel. The global navigation satellite systems (GNSSs), such as global positioning system (GPS) are the primary sources of navigation information for such purpose. However, high-rise buildings in urban canyons block the GPS satellites signals. Alternatively, inertial navigation system (INS) is typically working as a backup. A reduced inertial sensor system (RISS) is used instead of the full INS to achieve the same purpose in land vehicles navigation with fewer sensors and computations. Unfortunately, the RISS solution drifts over time, but this can be mitigated when integrated with the GPS. However, the integration solution drifts in the case of GPS signal loss (outages). Therefore, the position errors grow especially during extended periods of GPS outages. Azimuth/heading angle is critical to keep the vehicle on the route. In this paper, an azimuth update estimated from a calibrated magnetometer is introduced to improve the accuracy of the overall system. A new approach is proposed for pre-processing the magnetometer data utilizing a discrete-cosine-transform (DCT)-based pre-filtering stage. The obtained azimuth is utilized in updating the RISS system during the whole trajectory and mainly during GPS outage periods. The proposed approach significantly decreases both the azimuth error and the position error growth rate when driving in urban canyons where the GPS signals are blocked. Finally, the proposed system was tested on a real road trajectory data for a metropolitan area. The results demonstrate that the accuracy of the whole system improved, especially during the GPS outage periods.

The Korean Multipurpose Satellite-5 (KOMPSAT-5) launched on 22 August 2013 was equipped with a global positioning system (GPS) receiver for precision orbit determination (POD). Even though the GPS receiver of KOMPSAT-5 shares the same heritage as the BlackJack receiver onboard in Gravity Recovery and Climate Experiment (GRACE) satellites, KOMPSAT-5 has a lower orbital position accuracy (~10 cm) compared with GRACE (~2 cm). The reduced dynamic and kinematic methods are applied for POD of KOMPSAT-5 and GRACE to investigate the GPS observation quality due to the satellite operation concept and hardware design. The results are analyzed in terms of the number of observations and their spatial distribution, GPS signal quality, and orbital position accuracies. The results show that the frequent attitude maneuvers of KOMPSAT-5 affect the quality of the GPS signals and solutions obtained from the kinematic method compared with that determined from the reduced-dynamic method. The onboard patch GPS antenna installed in KOMPSAT-5 and its geometrical position resulted in more erratic measurement residuals by 140% compared with the choke ring antenna of GRACE. The POD accuracy is dependent on the hardware design and regular attitude tilting for the synthetic aperture radar (SAR) imaging even though the same GPS receiver performances.

This paper presents a physics-based, multifrequency, strong scintillation simulator that requires only two input parameters: the S4 index and decorrelation time t0. This simulator is developed based on the two-component power-law phase screen model, which is specified by five parameters and suitable for representation of strong equatorial scintillation. By setting three of the model parameters to representative values, numerical mappings from the user input parameter set (S4 and t0) to the remaining model parameter subset of two parameters can be established through numerical evaluation. The numerical mappings are then used to drive the scintillation model to generate time series of scintillation amplitude and phase. A large group of strong scintillation data (with S4 > 0.6) from two equatorial sites are processed to obtain the representative values of the three model default parameters and to validate the numerical parameter mappings. A MATLAB implementation of the scintillation simulator has been made available.

Modernized Global Positioning System (GPS) and other global navigation satellite systems (GNSSs) have improved upon the legacy GPS waveform in terms of multipath, correlation, and overall accuracy and availability performance. This chapter provides details of the legacy GPS signals, and the modernized GPS signals. A discussion on the Global Orbiting Navigation Satellite System (GLONASS), Galileo, BeiDou Navigation Satellite Systems (BDS), Quasi‐Zenith Satellite System, and the Indian Regional Navigation Satellite Systems follows. The GPS is a code‐division multiple access (CDMA) satellite‐based ranging system. It has two basic services that are provided by the legacy GPS: the standard positioning service and the precise positioning service, with documented performance standards. Like GPS and GLONASS, the Galileo GNSS is an all‐weather, 24‐hour satellite‐based navigation system designed to provide various services, and most of the Galileo satellites transmit their navigation signals using a passive hydrogen maser clock.

Ground-based augmentation systems (GBASs) are widely used augmentation systems using satellite navigation. GBAS can improve the performance of BeiDou navigation satellite system (BDS) in civil aviation application. Evaluation is necessary before using a GBAS. On the basis of the flight trials conducted at the Dongying airport, evaluation results show that based on both the global positioning system (GPS) L1 signal and BDS B1I signal, ground accuracy designators (GADs) of four reference receivers are consistent with GAD-C levels. The improvement in accuracy obtained with GBAS differential process is ∼60%, and the differential position error based on the BDS B1I signal is 40% greater than that of the GPS L1 signal, because currently incomplete BDS satellites distribution is worse than that of GPS. Airborne protection levels assessments show that the approach trials based on the BDS B1I signal exhibit several false alarm events. Further analysis indicates that BDS vertical protection levels were larger because of larger pseudoinverse S result from low elevation angle and unsatisfactory geometric distribution of BDS satellites. Besides, monitors show that BDS B1I signal is more affected by ionospheric delay than GPS L1 signal, which can also contribute to false alarm events.

Modern aviation safety increasingly depends on reliable GPS services, while signal degrading effects such as multipath and masking often occur during critical flight phases, such as takeoff and landing. In this regard, we propose multireceiver direct position estimation (MR-DPE), which operates a network of DPE receivers to enhance GPS accuracy under degraded signal conditions. A DPE receiver directly estimates navigation solutions in the position–velocity–time domain with a maximum-likelihood approach, bypassing the intermediate range measurements. Whereas prior works have shown the enhanced accuracy of DPE with weak signals, MR-DPE provides further improvement by leveraging the information redundancy and the geometric diversity provided by the network of receivers and antennas. We implemented MR-DPE using software-defined radio and tested it with simulated GPS signals to show improved GPS accuracy under degraded environments. We conducted comprehensive, full-scale flight experiments, a first for DPE-related works. A wide range of flight profiles was explored and analyzed, especially those prone to signal multipath and masking, thus validating the claimed benefits of MR-DPE in GPS accuracy.

In order to provide a robust estimate of vehicle position in all environments, especially, in challenging urban areas where GPS signals are blocked, a fusion framework based on sparse Gaussian–Wigner prediction (SG-WP) is proposed. This new approach combines the advantages of both the random matrix theory and the sparse property to provide enhanced vehicle localization capabilities. In this method, measurement noises are assumed to be non-Gaussian distributed, and a generalized error distribution is adopted as an approximation to non-Gaussian densities. To ensure the robustness and the stability of the proposed approach, road-test experiments in various scenarios, including free, partial, and complete GPS outages, were performed based on the geometric dilution of precision metric. During complete outages, the SG-WP fuses all available INS measurements to improve the vehicle position prediction, whereas in free outages, only GPS information is processed. Besides, information from both GPS and INS are taken as inputs during partial outages, and the slide window is then introduced to regulate the flow data. The experimental comparison with the existing prediction methods reveals that the proposed method can achieve accurate and reliable positioning for land vehicles in all considered environments when the measurement noises are Gaussian or non-Gaussian distributed.

With GPS/Global Navigation Satellite System (GNSS) receivers exposed to greater levels of Radio Frequency Interference (RFI), a potential problem is the saturation of the receiver front end (FE). This problem is further complicated in typical multifrequency receivers by the interfrequency saturation effect. Specifically, any inband RFI targeted to induce FE saturation at only one specific GPS/GNSS frequency, if not properly handled, would potentially impact the reception of the other frequencies. This paper presents the design of an antenna module to detect, identify, and isolate potential RFI to prevent FE saturation including that due to the interfrequency effect. Analysis showed that any specific antenna module configuration with fixed internal components must sacrifice noise figure (NF) performance to increase robustness to RFI saturation, and vice versa. To provide a compact solution to this dilemma and the interfrequency saturation issue, two dynamically configurable antenna modules based on the concept of network topology were proposed for typical dual-frequency GPS/GNSS receivers. Such a solution can adapt to different RFI conditions by operating in corresponding modes resulting in a better NF versus robustness tradeoff. The proposed antenna-module design was validated by experiments with live GPS signals under controlled RFI conditions.

El impacto producido por las capas de la atmosfera sobre la señal emitida desde los satélites han sido analizados y estudiados con gran importancia ya que es posible evaluar el efecto producido sobre las señales. Una de las capas de acuerdo a su composición y características que atenúa gravemente la señal a altas frecuencias es la troposfera. En el caso del sistema de posicionamiento global (GPS), su señal sufre un retardo al atravesar la atmosfera durante el intervalo de tiempo de transmisión. El 90% del retardo total es debido a los componentes húmedo y seco de la capa troposférica. El retardo troposférico depende directamente de la presión atmosférica, la cual se ve influenciada por el vapor de agua y la temperatura, afectando gravemente la precisión en el receptor en tierra. El objetivo propuesto es estimar el retardo que sufre la señal de GPS considerando las características troposféricas en la ciudad de Riobamba bajo diferentes circunstancias: días con mayor y menor humedad, así como la precipitación acumulada basado en el modelo de Saastamoinen.Se analiza en forma paralela los datos extraídos de la estación GNSS que forma parte de la Red de Monitoreo Continuo GNSS de Ecuador (REGME) para valorar el impacto sobre la precisión en la solución final de posicionamiento del receptor. Por medio de este proceso, es posible determinar cómo las condiciones troposféricas afectan a las señales de los sistemas de comunicación satelital, en este caso evaluando el sistema de posicionamiento GPS. The impact produced by the layers of the atmosphere on the signal emitted from the satellites has been analyzed and studied with great importance since it is possible to evaluate the effect produced on the signs. One of the layers according to its composition and characteristics that severely reduces the signal at high frequencies is the troposphere. In the case of the global positioning system (GPS), its signal suffers a delay when passes through the atmosphere during the transmission time interval. The 90% of the total delay is related to the wet and dry components of the tropospheric layer. The tropospheric delay depends directly on the atmospheric pressure, which is influenced by water vapor and temperature, seriously affecting the accuracy of the receiver on the ground. The proposed objective is to estimate the delay suffered by the GPS signal considering the tropospheric characteristics in Riobamba city under different circumstances such as days with higher and lower humidity, as well as accumulated precipitation based on the Saastamoinen model. The data extracted from the GNSS station that is part of the Continuous Monitoring Network of Ecuador (REGME) is analyzed in parallel to assess the impact on accuracy in the final positioning solution of the receiver. Through this process, it is possible to determine how the tropospheric conditions affect the signals of satellite communication systems, in this case evaluating the GPS positioning system.

The main objective of the European Geostationary Navigation Overlay System (EGNOS) is to improve the positioning accuracy by correcting several error sources affecting the Global Positioning System (GPS) and to provide integrity information to GPS signals for users in real time. This research presents analysis used to investigate improvement in the performance of single-frequency GPS positioning using EGNOS corrections in Algeria. In this study, we performed position measurements with two calculation approaches, the first based on GPS single-point positioning and the second using EGNOS differential corrections. Positioning accuracy was determined by comparison with the known precise coordinates of the sites; and then the improved ionospheric correction using EGNOS was investigated. The results revealed that GPS + EGNOS performance was significantly improved compared with GPS alone, when measurements of horizontal and vertical accuracy were taken into account, and that the EGNOS corrections improved east and north components slightly, and the up component significantly.

Path planning algorithms for unmanned aerial or ground vehicles rely on Global Positioning System (GPS) information for localization in many surveillance and reconnaissance applications. However, disruption of GPS signals, by intention or otherwise, can render these algorithms ineffective. This paper provides a way of addressing this issue by leveraging range information from additionally placed stationary objects in the environment called Landmarks (LMs). The placement of LMs and the route followed by the vehicle is posed as an integer program such that the total travel and LM placement cost is minimized. The proposed formulation of the optimization problem also allows for a limited field-of-view of the sensor on-board the vehicle. For instances that are hard to solve for optimal solutions using the integer program, we present two fast heuristics to find good feasible solutions. We provide a systematic framework and algorithms for the problem, and evaluate the system using numerical, simulation and experimental results.

Global Positioning System (GPS) signals are delayed when passing through the ionosphere due to an ionospheric refraction effect. The ionospheric-free linear combination of GPS signals can eliminate most of the error caused by the first-order ionospheric term. However, the influence of higher-order ionospheric terms remains, and it should be accounted for when conducting high-precision geodetic applications. In this study, we use data from twenty-one GPS continuous operating stations from different observing networks distributed in Antarctica and analyze the effect of the second-order ionospheric term on GPS positioning during the whole year for 2012. Results show that the effect on these stations is at the level of submillimeters to millimeters, and the effect in summer is several times higher than it is in winter. In addition, this effect is found to be positively correlated to the change of the total electron content over the Antarctic continent. On the other hand, all of the stations show the southward and upward movements derived from the effect. The common and seasonal displacement trends displayed in the Antarctic region should not be ignored in future high-precision research or applications but should be brought to attention, especially when total electron content in the ionosphere is high.

This paper presents a low-cost based approach for solving the navigation problem of wheeled mobile robots to perform required tasks within indoor and outdoor environments. The presented solution is based on probabilistic approaches for multiple sensor fusion utilizing low-cost visual/inertial sensors. For the outdoor environment, the Extended Kalman Filter (EKF) is used to estimate the robot position and orientation, the system consists of wheel encoders, a reduced inertial sensor system (RISS), and a Global Positioning System (GPS). For the indoor environment, where GPS signals are blocked, another EKF algorithm is proposed using low cost depth sensor (Microsoft Kinect stream). EKF indoor localization is based on landmarks extracted from the depth measurements. A hybrid low-cost reduced navigation system (HLRNS) for indoor and outdoor environments is proposed and validated in both simulation and experimental environments. Additionally, an input-output state feedback linearization (I-O SFL) technique is used to control the robot to track the desired trajectory in such an environment. According to the conducted validation simulation and experimental testing, the proposed HLRNS provides an acceptable performance to be deployed for real-time applications.

In order to ensure that Inertial Navigation System/Global Positioning System integrated navigation system (INS/GPS) can still provide high precision positioning results when GPS outages, a novel hybrid algorithm based on Gated Recurrent Unit (GRU) and interacting multiple model adaptive robust cubature Kalman filter (IMM-ARCKF) is proposed. Firstly, the IMM-ARCKF algorithm is proposed to solve the uncertainty of system model and measurement noise statics in the application of INS/GPS on the road. Then, GRU neural network is introduced into INS/GPS system which includes two modes of training and prediction. When GPS signal can be received, the GRU neural network works in the training mode. When GPS outages, the GRU neural network predicts the GPS position increment. Finally, the effectiveness of the algorithm is evaluated by the experiment and analysis. From the data of the experiment, the proposed algorithm can improve the positioning accuracy during GPS outages.

In December 2018, the BeiDou III (BDS-3) basic system was completed and began to provide global services. along with the BeiDou second-generation satellite navigation system (BDS-2) and Beidou first-generation satellite navigation system (BDS-1), the whole BDS has advantages in the number of satellites, coverage, and positioning performance. This paper focuses on the comprehensive evaluation of BDS by comparing the data quality and positioning performance of BDS and those of Global Positioning System (GPS). The performance evaluation and comparison is based on 7-day observation data collected in April 2020 at three different geographical locations in China. The experimental results show that GPS and BDS have a number of significant differences. The number of visible satellites in BDS is twice that of GPS in China. The SNRs of GPS signals of different frequencies are quite different from each other, while BDS signals of different frequencies have very similar SNR observations. BDS-3 and GPS achieve similar positioning performance in terms of internal positional error, external positional accuracy, coordinate time series and Dilution of Precision (DOP). The mean and STD of the internal positional error of GPS and BDS are less than 3 m, while the external positional accuracy of both systems is better than 5 m. It is also observed that the overall variation in coordinate time series of the three coordinates of BDS is slightly smaller than that of GPS. In addition, the BDS DOP is basically equivalent to the GPS DOP, although occasionally the former is greater than the latter. The results are largely contrary to most of the results associated with performance comparison between BDS and GPS reported in the literature. The main reason would be that the number of visible satellites, observations and DOP of BDS are superior to GPS and single BDS-1, BDS-2 or BDS-3 in China. We believe that the development of BDS is still in progress and BDS is making continuous performance enhancement.

The indoor location technique plays a essential role during the application of quadrotor unmanned aerial vehicle (UAV). However, the control design problem for the quadrotor UAV is quite difficult in the indoor environment due to the weak GPS signal. Based on Ultra Wide Band (UWB), the related positioning issues can be solved of UAV through base station with known coordinate position and equipment with location tag, but it is difficult to meet the high-precision operation requirements. In this paper, an indoor positioning design method combined with the Inertial Measurement Unit (IMU) and UWB positioning technology is proposed, which can effectively suppress the error accumulation of the IMU and further improve the positioning accuracy. Moreover, the system architecture for a class of quadrotor UAV is designed. The multisensor fusion technology based on unscented Kalman filter (UKF) is used to avoid neglecting the high-order terms of the nonlinear observation equations of UWB and IMU, which can effectively improve the accuracy of solving the nonlinear equations. Finally, a hardware-in-the-loop simulation platform is designed to verify the effectiveness of the indoor positioning method and improve the positioning accuracy.

The systems belonging to the European Position Determination System (EUPOS) are obliged to have a sub-service for automatic post-processing (APPS) of static satellite user observations based on observations registered by reference stations. The optimization of the number of tie vectors to reference stations is determined by the authors of APPS algorithms and is a key issue. The study validates the determination of the position of a point based on a smaller number of tie vectors than for the standard solution provided by a sub-service on an example of APPS of the Polish Active Geodetic Network (ASG-EUPOS). It was shown that the reduction of the number of tie vectors by one does not result in a significant deterioration of the accuracy of the obtained coordinates. The other objective of this study is to propose a method for comparing 3D sets. This method is based on 3D figures of assessment and a modification of a method for comparing text sets. The proposed method was validated by comparison with the Spearman correlation coefficient and Mahalanobis distance. The usefulness of the method for similarity and dissimilarity analyses of 3D sets has been demonstrated with an example of comparing the combination of reference vectors with a standard solution of APPS of ASG-EUPOS.

International audience; This paper presents an innovative circularly-polarized compact cavity antenna based on metasurfaces; the proposed design is explained starting from a linearly-polarized antenna based on similar concepts. The main objective is to cover three different GNSS systems, namely Galileo E1, GLONASS G1 and GPS L1, with a single antenna embedded in a metallic cavity. The aperture dimension is set to 0.26λ0×0.26λ0, with a central frequency of 1578 MHz. Loading the aperture with a metasurface allows an efficient radiation within such a small aperture size. Our experimental results are in very good agreement with the simulations, with an axial ratio lower than 2 dB between 1540 MHz and 1655 MHz.

In previous works, methods were explored for position estimation utilizing satellite-borne signals of opportunity, mainly the global positioning system (GPS). The GPS signal was exploited for use in a multistatic passive coherent location (PCL) system. The GPS signal is especially attractive for PCL applications because of the native capability to produce position and velocity estimation. This paper examines the signal specifications for PCL implementation and explores the potential limitations of the proposed solutions. GPS specific methods are developed for multistatic PCL velocity estimation in a three-dimensional plane. The method developed is combined with previously completed work of GPS PCL position estimation for a complete system design in range and Doppler. The PCL system is evaluated against conventional GPS position estimation and velocity estimation and proves to have comparable metrics of performance. Analysis and simulation are performed for verification and validation of the developed methods.

In the future, the modernized power grid, or smart grid, will utilize devices called phasor measurement units (PMUs) to continuously monitor the power grid state in real time. These devices utilize GPS to synchronize the voltage and current phasor measurements across the continental network; however, because the civilian GPS signals are unencrypted, PMUs are susceptible to GPS spoofing attacks. We propose a spoofing detection algorithm using a wide-area, hierarchical architecture. In the network, each PMU transmits conditioned signal fragments containing the military P(Y) signal, which serves as an encrypted signature in the background of all authentic GPS signals. This signature is then verified among a subnetwork consisting of a select number of well-dispersed receivers. We subsequently compare representative signals generated for each subnetwork in order to detect coordinated attacks against the subnetwork receiver collection. Using real-world data recorded during a government-sponsored, live-sky spoofing event, we demonstrate that our algorithm successfully evaluates the authenticity of a widely dispersed receiver network.

In an age when people spend most of their time indoors and smartphones become a necessity, there is an increasing demand to navigate user absolute position in indoor environments. While global positioning system (GPSs) perform well outdoors, their inaccuracy can not be tolerated in places where GPS signal is weak or barely detected. This leads to a number of solutions which utilize smartphone inertial measurement unit (IMU) to track user location. Most IMU-based methods track the trajectory of a person by using stride-length and heading estimation. Thus, the accuracy of stride-length estimation plays a very important role in these methods. Inspired by recent success in the field of computer vision and machine learning, we proposed an image-based stride-length estimation method that employs gramian angular field (GAF) in converting accelerometer data into images, and then feed them into a convolutional neural network (CNN) to predict the stride-length. We evaluate the performance of our proposed method by using a public dataset from Qu Wang in his GitHub repository (available at https://github.com/Archeries/StrideLengthEstimation). The result shows that our proposed method is superior in terms of accuracy in one stride and in large walking distance than others using only data collected from the accelerometer.

This paper assesses the feasibility of using Passive UHF Radio-Frequency Identity (RFID) tags to augment the existing technologies for Autonomous Vehicle (AV) limitations such as camera occlusions, traffic-sign tempering, and spotty GPS signals interrupting AV localization. The study finds the Received Signal Strength Indicator (RSSI) and the number of tag reads in various propagation mediums such as Ice, Water, and Snow with single and dual-antenna configurations. A reader was mounted onto the AV, and readings were collected by driving it over the tags. Experiments were conducted with an AV on a test track with speeds up to 90 km/h. Due to the track constraints, speeds higher than that could not be tried. One research, however, was able to read the Passive tags up to 200 km/h. This paper finds that the technology in its current form is suitable for controlled indoor and confined environments. However, if more antennas and readers are experimented with - and more Passive RFID equipment is developed for AVs, it could show promise against the current AV limitations.

A method has been developed for calibrating the time intervals of video recorders using a LED panel known as “SEXTA”, which utilizes Global Positioning System (GPS) signal as time reference. This method directly measures the frame intervals frame by frame down to a time resolution of 2 ms of the recordings by video recorders equipped with CCD or CMOS sensors in global shutter or rolling shutter modes respectively. The method has been validated by using a Digital Single Lens Reflex (DSLR) camera and a previous studied dashboard camera. Frame by frame analysis of the calibrated video recording of SEXTA can reveal characteristics of sensors, such as type of sensors (CMOS vs CCD), exposure time, the direction and speed of rolling shutter for CMOS sensors as well as the common characteristics of dashboard cameras such as frame skipping.

GPS signals in dense urban areas usually get blocked by buildings or tunnels, making the localization task challenging. Therefore, robust positioning strategies are needed for safe autonomous driving operation. This paper proposes a localization framework for dense urban areas that includes a robust alternative localization algorithm based on a computationally low demanding map matching algorithm. The approach includes an error detection module and a decision layer to determine the most reliable source of positioning: GPS, map matching algorithm or local positioning algorithm. The approach is validated on a simulation environment, presenting an average mean positioning error of 3. 11m for the longitudinal axis and 0. 629m for the lateral axis, in a wide range of different variable failure scenarios.

This paper analyzes the surface reflectivity (SR) over time of GPS L1 signals scattered off a partially frozen lake surface. The SR for a given GPS satellite is compared with the mean red value (MRV) of pixels inside the Fresnel zone taken by collocated cameras, with the MRV serving as a truth reference for water (low MRV) or ice (high MRV). GPS signals are collected during a data campaign at Lake Michigan in Chicago, at a time when the lake surface consisted of a mixture of ice and water. A portable ground-based sensor suite is set up at the lake front to collect both surface-scattered GPS signals and independent validation data (lidar and camera) from the surface. The camera pixel locations and the Fresnel zone of the GPS satellites are mapped into an east-north-up (ENU) coordinate system centered at the reflected antenna. The MRV of the Fresnel zone is computed for each satellite. Satellites whose Fresnel zones sweep across both water and ice over the lake surface are selected. To process the GPS reflected signals of chosen satellites, a software defined receiver is customized. In order to find the SNR, one ms coherent correlation power is summed incoherently for one minute for reflected signals. The distance from the satellites to reflection point (Fresnel zone) and from the reflection point to the reflected antenna are computed in order to estimate the SR. Then the SR is correlated with the mean red value. High correlation is seen between SR and MRV for PRN 26, while for PRN 16 only low-to-moderate correlation is found. The low correlation is attributed to a lack of overlap between the Fresnel zone of PRN 16 and the cameras’ fields of view.

Irregularities in the ionosphere modulate the amplitude and phase of trans-ionospheric satellite signals, causing signal fluctuations known as scintillation. The scintillation of global navigation satellite system (GNSS) satellite signals can impact tracking loop performance and degrade positioning accuracy. Cycle slips and outages are manifestations of tracking loop impacts which are known to correlate with the scintillation index S4 and other statistical measures of the fluctuating ionospheric channel. The purpose of our study is to compare and validate theoretical models of cycle slip and outage rates using high-rate scintillation measurements at low latitudes. We detrended the high rate amplitude and phase measurements ourselves, and counted cycle slips and data gaps. We calculated the S4 index and intensity decorrelation time directly. We used the maximum likelihood (ML) technique to fit the ?-µ model to the distribution of signal fades. This procedure was performed using data from two different receiver models and for each of the three GPS carrier signals L1/L2C/L5. We compared the measured rates of cycle slips and data gaps with three theoretical models, namely the Rician model proposed by Humphreys [1], the Nakagamim model, and the ?-µ model proposed by Moraes et al. [2]. We found that both the Humphreys and ?-µ models yielded accurate predictions of the mean rates of cycle slips and outages. Notably, we observed experimentally the behavior that was predicted theoretically by Moraes et al. [2], namely that for a given S4 value the rate of cycle slips and outages increases with increasing ?. This corroborates the interpretation of ? as a measure of scintillation “severity” for a given S4.

Automatic maneuvering of vessels requires reliable and highly accurate navigation. Therefore, this publication describes the development of a tightly coupled navigation filter used as a state estimator for automated driving for vessels operating in harbors. The filter estimates 3D-position, -velocity and -orientation, bases on inertial quantities and is aided by pseudoranges, deltaranges and carrier phases. In order to achieve high position accuracy carrier phase ambiguity is estimated within the filter states and fixed by LAMBDA. For common-mode error mitigation, differential data from a reference station network is integrated, received via cellular connection. The accuracy of the ambiguity estimate correlates with the accuracy of the height estimate. In harbors, accurate height information is available in the form of sea level. Therefore, this work integrates sea level data into the ambiguity estimation of the navigation filter, and thereby significantly improves time to fix. Frequent cellular service outages can be observed in the harbor, which results in losing high accuracy temporally, or even the ambiguity fix. To enable continuous, reliable high accuracy positioning, a new Kalman filter-based local estimation of differential data is implemented, such that a continuous stream of differential data is provided. Extensive evaluation with an unmanned surface vehicle (USV) in Rostock Harbor, Germany, is performed. Time to fix is evaluated with simple GPS L1/L2C wide-lane carrier phases, as this allows sufficient constellations with more than 4 satellites for navigation. With sea level data, a nearly instantaneous ambiguity fix is achieved (time-to-fix in average 1.5 s). Results show, that the local estimation of differential data allows ambiguity fixing, even if the last received differential epoch is older than 2.5 min, which is an important achievement for autonomous navigation. Over all, a 3D-position accuracy of less than 40 cm is achieved, even with frequently cellular connection outages of several minutes. In phases where outages of only a few seconds occur, a 3D-position accuracy of less than 10 cm is achieved. The implementation bases on GPS and can be further improved in the future by adding Galileo observables.

GNSS (Global Navigation Satellite Systems) interference detection using data from the Swedish CORS (Continuously Operating Reference Stations) network SWEPOS has been investigated. Actual interference events have been detected at these three operative SWEPOS stations using a reference interference detection system named RF Oculus developed by the Swedish Defence Research Agency (FOI). RF Oculus monitors e.g. the received power in the L1 frequency band and detects interference if it exceeds a preset threshold above the ambient noise level. Interference detection, using data from the SWEPOS network, was also performed at these three locations by investigating when the mean carrier-to-noise-density ratio of the tracked GPS L1 C/A-signals was below a determined expected level based on historical data. In total around 50 interference events were detected by the reference systems across the three locations during a period of a couple of months. Different types of interference were identified to affect the mean carrier-to-noise-density ratio from the SWEPOS receivers. This showed that detection of different type of interference is possible using SWEPOS data. Interference with durations as short as below a second were detected. Results from the reference interference detection system and detections based on data from the SWEPOS network showed that most interference events were detected by both approaches. Differences in start time and duration of detected interference events between the two methods can probably be attributed to differences in thresholds and detection methods. On the whole it is shown that the detection approach using the SWEPOS network can provide useful and valid detections.

A tightly coupled GPS/IMU estimation algorithm is developed assuming that all received measurements must first be authenticated by CHIps MEssage Robust Authentication (CHIMERA). CHIMERA is designed to authenticate incoming GPS signals through two methods referred to as the fast and slow channels. This paper analyzes the accuracy of estimation algorithms for both of these channels when using an Inertial Measurement Unit (IMU) to compensate for authentication delay, and it considers the effects of different quality IMUs. This paper also introduces a concept of authentication staggering as a possible approach to improve location and attitude accuracy. The estimation algorithm is modified to account for authentication staggering and different possible estimation architectures are developed for this purpose. The results indicate that the fast channel produces typical GPS navigation accuracy for different quality IMUs while the slow channel has moderately degraded navigation accuracy even with a navigation-grade IMU and highly degraded accuracy with tactical- and MEMS-grade IMUs. Staggering the authentication times of the GPS satellites can be used to improve navigation accuracy for the slow channel.

Antenna arrays and spatial signal processing techniques have proven to be the most effective countermeasures against radio interference (RFI). Beamforming algorithms such as the prewhitening filter estimate the covariance matrix from a finite block of digitized raw antenna signals. Subsequently a scaled inverse of the covariance matrix can be applied to the same signal block to decorrelate the signals and hence, mitigate incident jammers. The application of this algorithms supposes that the narrowband assumption is met, which is the case for compact arrays, such as uniform rectangular arrays (URAs). Through the comparably large size of the latter, a hidden installation in cars produced for the consumer mass market is not possible, which is on the other hand a strict requirement by industry and customers, such that the aesthetic design of the car is not infringed. Therefore, the authors of this paper presented a new array concept consisting of two uniform linear arrays (ULAs), which can be spaced several wavelengths apart from each other. With that additional degree of freedom and the reduced footprint of a ULA compared to a URA, this array allows for a concealed installation in the front and/or rear bumpers as well as the side mirrors of a car. On the other hand, the narrowband assumption is violated through the increased spacing between the ULAs. Therefore each antenna receives the signals with a non negligible delay and, furthermore, a different Doppler frequency, if platform rotations are considered. Both effects decrease the correlation of the impinging signal and hence the beamformer performance. The authors demonstrated the possibility to utilize this new array using signals of the GPS L1 C/A band with a bandwidth of 2 ? 5 MHz. This paper demonstrates measured results of the array under consideration evaluated at a bandwidth of 12.5 MHz as incorporated in the new signals for GPS L5 or GALILEO E5a respectively. A novel approach to improve the performance for wideband signals will be presented which is capable of compensating the aforementioned differential delays as well. The approach will be developed, analyzed and the improvements in terms of interference mitigation capability will be assessed in comparison to the pure spatial state-of-the-implementation. The paper gives an insight into a novel technique for suppressing interference, jamming and spoofing using distributed antennas which is capable of also handling GNSS signals with high bandwidth (E5a/L5). Such signals are considered to be crucial for future automatically or autonomously driving cars. Real measurement data will be used to ensure realistic results and assessments.

Objectives IoT use cases are very demanding in terms of low power and low consumption for design of the objects. The value of a solution is not only given by the raw data reported (pressure, temperature, ...) but more and more by the big data analysis, meaning the combination of all information reported by the sensors and their evolution over time. This leads, for example, to valuable information to predict meteorological phenomenon or the progress of refurbishment of road infrastructure. To that end, the location feature is now commonly embedded in any device whether they are in motion or static. Thus, it is taken for granted that the sensors data come with their position. In most of the case, the object does not require to deal with its position, this information is sent back to a back office that will process it. So, the computation of the position with low power profile and at low cost is a key feature. This can be done by the telecommunication networks with various level of accuracy. The Object can be located with the position of the antenna that received the message and augmented by measurement linked to the power (Radio Signal Strength Information) of the signal received or the time of arrival (TOA) when available. Advanced technics use trilateration to combine the Time difference Of Arrival (TDOA) from several antennas. These implementations are standardized by ETSI 3Gpp. Nonetheless, in terms of accuracy and precision GNSS remains the preferred technology. (It is the one selected to locate emergency call 911 in the USA) Cloud based GNSS computation has blossomed in various ways to get rid of the cold start effect especially because the common use cases require only several acquisitions per days and do not withstand to set the receiver in tracking mode. The implementations come under the name “snapshot GNSS receivers” or “cloud offloaded receivers”. A white paper published by GSA in 2020 describes the architectures taking benefits of the chipset or even using only an RF front end to cut costs. The most efficient solution has the objective to use only a snapshot of few tens of milliseconds of RF samples (see APOLLO research project funded by GSA ) without any dependency on the data contained in the navigation message or on the network. To reach this objective, a method must be developed to identify a coarse position of the object to resolve the ambiguity of the location of the code whether the object is in motion or not. The paper proposed by syntony explains the structure of the reception chain, the issues met and the final implementation with lab test results. Actual results The principle of the algorithm is to restrict to the bare minimum the signal processing on the object. The actual implementation uses as input a limited set of I&Q samples. Typical snapshot duration for GPS L1 C/A is 20 milliseconds. From it, a search in doppler and in PRN code phase is done and the estimated code phase for each PRN found are sent to the server with an associated coarse time tag T. The algorithm in the server identifies a set of candidates ‘nodes’ on earth having in visibility the satellites PRNs found at T by the object. The server then computes the difference between a reference code phase and all the code phases estimated by the connected object. A function F computes, for each candidate node, an error metric gathering for all the visible satellites the gap between on the one hand the theoretical code phase differences corresponding to this node and the list of visible satellites, and on the other hand the code phase differences computed from the RF snapshots. the node corresponding to the optimum of the function F gives the coarse position C. From test samples, first results on ‘roof antenna’ validate the process described above. With 7 to 8 satellites in visibility, a test run on 1000 snapshots taken over a period of 24 hours achieves a coarse position accuracy of 3,8 km for 99% of the points. This coarse position is then the starting point for a fine positioning algorithm able to achieve a metric accuracy. The proposed paper focuses on the coarse positioning which is the key enabler for Syntony solution, making it compatible with any telecommunication network, the fine positioning is thus out of scope of this contribution. Anticipated results Roof top antennas is a ‘best case’ scenario that matches some uses case. The work on going is to refine the algorithm to adapt to more harsh conditions as heavy masking (Urban canyon) while still using the same few amount of data (20 ms snapshot). There are several solutions either to keep a high number of PRN or to add other raw data from the signal processing (e.g. Doppler or geographical exclusion). Our early findings show that one can anticipate reaching a coarse position around 50 km at 99% that still meets the requirements to get a fix. Conclusion For the Iot market, the GNSS is still considered as the best technics should it matches the objectives of power consumption. The most effective solution is to take a snapshot of RF samples, process them to minimize the data stream over the air and compute the PVT in the ‘cloud’. To that end, the PVT algorithm needs a coarse position to solve the ambiguity on each PRN code phase. Although the coarse position could be given by the Cell tower of the network, the more network agnostic the solution is, the more scalable it will be. This study shows that the coarse position can be extracted from the few data sent by the connected Object. Key innovative steps These are : - Minimal amount of data processed by the object (20ms), - Determination of the coarse position without dependency on the given telecommunication network, - Robustness of the algorithm even with harsh condition (5 PRN detected)

The new GPS signal for civilian use, L1C, is a modernized signal designed to enhance various performance aspects for satellite positioning. L1C differs from the currently used L1C/A signal in numerous ways. The L1C codes are ten times longer than the L1 C/A codes. This increased length helps in reducing interference when multiple satellites are tracked at the same frequency. The L1C signal consists of a dataless pilot component and a data component with navigation data. Dataless signals enable more robust tracking under challenging conditions. Furthermore, the L1C signal provides a new CNAV-2 navigation message, which is the first navigation signal using modern advanced forward error correction (FEC) codes. Several studies have compared the performance of the L1C signal with that of the conventional L1C/A signal, but most of them are based on simulations. This is mainly because only the Japanese Quasi-Zenith Satellite System (QZSS) and one GPS Block III satellite launched in the year 2019 actually broadcast the L1C signal. Although few studies evaluated the ranging performance of the L1C signal, they were confined to open-sky environments. In this study, we evaluated the actual ranging accuracy of the L1C signal in forest and urban environments and the decoding performance of the CNAV-2 navigation message compared to the conventional L1C/A signal. This information is the most vital for GNSS users. The main contributions of this paper are as follows: (1) This is the first study to evaluate the actual reception performance of the L1C signal in urban and forest environments. (2) The error rate of CNAV-2 was compared with that of LNAV and CNAV in a real environment. The performance of the QZSS L1C signal was evaluated through static and kinematic experiments in actual forest and urban environments. We compared the performances of the L1C, L1C/A, and L5 signals in terms of the observed signal-to-noise ratio and signal availability in harsh environments, the ranging accuracy using multipath linear combinations, and the actual navigation message demodulation error rate. We employed the software GNSS receiver to evaluate the navigation message demodulation error rate. For this, we implemented the acquisition and tracking algorithms of L1C/A, L5, and L1C signals in the software GNSS receiver and extracted the raw navigation bits. Subsequently, we decoded each navigation message and evaluated the error rate of the navigation message. In the case of L1C signals, CNAV-2 is encoded by low-density parity-check (LDPC) and interleaving. Because the LDPC decoding performance is close to the Shannon limit, it can be expected to demodulate the most stable navigation messages in harsh environments. From the results of the evaluation experiments, it is confirmed that the L1C signal has a lower measurement noise and a higher multipath tolerance than those of the L1C/A signal. In addition, the L1C signal is approximately 2–3 dB-Hz stronger and more robust than the legacy L1C/A signal, and its availability is higher than that of the L1C/A signal in the forest and urban kinematic experiments. The CNAV-2 message in L1C was found to have a lower message error rate than that of the legacy LNAV owing to its data structure and advanced error correction codes.

Due to the fact that civilian GPS signals are currently unencrypted and have publicly available signal structures, all civilian users are vulnerable to spoofing attacks. Seeking to address this vulnerability, the U.S. Air Force Research Lab has proposed the Chips-Message Robust Authentication (Chimera) signal enhancement for the future GPS L1C signal, which allows users to authenticate the received GPS signal every 6 seconds using Chimera’s fast watermark channel. Correspondingly, if a user sought to only utilize the Chimera-authenticated GPS measurements, this would result in a 6-second latency. However, for many moving receiver applications (e.g., autonomous vehicles, UAVs), the receiver often relies on a much faster rate of GPS measurements, at 5-20 Hz. In this work, we propose a method to provide continuous GPS signal verification between Chimera authentication times by using stochastic reachability analysis to validate the received GPS measurement against any self-contained sensor measurement for state propagation (e.g., IMU, wheel encoders, LiDAR, direct control inputs, etc.). Furthermore, we derive our stochastic reachability-based spoofing detector to satisfy a user-defined false alarm requirement in the presence of bounded biases in the error distributions of the self-contained sensor and GPS receiver, during authentic conditions. We demonstrate via Monte Carlo simulations that our spoofing detector probabilistically satisfies a user-defined false alarm requirement throughout the trajectory during nominal conditions, while demonstrating its ability to successfully detect spoofing during a simulated attack. We further demonstrate the ability of our stochastic reachability state estimation filter to successfully bound the true receiver state, during both authentic and spoofed conditions.

The development of a robust navigation infrastructure in cis-lunar space is crucial for the coming new era of advanced lunar exploration. NASA has set a goal to develop an extensible and scalable lunar communication and navigation architecture called LunaNet. For the flexibility and robustness of the system, the architecture is desired to have an autonomous and decentralized operation capability. This paper presents and demonstrates a decentralized and autonomous state estimation algorithm for SmallSats that provides positioning, navigation, and timing service, each equipped with a GNSS receiver, chip-scale atomic clock, and inter-satellite communication module. In the framework, each satellite individually estimates its own state and clock offset with a decentralized Schmidt Extended Kalman Filter by processing weak GNSS signal and inter-satellite range measurements. The algorithm is validated with Monte-Carlo state estimate simulations of five satellites at the hybrid constellation of lunar frozen orbit and the Near-Rectilinear Halo Orbit (NRHO). The proposed filter converged in all Monte-Carlo cases with clock offset errors below 0.15 microseconds and 1.0 microseconds in the frozen orbit and the NRHO, respectively.

NASA aims to bring astronauts to the moon by 2024. According to their lunar exploration program called the Artemis, the spacecraft Orion will be launched by the Space Launch System (SLS) and the Orion will bring astronauts to the moon. Then, they will land on the moon by using the human landing systems (HLS) docked to the Orion or by using the HLS docked to the lunar gateway orbiting around the moon whose components will be launched in 2023. The Japan Aerospace Exploration Agency (JAXA) is one of the international cooperation partners for this magnificent program. Although there are many technical challenges that should be addressed for accomplishing the lunar exploration mission, one of the key issues is the navigation of the Orion and of the supply spacecrafts to the gateway. Since the Orion will fly far beyond the GPS satellites, it will only receive signals coming from those GPS satellites located at the opposite side of the earth and the number of the available GPS satellites will become small. Moreover, the weaker GPS signals will enhance the measurements noises that corrupt the quality of the pseudo range and carrier phase. Following this background, this paper investigates the achievable navigation accuracy using the GPS carrier phases for the moon transfer orbit (MTO) through a series of simulation using actual ephemerides of the GPS satellites, moon, and sun. Starting from the altitude of about 180 kilometers, direct MTO, GPS pseudo ranges, and carrier phases are simulated. Then, the MTO navigation accuracy using the pseudo ranges or the carrier phases was evaluated. According to our simulation results, the navigation using the carrier phases was significantly more accurate than that using the pseudo ranges. The confirmed position estimation errors were below 100 meters until the altitude of about 200, 000 kilometers and below 180 until the moon altitude. The velocity estimation errors were about 1 cm/s until the altitude of about 200, 000 kilometers and became 5 mm/s after about 280, 000 kilometers.

This paper describes the design and evaluation of new GNSS jamming detection methods for position, navigation and timing (PNT) situational awareness (SA). These methods are intended for implementation over large networks of GNSS receivers. We focus on jamming threats caused by personal privacy devices (PPDs). We first derive two new jamming detection tests to identify events of simultaneous drops in C/N0 impacting all satellites in view. To limit the risk of false alerts, we develop an automated process to model satellite-specific and receiver-station-specific C/N0 measurement variations under jamming-free conditions. These models are then incorporated in our new detectors and evaluated using months of GPS L1 C/N0 data from continuously operating reference stations (CORS). Tens to hundreds of events are detected monthly at CORS sites located next to highways. To confirm that the detected events are caused by jamming, we analyze CORS data over multiple days at multiple locations, and find patterns in jamming schedules. In addition, we process ADS-B-reported aircraft receiver data during two known radio-frequency interference (RFI) events that also impacted CORS data.

Positioning, Navigation, and Timing (PNT) supports critical infrastructure with its most known service: Global Positioning System (GPS). The presence of signals broadcast from satellites in Medium Earth Orbit (MEO), results in low signal strength from the receiver making it vulnerable to intentional and unintentional disruptions which could lead to public sector economic and safety losses including transportation services, property damage, environmental damage, injury, and loss of confidence for the system. With guidance from current legislation, the main goal within these field demonstrations is to find a suitable vendor(s) to offer complementary service in the event of GPS disruptions with an emphasis for a scenario-based demonstration rather than a technological based demonstration. The purpose of the demonstration was to gather information on PNT technologies at a high Technology Readiness Level (TRL) that can work in the absence of GPS. Six key constructs were implemented when considering potential stakeholders including timing performance, positioning performance, Timing-terrestrial broadcast, PNT-Terrestrial Broadcast, and PNT broadcast. Across eleven vendors, five timing scenarios were assessed including: 72 Hour Bench Static Timing, Static Outdoor Timing, Static Indoor Timing, Static Basement Timing, and eLORAN Reference Station Offset. Four positioning scenarios were also developed to assess vendor systems based on these five attributes: Dynamic Outdoor Positioning with Holds, 3D Positioning, Static Outdoor Positioning, and Static Indoor Positioning. The purpose of the positioning scenarios was to exemplify: coverage, two-dimensional (2D) dynamic positioning/three-dimensional (3D) dynamic positioning, static positioning including service availability/accuracy, and static positioning under long term service availability/accuracy under normal conditions as well as challenged GPS signal conditions in relevance to multimodal transportation and other critical infrastructure applications From the vendors’ technological demonstration, four key findings were issued and the decision to achieve resilient PNT service across all categories is to pursue multiple technologies while promoting diversity across PNT functions in areas including transportation and further infrastructure sectors.

For wireless systems, interfering signals from the surrounding environment is a serious threat to the performance of the system. A measurement system with the aim to autonomously detect and log interference incidents, particularly in the GPS bands, has been developed and used to collect data. Three measurement systems were placed in various locations at an international airport in Sweden, and have continuously recorded both background noise and interference incidents on GPS L1. In this paper, recordings from measurements performed during one year, starting in November 2019, are presented. Variations in noise levels over both time and between measurement locations have been investigated. All locations showed a diurnal variation in the mean noise power, with slightly higher mean noise at night. In contrast, the number of detected interference incidents were higher during daytime.

GNSS is nowadays used as a primarily timing source for the synchronization of various types of networks and has been defined as the backbone of the connected world and the invisible utility to highlight its pervasive presence in digital infrastructures. Despite this growing dependency on GNSS, network operators rarely perceive the vulnerability of GNSS receivers to interference or jamming as a real problem. The awareness about GNSS spoofing, namely the transmission of false signals with the intent to fool receivers, is low, while in most cases users and stakeholders continue to consider GNSS a non-critical commodity. A stationary GNSS receiver used for timing and synchronization is an ideal target for malevolent attacks because the antenna is static and often sited in a visible location. As an addition to advanced signal processing and use of backup technologies, the proposition of authenticated GNSS signals is seen as a powerful way to increase the resilience of satellite-based time synchronization. Recognizing this fact, the European Galileo program is gradually implementing the OSNMA authentication service, with a test signal already available from some satellites. Today most of the GNSS-based time synchronization systems on the market are built upon low-cost GPS-only, single-frequency modules, which provide PPS accuracy in the range 20 to 100 ns. Under open sky conditions, those modules do provide a stable time reference, but are very sensitive to degradations. With no doubts, the majority of timing receivers are relatively easy to jam and spoof given the low signal level handled at the antenna. Those degradations range from an increase of the PPS jitter due to a reduced number of satellites in view, to a complete loss of the PPS synchronization in the case of jamming of the single GPS L1 C/A signal. Septentrio is introducing the high-end mosaic-T module, a highly secure, resilient, and accurate GNSS timing module for critical infrastructure and mission-critical timing applications. The use of high-end GNSS receiver as source for synchronization first allows decreasing the noise/jitter on the PPS signal thanks to the use of more satellites and signals in the time solution. The multi-frequency multi-constellation positioning also brings an immediate increase of resilience and robustness in case of unintentional interference knocking out a band or a constellation. On top of its AIM+ interference monitoring and mitigation technology, Septentrio is deploying Galileo’s OSNMA support on its product portfolio and network timing synchronisation was identified as critical market for an increased robustness to jamming/spoofing attacks. The presentation reports on jamming and spoofing tests performed in the framework of the ROOT project. We start by a risk assessment of different interference/jamming/spoofing scenarios for network timing synchronization. We will prioritize the different GNSS threats for this industry, with a special attention on the added value of Galileo OSNMA, which is expected to set a new standard on the industry. We then present different test scenarios selected to measure the actual resilience of the receiver. Initial results from this test campaign are already presented, showing the benefit of OSNMA for time synchronisation of networks. Acknowledgment: This work was developed within the ROOT project (www.gnss-root.eu) funded by the European GNSS Agency under the European Union’s Horizon 2020–G.A. n. 101004261.

In this paper, we present a cost-effective software-developed Global Positioning System (GPS) anti-spoofing approach against Time Synchronization Attacks (TSAs) and spatial spoofing. We portray two signal-level spoofing characteristics that are predominantly discovered in aforementioned attacks, namely, spoofing profiles and consistency of modifying signals. While the spoofing profiles, which basically describe the inflicting signal shapes affecting the GPS observables, have already been analyzed in our previous research with respect to attacks against timing, this paper still utilizes the idea, yet implements and showcases the applicability of spoofing profiles in position domain. The extension, in fact, brings the idea of joint TSA and spatial spoofing. To jointly consider the time and spatial domains, the pseudorange and range rate equations are linearized with a non-conventional method. An antispoofing technique capable of withstanding TSAs, spatial attacks on single coordinate, and joint attacks is developed for stationary receiver. To validate the proposed approach, we utilize pre-recorded authentic GPS signals in the TEXBAT database for data transmission, which are captured by the Software Defined Radio receiver developed at UTSA. The algorithm is validated in simulations introducing synthetic spoofing and compared against Weighted Least Squares.

The Unmanned Surface Vehicle (USV) cannot rely on GPS signals for positioning when they pass through areas with weak or disappearing GPS signals such as culverts and bridges in a complex outdoor environment. An USV system based on lidar is designed to realize the positioning and autonomous navigation without GPS. Two-dimensional lidar combined with IMU is used to perceive the surrounding environment, Google Cartographer Simultaneous Location and Mapping (SLAM) method is adopted to perform 2D mapping of the surrounding environment, and the Extended Kalman Filter (EKF) algorithm is used for the fusion of map matching data and IMU pre-integration data. Results of experiments show that the system can achieve stable and high-precision positioning and navigation in narrow inland rivers.

The COVID-19 pandemic has affected the worldwide health and economy eco-system and remains one of the key global disturbances even in the presence. A promising solution partially suppressing the fast COVID spread in the dense areas is the digital contact tracing of the infected persons and those who they encounter. Digital contact tracing is an effective solution, whilst few notable drawbacks must be addressed. In general, we are facing both, technological and legal issues, when reconstructing the precise trajectories of the infected persons. The former one stems from the fact that there is commonly a need for personal intervention (specific application, GPS locator, Bluetooth), the latter one deals with the legal aspects of revealing the personal information to the governments/3rd parties. Technological limitations (battery draining, loss of GPS signal, etc.) limits the accuracy of trajectories, especially in dense urban areas. In this paper, we use the autonomous machine learning framework based on the application of the generative adversarial networks (GAN) that is capable of accurate reconstruction of the person trajectories identifying as far as 20% more contacts. Later, this specific person-sensitive information is processed in the private operators' database and blockchain platform is used to securely store healthiness information. The proposed concept facilitates the retrieval of accurate information on the trail of the COVID transparently, being limpid towards privacy and confidentiality concerns.

Positioning in indoor application is a challenging problem with GPS signals. Because the obstacles such as doors and walls weaken the GPS signal amplitudes, indoor positioning results are not satisfying with the global positioning system. Indoor positioning may be critical for a variety of applications such as detecting the number of people, locating criminals in bounded regions, and obtaining the number of users in a special area. The Wi- Fi signal strength may be a key point to solve this problem. With several routers, the received Wi-Fi signal power information may use to determine the indoor localization using the information of routers location. In this work, Multi-Layer Perceptron (MLP) neural network method is proposed that can be implemented in monitoring and tracking devices. In the end, the theoretical background and simulation results are shared. Both k-fold cross-validation and hidden neuron numbers are changed in the simulation then the results are compared.

Soil moisture is an important geophysical parameter for studying terrestrial water and energy cycles. It has been proven that Global Navigation Satellite System Interferometry Reflectometry (GNSS-IR) can be applied to monitor soil moisture. Unlike the Global Positioning System (GPS) that has only medium earth orbit (MEO) satellites, the Beidou Navigation Satellite System (BDS) also has geosynchronous earth orbit (GEO) satellites and inclined geosynchronous satellite orbit (IGSO) satellites. Benefiting from the distribution of three different orbits, the BDS has better coverage in Asia than other satellite systems. Previous retrieval methods that have been confirmed on GPS cannot be directly applied to BDS MEO satellites due to different satellite orbits. The contribution of this study is a proposed multi-satellite soil moisture retrieval method for BDS MEO and IGSO satellites based on signal-to-noise ratio (SNR) observations. The method weakened the influence of environmental differences in different directions by considering satellite repeat period. A 30-day observation experiment was conducted in Fengqiu County, China and was used for verification. The satellite data collected were divided according to the satellite repeat period, and ensured the response data moved in the same direction. The experimental results showed that the BDS IGSO and MEO soil moisture estimation results had good correlations with the in situ soil moisture fluctuations. The BDS MEO B1I estimation results had the best performance; the estimation accuracy in terms of correlation coefficient was 0.9824, root mean square error (RMSE) was 0.0056 cm3cm−3, and mean absolute error (MAE) was 0.0040 cm3cm−3. The estimations of the BDS MEO B1I, MEO B2I, and IGSO B2I performed better than the GPS L1 and L2 estimations. For the BDS IGSO satellites, the B1I signal was more suitable for soil moisture retrieval than the B2I signal; the correlation coefficient was increased by 19.84%, RMSE was decreased by 42.64%, and MAE was decreased by 43.93%. In addition, the BDS MEO satellites could effectively capture sudden rainfall events.

Over the years, WiFi received signal strength indicator (RSSI) measurements have been widely implemented for determining the location of a user’s position in an indoor environment, where the GPS signal might not be received. This method utilizes a huge RSSI dataset collected from numerous access points (APs). The WiFi RSSI measurements are nonlinear with distance and are largely influenced by interference in the indoor environment. Therefore, machine learning (ML) techniques such as a hidden Markov model (HMM) are generally utilized to efficiently identify a trend of RSSI values, which corresponds to locations around a region of interest. Similar to other ML tools, the performance and computing cost of the HMM are dependent on the feature dimension since a large quantity of RSSI measurements are required for the learning process. Hence, this article introduces a feature extraction method based on dynamic mode decomposition (DMD) for the HMM to effectively model WiFi fingerprint indoor localization. The DMD is adopted since it decomposes RSSIs to meaningful spatial and temporal forms over a given time. Here, the mode forms are analytically reconstructed to produce low-dimensional feature vectors, which are used with the HMM. The localization performance of the proposed HMM-DMD is compared with other well-known ML algorithms for WiFi fingerprinting localization using simulations. The results show that the HMM-DMD algorithm yields a significant localization performance improvement, accuracy, and reasonable processing time in comparison with the state-of-the-art algorithms.

A Global Positioning System (GPS) is widely used as a method of outdoor localization. However, indoor localization system is required because the GPS signals are disturbed indoors. Although the fingerprinting method is a simple and inexpensive method of indoor localization, it is affected by environmental factors such as multipath effects. This paper proposes a Convolutional Neural Networks (CNN)-based localization model that consists of pooling layers for Wi-Fi fingerprinting indoor localization. These pooling layers of CNN can extract features of RSSI that contribute to the localization. Experimental results show that the proposed model reduces MAE about 16%. Furthermore, the coefficient of determination improves by 0.5 compared to the fingerprinting method.

In this paper, a method based on deep reinforcement learning (DRL) is proposed, which allows unmanned aerial vehicles (UAVs) to complete obstacle avoidance tasks only through vision in an environment full of common indoor obstacles. This technology is very important for indoor UAVs, due to the limited GPS signal and overcrowding of obstacles compared to the outdoor environment. We use Variational Autoencoder (VAE) to compress image information combined with the policy-based DRL model to implement the visual obstacle avoidance of VAVs. Simulation experiments have demonstrated that this method can make the UAV master obstacle avoidance in a continuous action space with a fixed direction. Compared with the traditional policy-based DRL visual obstacle avoidance algorithms, it can converge faster.