scholarly journals Multi-Layer Defences for Robust GNSS Timing Retrieval

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 7787
Author(s):  
Ciro Gioia ◽  
Daniele Borio

A multi-layered interference mitigation approach can significantly improve the performance of Global Navigation Satellite System (GNSS) receivers in the presence of jamming. In this work, three levels of defence are considered including: pre-correlation interference mitigation techniques, post-correlation measurement screening and FDE at the Position, Velocity, and Time (PVT) level. The performance and interaction of these receiver defences are analysed with specific focus on Robust Interference Mitigation (RIM), measurement screening through Lock Indicator (LIs) and Receiver Autonomous Integrity Monitoring (RAIM). The case of timing receivers with a known user position and using Galileo signals from different frequencies has been studied with Time-Receiver Autonomous Integrity Monitoring (T-RAIM) based on the Backward-Forward method. From the experimental analysis it emerges that RIM improves the quality of the measurements reducing the number of exclusions performed by T-RAIM. Effective measurements screening is also fundamental to obtain unbiased timing solutions: in this respect T-RAIM can provide the required level of reliability.

2009 ◽  
Vol 63 (1) ◽  
pp. 105-117 ◽  
Author(s):  
Tomislav Radišić ◽  
Doris Novak ◽  
Tino Bucak

Receiver Autonomous Integrity Monitoring (RAIM) is a method, used by an aircraft's receiver, for detecting and isolating faulty satellites of the Global Navigation Satellite System (GNSS). In order for a receiver to be able to detect and isolate a faulty satellite using a RAIM algorithm, a couple of conditions must be met: a minimum number of satellites, and an adequate satellite geometry. Due to the highly predictable orbits of the GPS satellites, a RAIM availability prediction can be done easily. A number of RAIM methods exist; however, none of them takes into account the precise terrain masking of the satellites for the specific location. They consider a uniform fixed mask angle over the whole horizon. This paper will introduce the variable mask RAIM algorithm in order to show to what extent the terrain can affect the RAIM availability and how much it differs from the conventional algorithms.


Author(s):  
Y. Wu ◽  
J. Ren ◽  
W. Liu

As BeiDou navigation system has been operational since December 2012. There is an increasing desire to use multiple constellation to improve positioning performance. The signal-in-space (SIS) anomaly caused by the ground control and the space vehicle is one of the major threats to affect the integrity. For a young Global Navigation Satellite System, knowledge about SIS anomalies in history is very important for not only assessing the SIS integrity performance of a constellation but also providing the assumption for ARAIM (Advanced Receiver Autonomous Integrity Monitoring). <br><br> In this paper, the broadcast ephemerides and the precise ones are pre-processed for avoiding the false anomaly identification. The SIS errors over the period of Mar. 2013-Feb. 2016 are computed by comparing the broadcast ephemerides with the precise ones. The time offsets between GPST (GPS time) and BDT (BeiDou time) are estimated and removed by an improved estimation algorithm. SIS worst-UREs are computed and a RMS criteria are investigated to identify the SIS anomalies. The results show that the probability of BeiDou SIS anomalies is in 10-3 level in last three years. Even though BeiDou SIS integrity performance currently cannot match the GPS integrity performances, the result indicates that BeiDou has a tendency to improve its integrity performance.


2013 ◽  
Vol 66 (5) ◽  
pp. 683-700 ◽  
Author(s):  
Ling Yang ◽  
Nathan L. Knight ◽  
Yong Li ◽  
Chris Rizos

In Global Navigation Satellite System (GNSS) positioning, it is standard practice to apply the Fault Detection and Exclusion (FDE) procedure iteratively, in order to exclude all faulty measurements and then ensure reliable positioning results. Since it is often only necessary to consider a single fault in a Receiver Autonomous Integrity Monitoring (RAIM) procedure, it would be ideal if a fault could be correctly identified. Thus, fault detection does not need to be applied in an iterative sense. One way of evaluating whether fault detection needs to be reapplied is to determine the probability of a wrong exclusion. To date, however, limited progress has been made in evaluating such probabilities. In this paper the relationships between different parameters are analysed in terms of the probability of correct and incorrect identification. Using this knowledge, a practical strategy for incorporating the probability of a wrong exclusion into the FDE procedure is developed. The theoretical findings are then demonstrated using a GPS single point positioning example.


Sensors ◽  
2019 ◽  
Vol 19 (22) ◽  
pp. 4847
Author(s):  
Weichuan Pan ◽  
Xingqun Zhan ◽  
Xin Zhang ◽  
Shizhuang Wang

The advanced receiver autonomous integrity monitoring (advanced RAIM, ARAIM) is the next generation of RAIM which is widely used in civil aviation. However, the current ARAIM needs to evaluate hundreds of subsets, which results in huge computational loads. In this paper, a method using the subset excluding entire constellation to evaluate the single satellite fault subsets and the simultaneous multiple satellites fault subsets is presented. The proposed ARAIM algorithm is based on the tight integration of the global navigation satellite system (GNSS) and inertial navigation system (INS). The number of subsets that the proposed GNSS/INS ARAIM needs to consider is about 2% of that of the current ARAIM, which reduces the computational load dramatically. The detailed fault detection (FD) process and fault exclusion (FE) process of the proposed GNSS/INS ARAIM are provided. Meanwhile, the method to obtain the FD-only integrity bound and the after-exclusion integrity bound is also presented in this paper. The simulation results show that the proposed GNSS/INS ARAIM is able to find the failing satellite accurately and its integrity performance is able to meet the integrity requirements of CAT-I precision approach.


2020 ◽  
Vol 10 (1) ◽  
pp. 381 ◽  
Author(s):  
Giulio Franzese ◽  
Nicola Linty ◽  
Fabio Dovis

This work focuses on a machine learning based detection of ionospheric scintillation events affecting Global Navigation Satellite System (GNSS) signals. We here extend the recent detection results based on Decision Trees, designing a semi-supervised detection system based on the DeepInfomax approach recently proposed. The paper shows that it is possible to achieve good classification accuracy while reducing the amount of time that human experts must spend manually labelling the datasets for the training of supervised algorithms. The proposed method is scalable and reduces the required percentage of annotated samples to achieve a given performance, making it a viable candidate for a realistic deployment of scintillation detection in software defined GNSS receivers.


2019 ◽  
Vol 72 (06) ◽  
pp. 1533-1549
Author(s):  
X.M. Huang ◽  
X. Zhao ◽  
J.Y. Li ◽  
X.W. Zhu ◽  
G. Ou

An algorithm for Global Navigation Satellite System satellite atomic clock integrity monitoring based on an extended measurement model is proposed. A detection statistic achieved by parity transformation is used to detect clock anomalies, and the concept of the optimal accumulation number, with a method to find it, is provided. Numerical simulations are adopted to verify the validity of detecting two typical anomalies.


Author(s):  
H. Haddadi Amlashi ◽  
F. Samadzadegan ◽  
F. Dadrass Javan ◽  
M. Savadkouhi

Abstract. GNSS stands for Global Navigation Satellite System and is the standard generic term for satellite navigation systems that provide autonomous geo-spatial positioning with global coverage. The advantage of having access to multiple satellites is accuracy, redundancy, and availability at all the times. Though satellite systems do not often fail, if one fails GNSS receivers can pick up signals from other systems. If the line of sight is obstructed, having access to multiple satellites is also a benefit. GPS (Global Positioning System, USA), GLONASS (Global Navigation Satellite System, Russia), BeiDou (Compass, China), and some regional systems are positioning systems that are usually used. In recent years with the development of the UAVs and GNSS receivers, it is possible to manage an accurate PPK (Post Processing Kinematic) networks with a GNSS receiver mounted on a UAV to achieve the position of images principal points WGS1984 and to reduce the need for GCPs. But the most important challenge in a PPK task is, which a combination of different GNSS constellations would result in the most accurate computed position in checkpoints. For this purpose, this study focused on a PPK equipped UAV to map an open pit (Golgohar mine near Sirjan city). For the purpose, different combination of GPS, GLONASS and BeiDou used for position computed. Results are plotted and compared and found out having access to multiple constellations while doing a PPK task would bring higher accuracies in building photogrammetric models although it may cause some random error due to the higher values of noise while the number of the satellites increases.


2020 ◽  
Author(s):  
Patrick Henkel ◽  
Markus Lamm ◽  
Franziska Koch

<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>


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