Carrier Phase Differential (CPD) HARAIM Evaluation for UAS Surface Navigation

Global navigation satellite system (GNSS) plays a crucial role in many fields, such as aerospace and transportation. Integrity is the measure of trust used in GNSS positioning especially in safety-critical applications. Advanced receiver autonomous integrity monitoring (ARAIM), taking full advantage of multi-constellation GNSS, shows huge potential to provide vertical navigation in civil aviation en route navigation and terminal approaches. However, the multi-constellation ARAIM also greatly exposes computational complexity and potential performance hazards in fault modes determination and fault-tolerant positioning. From the perspective of integrity risk control, rather than the pursuit of better positioning accuracy blindly for safety-critical applications, the concept of constellation dynamic selection is proposed and implemented in ARAIM and the performance analysis is discussed in this paper. Only the best two constellations which have the best vertical geometry performance are involved in ARAIM calculation anytime anywhere. The proposed method shows superiority in both integrity availability and computational complexity in both simulations and actual GNSS signal experiments. While the computational complexity is less than 10% of that using four constellations, 100% availability under LPV-200 criteria can be achieved in worldwide coverage experiment. The proposed method also overcomes the shortcomings of ARAIM with two fixed constellations and shows good robustness under depleted scenarios. Furthermore, the statistics results from observation stations proved the applicability and generality of the proposed method under current developing GNSS constellations.

Ground-based GNSS ZTD/IWV estimation system for numerical weather prediction in challenging weather conditions

Acousto-optic devices using lead molybdate and related compounds: Bell Telephone Labs Inc US patent 3617931 (1971) filed 5 May 1969

With the growing availability of navigation satellite constellations from more than one service provider, emergence of multi-constellation Global Navigation Satellite System (GNSS) makes it possible to develop Receiver Autonomous Integrity Monitoring (RAIM) capability with improved range measurement redundancy and accuracy. An emerging concept for receiver avionics that leverages the availability of multiple constellations, called Advanced RAIM (ARAIM), is being developed with a vision of not only providing worldwide approaches with vertical guidance, (Localizer Performance with Vertical Guidance, LPV-200, in particular) but it would also significantly expand Required Navigation Precision (RNP) operations availability worldwide. In the design of ARAIM, this improved capability stems mainly from introduction of an Integrity Support Message (ISM). The ISM is a periodically updated set of parameters that represent the statistical characterization of the core navigation constellation performance. This ISM is received by and utilized in the user avionics as a priori information in the ARAIM user algorithm. One critical issue is that when the statistical characterization of the core constellation performance is not accurate, ARAIM integrity performance may degrade. In our previous paper, we analyzed the sensitivity of Vertical ARAIM (V-ARAIM) performance to the ISM parameter deviations, leading to very interesting and important findings regarding ISM sensitivity. In our analysis, the term “deviation” is used to refer to an increase in the ISM values representative of actual core constellation performance relative to the broadcast ISM parameter values. In this paper, we extend the earlier analysis to Horizontal ARAIM (H-ARAIM). H-ARAIM requires a fault exclusion capability to meet the stricter RNP continuity requirements. The purpose of this paper is to see how the sensitivity characteristics of ARAIM to ISM deviation from actual values differ between H-ARAIM and V-ARAIM. The analysis in this paper, along with the analysis done previously for V-ARAIM, will help develop procedures that could be applied consistently and systematically to determine a set of “close-to-optimal” ISM parameter values to broadcast. This will facilitate global acceptance of an ISM update process.

The study of climate change is an important field of research. Monitoring of atmospheric variability especially the tropospheric precipitable water vapor (PWV) is a powerful way to investigate climate change. Global navigation satellite systems (GNSS) provide a good tool for studying atmospheric parameters as GNSS signals along its path from the satellites to the ground based receivers suffer a significant delay due to the refractivity of earth’s atmosphere. GNSS signals are not only delayed but also refracted in the neutral atmosphere. The zenith wet delays (ZWD) caused by the troposphere can be estimated during the geodetic processing of GNSS signals. Since the ZWD is tightly correlated to the PWV, GNSS observations can be used to study the changes of PWV. In this study GNSS data from the Egyptian permanent GNSS network (EPGN), International GNSS Service (IGS), EUREF Permanent Network (EPN) and Scripps Orbit and Permanent Array Center (SOPAC), for the years 2013 and 2014, were used. These GNSS stations provided a good geometrical coverage over the respective region. All GNSS data were processed using Bernese V 5.2 software. The needed product from GNSS data processing is the tropospheric zenith total delay (ZTD) over each GNSS site. The ZTD is divided based on physical parameters into zenith hydrostatic delay (ZHD) and zenith wet delay (ZWD). The ZWD is the basic observable used to calculate PWV. The values of the PWV were calculated and its variation over the study area was investigated. The quality of calculated PWV values from GNSS data evaluated against traditional Radiosonde (RS) measurements. The results of GNSS PWV showed good agreement with RS PWV when the PWV values were low, but this agreement became worse at high PWV values as the differences between the two techniques increased. The correlation coefficient between RS PWV and GNSS PWV varied from 0.31 to 0.84. Standard deviation of the differences between RS PWV and GNSS PWV ranged from 2.369 to 5.973 mm. The PWV estimated from GNSS observations had annual cycle, and its cycle in 2013 was different from that in 2014. The PWV differences between the 2 years clarified that the water vapor content over the east Mediterranean in most of 2014 days was higher than that in 2013. Furthermore, PWV variations were noted on both temporal and spatial scales. The highest temporal variation value was 25.41 mm whereas the maximum value of the spatial variation was 19.67 mm. The present study illustrated the importance of using geodetic networks to provide atmospheric information.

Nowadays one can use four global navigation satellite systems (GNSS). Two of them are complete constellations (GPS, Glonass) and two (Beidou, Galileo) are already usable and will be finish in the near future. Additionally satellite based augmentation systems (SBAS) like WAAS, EGNOS, GAGAN or QZSS complement the GNSS service. However, within all systems one can observe changes, modifications, and updates every year. This can be related to satellite renewables leading to signal property changes. Especially, for safety critical applications using GNSS, like advanced receiver autonomous integrity monitoring (ARAIM) or ground-based augmentation systems (GBAS) the new or changed signal properties are of high interest. With the help of detailed information about the signal deformation and the received signal power it is possible to calculate realistic error bounds and consequently realistic protection level for these kinds of safety critical applications. This paper presents an overview of the findings according new signals or signal configurations of GPS, Beidou and QZSS of the last two years. After a brief introduction of the measurement facility the paper will introduce basic analysis about the quality of the signal shape in spectral and modulation domain. Using our precise calibrated measurement facility, we will also present an analysis of the transmitted satellite signal power including estimates about the power sharing among individual signal components within each band. Considering the measured power in relation to the boresight angle of the satellite one can derive a cut through the antenna pattern of the satellite and can assess the antenna symmetry properties. Examples for different satellites will be presented. Finally, we will end with a conclusion regarding the considered signal developments and its impact on GNSS users.

The electromagnetic signals of the Global Navigation Satellite Systems (GNSS) satellites suffer delays while propagating through the troposphere. The tropospheric delay is a significant systematic error of GNSS positioning. For safety-of-life applications of positioning many systematic error effects are either mitigated or eliminated in the positioning solution. Space based augmentation systems provide corrections for the orbital and satellite clock error, the ionospheric effects, etc. Moreover advanced GNSS provide dual frequency code observations for civilian users to eliminate the ionospheric delays caused by the electron content of the upper atmosphere. Nevertheless tropospheric delays are still taken into account using empirical models.For safety-of-life applications besides the accuracy of the positioning, the integrity of the positioning service is an important factor, too. The integrity information includes the maximal positioning error at an extremely rare probability level, called protection level to ensure highly reliable position solution in the aviation. The Radio Technical Commission for Aeronautics Minimum Operational Performance Standard (RTCA MOPS) recommends 0.12 m as the maximum zenith tropospheric error in terms of standard deviation. Previous studies show that this recommendation seems to be too conservative leading to a lower service availability. Therefore a more realistic integrity model has to be derived for the estimation of maximal residual tropospheric delay error.In the recent years many advanced empirical tropospheric delay models have been formulated compared to the one recommended by the RTCA. Recently new integrity models have been derived for estimating the maximum residual tropospheric delay error using numerical weather models under real extreme weather.The aim of this paper is to study the reliability of these models conditions. In order to achieve this, high-resolution numerical weather models were ray-traced using an improved ray-tracing algorithm to evaluate the slant and zenith tropospheric delays with the geographical resolution of 0.1° × 0.1°.

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.

Integrity monitoring (IM) is essential if GNSS positioning technologies are to be fully trusted by future intelligent transport systems. A tighter and conservative stochastic model can shrink protection levels in the position domain and therefore enhance the user-level integrity. In this study, the stochastic models for vehicle-based GNSS positioning are refined in three respects: (1) Gaussian bounds of precise orbit and clock error products from the International GNSS Service are used; (2) a variable standard deviation to characterize the residual tropospheric delay after model correction is adopted; and (3) an elevation-dependent model describing the receiver-related errors is adaptively refined using least-squares variance component estimation. The refined stochastic models are used for positioning and IM under the Advanced Receiver Autonomous Integrity Monitoring (ARAIM) framework, which is considered the basis for multi-constellation GNSS navigation to support air navigation in the future. These refinements are assessed via global simulations and real data experiments. Different schemes are designed and tested to evaluate the corresponding enhancements on ARAIM availability for both aviation and ground vehicle-based positioning applications.

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.

As Global Navigation Satellite System continues to upgrade, the number of satellite increases and new signals in different frequencies are available. This offers users some benefits such as increased availability and improved diversity of signal. For safety-of-life users like aviation, higher availability of integrity is the key goal. A technology providing integrity with little or no ground infrastructure is receiver autonomous integrity monitoring (RAIM). As more satellites are visible to the receiver, the redundancies increase and RAIM availability for detecting faults improves. Moreover, the ionosphere delay could be eliminated through multi-frequency combination. The Advanced RAIM (ARAIM) uses multi-constellation and multi-frequency GNSS and hopes to provide vertical guidance for aircraft. For evaluating ARAIM in flight, an inexpensive flight is executed by an unmanned aircraft systems (UAS) with a developed ARAIM software receiver. The receiver supports civilian signals including GPS L1 C/A, L2C and GLONASS G1, G2. An efficient software architecture are proposed for the real-time purpose. Using the outputs from the receiver, a developed ARAIM toolbox computes the protection levels. Field test is conducted by flying the UAS on a live test campaign. The ARAIM performance during the flight shows that the vertical protection level achieves the target.

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.

This paper describes a process by which Global Navigation Satellite System (GNSS) nominal statistics and fault rates can be assessed with high confidence. The use of a GNSS in Advanced Receiver Autonomous Integrity Monitoring (ARAIM) requires knowledge of the level of performance of the signal-in-space (SIS) user range error (URE) in both nominal and faulty conditions. The performance characterization is done through a careful analysis of historical data, from which a determination of whether or not it meets the performance commitments of the constellation service provider (CSP) can be made. Typically, this analysis is carried out though a comparison of the broadcast navigation messages with post-processed precise estimates of the satellite clock and orbit states. This paper highlights the issues with such an approach as well as mitigation strategies to those issues. The issues primarily impact the estimates of fault rates rather than nominal statistics, as the number of faults is small compared to the overall amount of nominal data. In particular, methods of ensuring integrity in the logged broadcast navigation messages and precise clock and orbit products used in the analysis are described. In order to verify precise clock and orbit products, a Kalman filter (KF) to produce independent GNSS clock bias estimates has been developed and tested. The KF leverages the International GNSS Service (IGS) receiver network to produce estimates of GNSS clock biases given precisely known positions of the receivers as well as precise satellite orbit products. The KF serves multiple purposes in detecting and verifying faults. First, when only low rate (5 or 15 minute) clock products are available, higher rate products can be produced to detect short faults. Second, when precise clock products are available, the KF serves as a secondary check to protect against erroneous external precise products, which have been observed. Initial testing of the filter has shown an RMS error of under 20 centimeters for a five day run of GPS clock bias estimates, which is sufficient performance for precise product verification and fault detection.

<p>The snow water equivalent (SWE) is a key parameter in hydrology. In the past years, the signals of Global Navigation Satellite System (GNSS) receivers were discovered to be very attractive for SWE monitoring. The set-up of GNSS-based SWE monitoring typically consists of two GNSS receivers, whereas one is placed on the ground to sense the signal attenuation and time delay being caused by the snow pack. A second receiver is placed above the snow and serves as reference receiver. The measurements of both receivers are differenced to eliminate the common effect of errors in the satellite orbits and clocks, satellite phase and code biases and atmospheric errors, while the information on the snow is kept.</p><p>In this talk, we discuss the replacement of the reference receiver by a virtual reference station (VRS). The VRS is a virtual GNSS reference station, whose corrections are obtained by interpolation of the corrections from multiple surrounding reference stations to achieve a higher accuracy at the user location. The concept of VRS was first developed by Trimble and is widely used in today's real-time kinematic (RTK) positioning receivers. The concept of VRS is also attractive for snow monitoring, since the GNSS reference receiver could be avoided resulting in a lower power consumption and less costs. Moreover, this could be a big advantage for applications in slopes, which are, e.g., potentially avalanche prone. Within the hardware setup of our GNSS SWE sensors, an internet communication link for the reception of the corrections from the VRS corrections at the SWE monitoring site is already available.</p><p>However, there are also two challenges: First, the SWE monitoring stations in Alpine areas are typically at a significantly different altitude than the geodetic reference receivers. The differential tropospheric zenith delay is not negligible for altitudinal differences of more than 100 m. Therefore, the differential tropospheric delay needs to be considered either in the determination of VRS corrections or alternatively in the SWE determination. For altitudinal differences of less than 1000 m, the differential tropospheric zenith delay could be approximated by a model with sufficient accuracy. The residual modelling error is projected to the SWE estimate. Second, the use of a VRS instead of a conventional GNSS reference station requires a stronger data link, since the GNSS raw data (pseudoranges, carrier phases and carrier-to-noise power ratio measurements from all tracked satellites) need to be transmitted besides the final SWE results. However, an LTE link is totally sufficient.</p><p>Besides the methodology, we will also focus on specific hardware implementations.</p>

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.