Galileo: The Added Value for Integrity in Harsh Environments.

This paper evaluates the potential capacity of Receiver Autonomous Integrity Monitoring (RAIM) algorithms for Global Positioning System (GPS) and GPS Aided GEO Augmented Navigation (GAGAN) to support approach and landing phases of flight with vertical guidance. As the traditional RAIM does not support vertical guidance, Advanced RAIM (ARAIM) with potential to achieve worldwide coverage of vertical guidance, is used. These algorithms have focused on the Horizontal Protection Level (HPL) and Vertical Protection Level (VPL). User position is computed by detecting and excluding faults within the navigation solution and position accuracies are also provided. It is found that using RAIM, Position Dilution of Precision (PDOP) mean was good (1.5674) and Position Error (PE) was estimated to be 9.4042m with Fault Detection and Exclusion (FDE) implementation. While on the other hand, to increase horizontal and vertical accuracies, Advanced RAIM algorithm is implemented using GPS and GAGAN data. In case of GPS, Horizontal Protection Error (HPE) mean is 32.8541m and Vertical Protection Error (VPE) mean is 19.9901m. In case of GAGAN, HPE mean is 2.7190m and VPE mean is 3.3010m, thus achieving 95 percent accuracy requirement.

Global Navigation Satellite Systems (GNSSs) have both internal and independent methods to maintain integrity. This chapter discusses two different methods– GNSS‐ receiver autonomous integrity monitoring and use of ground monitoring stations to monitor the health of the satellites, as is done via space‐based augmentation system (SBAS) and the ground‐based augmentation system. SBAS integrity is based on the premise that errors not detected or corrected in the operational environment can become threats to integrity and, if not mitigated, can become hazards to the user. The user must be protected from residual errors that can become threats to the integrity data that could result in hazardously misleading information being transmitted to the user. The SBAS system must compute estimates of the reference receiver clocks and GNSS/geostationary Earth orbit satellite clock errors. The chapter also describes some approaches that may be used to eliminate and minimize data errors, mitigate integrity threats, and satisfy the safety integrity requirements.

GNSS (“Global Navigation Satellite Systems”) applications with high security requirements usually manage integrity with Receiver Autonomous Integrity Monitoring (RAIM). Developed originally for aeronautics, RAIM techniques perform a consistency check on satellite measurements to provide a reliable positioning to the user. Modern versions of RAIM provide high performances for aviation. However, the problematics for terrestrial navigation, especially in urban environments, are quite different and have motivated the development of a new integrity process derived from the RAIM concept. In this paper, we introduce a new detection algorithm based on the a priori coherence between the direct received raw signals and the estimated position provided by the navigator. The use of raw satellite signals allows the detection of more estimation inconsistencies. The major differences between the proposed approach (a priori) and the RAIM approach (a posteriori) are outlined from a theoretical standpoint and illustrated by simulated data. Furthermore, we introduce a new criterion derived from both processes which proves their complementarity. Hence, this paper demonstrates the relevance of such a joint assessment in urban scenarios with degraded signal propagation conditions.

A sparse global navigation satellite system (GLONASS) signal acquisition method based on compressive sensing and multiple measurement vectors is proposed. The nonsparse GLONASS signal can be represented sparsely on our proposed dictionary which is designed based on the signal feature. Then, the GLONASS signal is sensed by a normalized orthogonal random matrix and acquired by the improved multiple measurement vectors acquisition algorithm. There are 10 cycles of pseudorandom codes in a navigation message, and these 10 pseudorandom codes have the same row sparse structure. So, the acquisition probability can be raised by row sparse features theoretically. A large number of simulated GLONASS signal experiments show that the acquisition probability increases with the increase in the measurement vector column dimension. Finally, the practical availability of the new method is verified by acquisition experiments with the real record GLONASS signal. The new method can reduce the storage space and energy loss of data transmission. We hope that the new method can be applied to field receivers that need to record and transmit navigation data for a long time.

In urban road environments, global navigation satellite system (GNSS) signals may be interrupted due to occlusion by buildings and obstacles, resulting in reduced accuracy and discontinuity of combined GNSS/inertial navigation system (INS) positioning. Improving the accuracy and robustness of combined GNSS/INS positioning systems for land vehicles in the presence of GNSS interruptions is a challenging task. The main objective of this paper is to develop a method for predicting GNSS information during GNSS outages based on a long short-term memory (LSTM) neural network to assist in factor graph-based combined GNSS/INS localization, which can provide a reliable combined localization solution during GNSS signal outages. In an environment with good GNSS signals, a factor graph fusion algorithm is used for data fusion of the combined positioning system, and an LSTM neural network prediction model is trained, and model parameters are determined using the INS velocity, inertial measurement unit (IMU) output, and GNSS position incremental data. In an environment with interrupted GNSS signals, the LSTM model is used to predict the GNSS positional increments and generate the pseudo-GNSS information and the solved results of INS for combined localization. In order to verify the performance and effectiveness of the proposed method, we conducted real-world road test experiments on land vehicles installed with GNSS receivers and inertial sensors. The experimental results show that, compared with the traditional combined GNSS/INS factor graph localization method, the proposed method can provide more accurate and robust localization results even in environments with frequent GNSS signal loss.

The combination of multiple constellations of the Global Navigation Satellite System (GNSS) provides a great possibility to improve the integrity of the satellite navigation. However, with the increase of the number of satellites, the probability of multiple outliers increases. Although a number of studies analyzed the performance of the combined GNSS Receiver Autonomous Integrity Monitoring (RAIM), the analysis of the case of multiple outliers is still lacking. In this paper, a Maximum Undetectable Position Bias (MUPB) scheme based on Bias Integrity Threat (BIT), is used to analyze the horizontal availability of RAIM with multiple outliers. The theoretical background for BIT and MUPB has been analyzed. Next, detailed simulations and analyses with single or combined GPS/BDS (BeiDou Satellite Navigation System)/GLONASS constellations were conducted to evaluate the performance of RAIM. In the simulated scenarios with 70 satellites (GPS 32+BDS 14+GLONASS 24) and 2 or 3 outliers occurring simultaneously, this paper compared the MUPB and the Horizontal Alert Level (HAL) of different aviation approach phases and analyzed the results.

Due to the low signal power, the Global Navigation Satellite System (GNSS) signal is vulnerable to interference and even cannot be captured or tracked in harsh environments. As an alternative, the Low Earth Orbit (LEO) satellite has been widely used in the navigation field due to the advantages of low cost and strong signals. It is becoming a significant component of the new combined navigation system with GNSS. The combination of an LEO Doppler signal and GNSS observables can improve the positioning accuracy and high-precision positioning convergence time of the GNSS receiver. However, the GNSS signal receiving capability cannot be improved from this data fusion level. We propose a novel assisted structure where GNSS signal acquisition and Doppler tracking are assisted by LEO Doppler positioning. The receiver uses the LEO signal to achieve Doppler positioning firstly. Then, the coarse position with the GNSS navigation messages received from LEO, as well as the estimated clock information, is used to assist in the acquisition and tracking of GNSS. In this way, the GNSS receiver’s sensitivity can get the benefit from this integrated system. The paper presents the structure of the assisted receiver and analyzes the assisted GNSS signal acquisition and carrier tracking performance in detail. Simulation experiments of this assisted structure are carried out to verify its superiority of acquisition and tracking sensitivity in comparison with standalone GNSS receivers. Theoretical analysis and experimental results show that the proposed acquisition method can achieve 90% detection probability at a carrier-to-noise ratio (C/N0) of 15 dB-Hz, which is about 8 dB higher than the conventional acquisition method without assistance; the proposed tracking method can track weak signals of 5 dB-Hz, which is about 4 dB higher than the conventional method. Therefore, this novel LEO-assisted receiver has significantly improved weak signal acquisition and tracking sensitivity.

The traditional measurement of signal bandwidth using single-carrier frequency sweeping is not suitable for global navigation satellite system (GNSS) signal. A new analysis technique of signal bandwidth with digital signal processing technique has been developed. After the GNSS signal is highly sampled, the spectrum of the actual GNSS signal and the ideal signal generated locally are evaluated in digital domain. The bandwidth of GNSS signal can be calculated. Simulation has been done for the proposed measurement technique using spectrum evaluation algorithm of the Periodogram and the Welch respectively. The simulation results shows that the GNSS signal bandwidth can be accurately estimated after the ideal and actual signal spectrum are evaluated and calculated using the Welch. The simulation results show that The estimating accurate of signal bandwidth can be higher than 99.9% in the case of the proper parameters have been set for Welch.

Modern localization relies on satellite and terrestrial infrastructures (e.g., global navigation satellite system (GNSS) and crowd-sourced Wi-Fi), supporting critical applications ranging from autonomous navigation to life services. However, they are susceptible to multimodal location spoofing attacks, where various types of wireless signals are crafted in a coordinated manner to deceive the receiver position. Such attacks can cause severe disruptions, such as misguiding vehicles and enabling fraud. Although recent deep learning methods help detect GNSS spoofing and jamming, they require extensive labeled datasets, they are not designed for multimodal location spoofing, training is computationally intensive and centralized, thus raising privacy concerns due to the sensitive nature of location data. We propose FedRAIM, a novel federated learning framework designed for robust, efficient, and privacy-preserving multimodal location spoofing detection. The key FedRAIM elements are: training labels are autonomously generated through an extended receiver autonomous integrity monitoring (RAIM) algorithm, incorporating diverse opportunistic information, i.e., terrestrial infrastructure signals, onboard sensor data, and naturally GNSS signals. During federated training, local long short-term memory (LSTM) models are trained independently on client devices. Server then aggregates, using the FedAvg algorithm, these local model parameters, not data, into a global model, which is subsequently distributed back to the clients for iterative training. Experimental results on real-world multimodal spoofing datasets demonstrate that FedRAIM achieves an area under the curve (AUC) of up to 98.3%.

The Global Navigation Satellite System (GNSS) signals are often blocked or interfered in complex geographical or electromagnetic environments, which may make GNSS receivers unable to provide satisfying navigation and positioning services. There have been many ground-based or space-based GNSS augmentation systems to improve the resilience of GNSS positioning, of which most of them rely on additional infrastructures. In this study, a smartphone-based tightly-coupled positioning method was developed using the images from a build-in monocular camera and GNSS signals. In this method, the feature points with the known coordinates are regarded as 'visual pseudolite' and the distance between the camera and the feature points was calculated according to the photogrammetry approaches and used to estimate the user positioning with GNSS signals. The experimental results showed the feasibility of the tightly-coupled positioning algorithm and reached the positioning accuracy of ±5.56 m (1?), which is significantly higher than that of GNSS-only and vision-only positioning solutions.

In order to foster the development of Global Navigation Satellite Systems (GNSS) for land navigation services, there is a pressing need for providing a trust level of the localization solution, especially for liability critical applications. In view of such need, integrity monitoring aims to compute protection levels that successfully bounds positioning errors in nominal conditions. Conventional Receiver Autonomous Integrity Monitoring (RAIM) algorithms have been widely used for integrity monitoring especially for aircraft navigation. Conventional RAIM starts out by supposing a Gaussian measurement errors model with known means and variances. However, this assumption does not hold in dense urban environment. Therefore, we propose novel algorithms that dispenses with this classical assumption. We use external information provided by a 3D GNSS simulation to characterize GNSS errors in urban areas. We propose two approaches for protection levels computation based on comparison between predicted 3D pseudo-ranges bias and computed thresholds. These approaches give a new vision on integrity control based on indicators on the pseudo-ranges bias. Experimental results show that proposed algorithms give an acceptable success rate on integrity monitoring in a harsh areas.

With the ever-increasing number of small satellites launched into space, on-board orbit determination is becoming more and more important. Precise satellite positions are needed for constellation maintenance, formation flying, communication optimization, collision avoidance and Earth observation. In addition, accurate time information is required for the functioning of the instruments on board. Low-cost off-the-shelf Global Navigation Satellite System (GNSS) receivers and antennas, adapted to the small size and weight of the satellite, allow precise on-board orbit determination and time synchronization for even the smallest satellites.We analysed more than one year of on-board navigation solutions (NAVSOL) from 8 Astrocast CubeSats, which are equipped with our multi-GNSS single-frequency payload. Based on the residuals from a dynamic orbit fitting, we found that the quality of the NAVSOL from the Astrocast satellites varies strongly both over time and between satellites, with an RMS ranging from a few meters to several tens of meters.In this study, we focus on three main effects that influence the quality of the NAVSOL from the Astrocast satellites: (1) The orbit-fit residuals show a positive radial offset in the range of about 6 m to 15 m, which is caused by ionospheric refraction. Our data show that GNSS signals at negative elevations are very strongly affected by the ionospheric refraction and thus account for the major part of the radial offset. (2) Discrepancies between the NAVSOL positions and velocities, and simulations with a GNSS signal generator show a once-per-revolution periodicity in the out-of-plane component, with amplitudes of 2 m and 5 cm/s in position and velocity, respectively. This effect is probably due to an inappropriate dynamic model used in the real-time navigation algorithm of the GNSS receiver. (3) Depending on the location of the GNSS antenna on board the satellite, the noise level varies by a multiple. This is assumed to be due to the antenna orientations, interference of the GNSS signals with electromagnetic waves coming from the satellite platform, signal attenuation during the transmission from the antenna to the receiver and differences in the soldering or tuning of the individual GNSS antennas.These findings help us refine hardware configurations and firmware settings in preparation for future Astrocast satellites or other nanosatellite missions. By eliminating or minimizing the mentioned effects, we expect to consistently achieve an accuracy on the level of a few meters for the real-time navigation solution of commercial off-the-shelf single-frequency GNSS receivers in space.

In recent years, the Global Navigation Satellite System (GNSS) technology has been undergoing a process of modernization and expansion.The emergence of new systems and the need for stringent accuracy levels triggered the development and launch of new satellites able to broadcast new civil signals.For GNSS users, the ionosphere can bring the most significant propagation errors.Among the disturbances due to the ionosphere is scintillation, which results from rapid phase and amplitude fluctuations of the wave caused by variations or inhomogeneities in the refractive index of the propagating medium [1,2].Ionospheric scintillation causes fading of the signal received power because of signal scattering by irregularities at the ionosphere layer [3].Lower L band frequencies are more susceptible to ionospheric scintillations than higher frequencies, so GNSS signals are prone to be affected by scintillations.The reduced signal amplitude (fading) can affect the receiver's ability to track the signals, thus causing losses of lock (LOL), resulting in navigation failures and increased Dilution of Precision (DOP) values and affecting the availability requirement.LOL can last several minutes, jeopardizing the use of GNSS on safetycritical operations such as aircraft navigation.In equatorial and low-latitude regions, the ionosphere has behaviors that accentuate the impacts on GNSS signals.In addition to sporadic events such as the occurrence of geomagnetic storms, these regions are characterized by the occurrence of phenomena such as the Equatorial Ionization Anomaly (EIA) and Equatorial Plasma Bubbles (EPB) [4,5,6] reinforcing the scintillation effects.In Brazil, the Space Geodesy Study Group (GEGE, Portuguese acronyms) has maintained an active GNSS network named GNSS-NavAer, equipped with Ionospheric Scintillation Monitor Receivers (ISMR) and provides an extensive database with high-rate recorded data.Thus, this work presents a comparative analysis of the amplitude scintillation considering all the signals and constellations (GPS, GLONASS, Galileo, BeiDou and SBAS) tracked by the GNSS-NavAer receivers.Therefore, in the high activity period of the solar cycle 24, we selected 29 days between 2013 and 2015, characterized by strong amplitude scintillations (S4 > 0.7) occurrences.We used S4 data from four stations (PALM, PRU2, SJCU and POAL) located at different geomagnetic latitudes to contemplate varied behaviors of the ionosphere.The preliminary results showed that the E5b followed by L5 signals are more susceptible to ionospheric amplitude scintillation after sunset.In terms of the constellations, Galileo signals suffered more drawbacks caused by ionospheric scintillations than signals from other constellations (GPS, GLONASS and Beidou).However, Galileo signals showed the best robustness against amplitude fluctuations during the daytime.E5a and E5b signals showed the greatest similarity in response to ionospheric interference, reaching a 99% correlation between S4 values.

The global navigation satellite system (GNSS) signal modulation type plays a crucial role in determining the performances of positioning, navigation and timing (PNT) services. Currently, the binary offset carrier (BOC) modulation signal and binary coded symbol (BCS) modulation signal are both bipolar signals, which greatly restrict the room of improving the GNSS signal performance. Therefore, multi-level coded symbol (MCS) modulation has received great attention in the field of GNSS signal design. The MCS modulation is the most extensive step-coded symbol modulation mode, where BOC modulation and BCS modulation are its special cases. Since the waveform symbol of the MCS modulation signal can be arbitrarily valued, the optimal GNSS signal can be designed. However, like the BOC modulation signal, the MCS modulation signal also has the problem of ambiguous tracking, and then results in a large pseudo range measurement error, which is unacceptable for the new generation GNSS with high accuracy. In recent years, the unambiguous tracking of GNSS signals has become a hot research subject in the navigation signal processing domain and many methods are presented, and those methods can be divided into three categories:BPSK-like method, bump jump (BJ) method, and side-peak cancellation (SC) method. However, these methods are designed for BOC signal, and they are not suitable for MCS signal. Therefore, in this paper we propose a general unambiguous tracking algorithm based on the pseudo correlation function (PCF), which is suitable for MCS modulated signals. Firstly, the unitary expression of MCS modulated signal based on waveform value vector is given, then the unitary formula of cross-correlation function for MCS signal is derived and the definition of PCF is given. Then the constraint condition which should be satisfied to realize unambiguous tracking is analyzed in depth, and the universal constructing method of two reference signals and the relationship between each other are derived according to this constraint condition, which brings great convenience for solving the specific MCS signal. The code tracking loop model of GNSS receiver based on the proposed method is illustrated. It is observed that the proposed method can receive different MCS signals under the same receiver loop framework, and can simplify the design of the receiver while eliminating the tracking ambiguous problem. Finally, as a special case of MCS signal, the applications of the proposed method in four kinds of BOC signals are discussed respectively, and then the waveform value vector of the reference signal and the unitary expression of code discriminator are derived. Simulation results show that the proposed method can effectively solve the ambiguous tracking problem of MCS signal, which has good performance and broad application prospect.

As the strength of global navigation satellite system (GNSS) signals is very low, they are vulnerable to interference and susceptible to attacks motivated by economic, military, and security reasons. These threats are gradually increasing. The most common attack is jamming, in which a strong signal is used to make a receiver miss the GNSS signal. As attacks become more sophisticated, they are expected to evolve to spoofing interference, in which the receiver is deceived. Spoofing interference is a larger threat because the receiver cannot recognize that they are being targeted by an attacker. For this reason, it is becoming more important to monitor GNSS signals so that they can be evaluated in terms of reliability. In this paper, we analyze spoofing detection methods with respect to the navigation solution, measurements, and navigation messages obtained by a receiver. We propose an advanced detection method for overcoming the limitations of each individual detection algorithm. The proposed methods enhance the performance of spoofing detection and reduce the false alarm rate. This research can be applied directly to GNSS signal monitoring systems and will be helpful for enhancing the stability of satellite navigation systems.