Method for eliminating or mitigating the effect of ionospheric irregularities on GNSS positioning

Abstract

The present invention concerns a method for eliminating or mitigating the effect of ionospheric irregularities on GNSS enabled positioning in areas where ground network of ionospheric monitoring receivers providing streaming of ionospheric scintillation parameters are available Moreover in the case of very harsh ionospheric conditions which prevent the algorithm to be successful in mitigation approach an alerting is provided to the user.

Description

[0001]Method for eliminating or mitigating the effect of ionospheric irregularities on GNSS positioning ------ Applicant: SPACEARTH NAV S.R.L. Inventors: Marco Fermi, Pietro Vermicelli, Maurizio Vasta, Giorgiana De Franceschi ------ The present invention concerns a method for eliminating or mitigating the effect of ionospheric irregularities on GNSS positioning. Background The occurrence, spatial distribution, characteristics, and dynamics of ionospheric irregularities are among the most widely studied features of ionospheric phenomenology. Despite the progress achieved in the last decades, the physics of ionospheric irregularities is far from being completely understood. Despite their ruling mechanisms and related plasma structuring being generally known, their behavior still challenges the modeling and predicting capabilities. This poses serious limitations in getting rid of the disruptive effects on GNSS-based navigation and positioning, such as scintillation. It is reasonable to say that an effective and operative modeling able to predict the appearance and evolution of the ionospheric irregularities that cause signal scintillations is the last significant unsolved problem in ionospheric science applied to communications, positioning and navigation. Small-scale irregularities (order of a few hundreds of Jacobacci & Partners / ANP meters) cause scintillation effects on GNSS signals, jeopardizing the rover receiver performance in terms of positioning accuracy degradation (through range errors) or even leading to a complete loss of signal tracking. They can degrade the performance of navigation systems and generate errors in received messages and measurements. The required levels of accuracy, integrity, functional consistency, and availability of GNSS signals are sometimes not satisfied, compromising commercial operations like offshore navigation, digital farming, and station keeping, including safety-critical applications. Object and subject-matter of the invention It is an object of the present invention to provide a method for eliminating or mitigating the effect of ionospheric irregularities on GNSS positioning. It is subject-matter of the present invention a method according to the attached claims. Detailed description of invention embodiments List of figures The invention will now be described by way of illustration but not by way of limitation, with particular reference to the drawings of the attached figures, in which: ^Figure 1 shows a block diagram of the inventionsystem; ^Figure 2 shows a geometry of a satellite-groundJacobacci & Partners / ANP station line-of-sight depicting the Ionospheric Pierce Point. It is specified here that elements of different embodiments can be combined together to provide further embodiments without limits while respecting the technical concept of the invention, as the skilled person understands without problems from what has been described. The present description also refers to the known art for its implementation, with regard to the detailed characteristics not described, such as for example elements of minor importance usually used in the known art in solutions of the same type. When introducing an element it always means that it can be “at least one” or “one or more”. When a list of elements or features is given in this description, it is meant that the invention according to the invention “includes” or alternatively “is composed of” such elements. When features are listed within the same sentence or bulleted list, one or more of the individual features may be included in the invention without connection to the other features in the list. Two or more of the parts (elements, devices, systems) described above can be freely associated and considered as kits of parts according to the invention. Embodiments The present invention eliminates the impact of scintillation on GNSS-based real-time positioning and Jacobacci & Partners / ANP navigation exploiting the streaming of scintillation parameters (the scintillation indices S4 and σΦ) provided by a dedicated network of GNSS receivers. The scintillation parameters are used to generate a weighing matrix related to the link between a satellite and a rover receiver, the signal being corrupted by scintillation, the weighing matrix being used to update the observables variance matrix thus modifying the stochastic model of the PVT (Position, Velocity and Time) estimator. It is here to specify that the GNSS receivers are different from the rover receivers, in that the former are used to obtain scintillation parameters and the latter are the receivers that are to be tracked and / or guided on the ground. The effectiveness of the method is guaranteed in a Region of Interest delimited by the static GNSS stations belonging to the dedicated GNSS network. Good performances in eliminating impact of Scintillation can be achieved also in the vicinity of the dedicated GNSS network but it is not guaranteed. Depending on the application, the streaming of Scintillation parameters (S4 and σΦ) provided by the dedicated GNSS network can be used “as is” or can be used as input for the Short-Term Forecasting model (patent application number WO2016185500A1) which forecast the expected scintillation parameters with a time horizon up to twenty minutes in advance. The innovation is applicable at low latitudes around the equator and at high latitudes as well, where the Jacobacci & Partners / ANP ionospheric irregularities are expected to pose the harshest conditions to positioning. At low latitudes, very harsh ionospheric condition can happen sometimes and, in those cases, the invention method could not guarantee the effectiveness of the scintillation mitigation strategy, but in those cases the invention method shall raise an Alert warning the user about the potential lack of effectiveness of the invention method mitigation. An embodiment of the invention method is described in the following with reference to Fig. 1. Block 1 S4 and σΦindices from a network of GNSS receivers capable of providing real-time monitoring of signal phase and amplitude with a rate of, at least, 20 Hz. The S4 and σΦ parameters are defined as: (1a) (1b) wherein SI is the detrended signal intensity and Φ is the detrended phase and < … > denotes an ensemble running average on a time window of 1 minute or less (Fremouw et al. 1978). Phase detrending arises from the need to remove the low-frequency phase variations due to relative motion between the satellite and the GNSS receiver. To calculate the detrended phase, a sixth order Jacobacci & Partners / ANP Butterworth filter with a fixed 0.1 Hz cutoff frequency is commonly used (Van Dierendonk and Arbesser-Rastburg 2004). The received signal power varies due to changing range, antenna patterns and multipath. So, as with the phase measurements, the intensity measurements must be detrended and this can be done by using the same sixth order Butterworth filter with a fixed 0.1 Hz cutoff introduced above. This provides the proper inputs to the “Scintillation parameters forecasting” module (block 4, which is described in patent application WO2016185500A1) in the cases in which forecasting is applied. Block 2 Rover receiver capable of providing its position (according to NMEA or other protocols) to be used for the computation of the proper scintillation parameters in block 6. Block 3 This block comprises all the process functionalities blocks. Block 4 This block includes the algorithms for the patented scintillation empirical short-term forecasting algorithm (patent WO2016185500A1 “Method for forecasting ionosphere total electron content and / or scintillation parameters”). This algorithm outputs the scintillation parameters (S4 and σΦ) seconds to minute in advance to Jacobacci & Partners / ANP feed the scintillation parameters mapping module (block 5). Block 5 Performing the mapping of scintillation parameters requires interpolating Ionospheric Pierce Point (IPP) samples. A sample consists of the scintillation parameters associated with the IPP of each satellite- station link for each station, providing scintillation parameters data in the given area and time interval. Among the possible approaches for scintillation parameters mapping, the Gaussian Process Regression (GPR), described in Rasmussen, C. E., & Williams, C. (2006), with a specific set of pre-processing options, allows the generation of the most accurate maps. This is the algorithm that has been implemented, which will be described below. A sample for the scintillation mapping algorithm consists of the scintillation parameters calculated at the IPPs, defined as the intersection of the line-of- sight station-satellite (the slant path) with the ionosphere (seen as a thin shell at 350 km altitude over the Earth, Fig. 1). The IPPs coordinates are computed from the azimuth and elevation angles of the satellites in view from the network of GNSS receivers. Given a timeframe and a set of samples, the computation of the scintillation maps follows these two steps: ^Performing a pre-processing of the data to get the“interpolation samples”. The pre-processing is Jacobacci & Partners / ANP based on a regular aggregation grid (whose length is customizable). The samples that fall in the same cell are aggregated to obtain an “interpolation sample” with: -IPP coordinates equal to the average locationof the IPP samples with scintillation parameters above the 3rd quartile; -scintillation parameters equal to the averageof scintillation parameters above the 3rd quartile. ^Performing the Gaussian Process Regressioninterpolation using the “interpolation samples.” The GPR method employs a Rational Quadratic kernel to modify the interpolation result based on the distance between the “interpolation samples.” The Haversine distance was used, which determines the great-circle distance between two points on a sphere given their longitudes and latitudes. From the interpolation map returned from the GPR algorithm, one can evaluate the scintillation parameters at each point of the desired interpolation grid to be used as one of the inputs to the module “Satellite- receiver observables weight computation” (block 6). Block 6 This block is based on the computation of weights, according to the scintillation map provided by block 5, to be associated with each satellite-rover link. This to update the stochastic model in the Kalman filter included in the rover receiver PVT engine. Jacobacci & Partners / ANP This module is organized into 2 main steps: 1. selection, depending on the rover position providedby block 2, of the proper scintillation parameters from the scintillation map provided by block 5. This step is performed by selecting, for each IPP related to a particular satellite-rover receiver link, the scintillation parameters computed, by the GPR algorithm described in Block 5, at the latitude and longitude of the IPP. 2. computation of the weights based on a parametrizedmitigation function related to the selected scintillation parameters. According to the basic theory, the mitigation function is based on the risk function (e.g. the risk that the data are corrupted). The risk function is derived from a loss function. In this case the loss function is related to the loss of geometrical significance of the pseudorange and phase range measurements given by an error due to scintillation not modelled. For example, the 0-1 loss function can be defined for each k-th satellite-rover link as^^(^^; ^^^^ℎ)= {0, ^^^^ ^^ < ^^^^ℎ 1, ^^^^ℎ^^^^^^^^^^^^ (2)Where ^^ is the measurable feature of interest (S4) and ^^^^ℎa given threshold. Then the risk r(zth) can be defined as the probabilitythat ^^ exceeds ^^^^ℎ:^^(^^^^ℎ) = ^^ ( ^^ ≥ ^^^^ℎ ) (3)Jacobacci & Partners / ANP In our case the Loss and Risk functions are coincident. In fact, we compute the IPP of each satellite-rover link, and using the computed scintillation parameters maps, the probability that the scintillation parameters influencing the receiver- satellite-link is lower or higher than a specific threshold is 1. We designed the Risk function, used to generate weights for the rover, in a configurable and parametrizable way. It could be a very simple risk function as in Equation (2) or it could consider the more complex classification of scintillations in weak, moderate, and strong. Moreover, it can be given by empirical fixed values associated to specific range of S4. In general, for each k-th satellite-rover link, a risk function that considers the more complex scintillation behavior looks as follows: Wherein the threshold is a configurable vector T=[T1,T2, …,TN] and the vector r=[r1,r2,…,rN] is a configurable vector of empirical fixed values associated to the specific ranges of S4 given by T. Or it can consider the non-linearity of the impact of scintillation as S4 and σΦ increase. For example: Jacobacci & Partners / ANP Wherein 0.5 < j < 1.0 is an empiric configurable parameter within the indicated range. The weights are then given by:^^(^^4) = (1 − ^^(^^4, ^^))2 For each k-th satellite-rover link. An advantage of this approach is that the mitigation method is independent from the receiver characteristics. Finally, the module makes available through standard NTRIP protocol or other protocols, the calculated weights to the rover for the de-weighing of the satellite-rover link affected by the ionospheric scintillation. References Van Dierendonck AJ, Arbesser-Rastburg B (2004) Measuring Iono - spheric scintillation in the equatorial region over Africa, includ - ing measurements from SBAS geostationary satellite signals. In: Proceedings of the ION GNSS 2004, Institute of Navigation, Long Beach, CA, September 21–24, pp 316–324 Fremouw, E. J., Leadabrand, R. L., Livingston, R. C., Cousins, M. D., Rino, C. L., Fair, B. C., & Long, R. A. (1978). Early results from the DNA Wideband satellite Jacobacci & Partners / ANP experiment—Complex-signal scintillation. Radio Science, 13(1), 167-187. Rasmussen, C. E., & Williams, C. (2006). Gaussian processes for machine learning, the MIT press. Cambridge, MA, 32, 68. List of abbreviations GNSS: Global Navigation Satellite System GPR: Gaussian Process Regression IPP: Ionospheric Pierce Point NMEA: National Marine Electronic Association PVT: Position, Velocity and Time ROI: Region of Interest In the foregoing, the preferred embodiments have been recognized and variations of the present invention have been suggested, but it is to be understood that those skilled in the art will be able to make modifications and changes without thereby departing from the relevant scope of protection, as defined by the attached claims. Jacobacci & Partners / ANP

Claims

CLAIMS 1. Method for eliminating or mitigating the effect of ionospheric irregularities on GNSS positioning, comprising the execution of the following steps: A. obtaining (1) real-time scintillation parameters from a network of GNSS receivers; B. obtaining (2) a position from each of a plurality of rover receivers linked to GNSS satellites, in the area covered by a network of GNSS receivers; C. forecasting (4) ionosphere total electron content and / or scintillation parameters S4 and σΦ based on scintillation parameters of step A; D. performing a mapping (5) of scintillation parameters based on the forecasts of step C, by interpolating Ionospheric Pierce Point samples, wherein a Ionospheric Pierce Point sample consists of scintillation parameters associated with the Ionospheric Pierce Point of each GNSS satellite-receiver link for each rover receiver, providing an interpolation map with scintillation parameters data in a given area and time frame, each Ionospheric Pierce Point being associated to a respective latitude and longitude; E. selecting a grid of points on said interpolation map, wherein respective averaged scintillation parameters are associated to each grid point by averaging the scintillation parameters on a pre-defined cell around a respective grid point; Jacobacci & Partners / ANPF. From the interpolation map of step D and E, evaluating the scintillation parameters at each grid point, by performing the following sub-steps: ^selecting, depending on the position of each roverreceiver of step B, corresponding scintillation parameters from the interpolation map provided by step D and E, by selecting, for each Ionospheric Pierce Point related to a respective satellite- rover receiver link, respective scintillation parameters associated to the respective latitude and longitude of the Ionospheric Pierce Point; ^computing (6) respective weights to be used to downweighing a GNSS signal in the respective satellite- rover receiver link, to eliminate or mitigate the impact of scintillation on the GNSS signal.

2. Method according to claim 1, wherein step D comprises the following sub-steps, given a timeframe and a set of Ionospheric Pierce Point samples, the Ionospheric Pierce Point samples that fall in each respective pre-defined cell are aggregated to obtain a respective interpolation sample associated to: -Ionospheric Pierce Point coordinates equal to theaverage location of the Ionospheric Pierce Point samples with scintillation parameters above the 3rd quartile; -scintillation parameters equal to the average ofscintillation parameters above the 3rd quartile; thus obtaining a set of interpolation samples, wherein a Gaussian Process Regression interpolation using the Jacobacci & Partners / ANPset of interpolation samples is performed to obtain the interpolation map with associated grid points.

3. Method according to claim 1 or 2, wherein step A provides S4 and σΦ parameters from the network of GNSS receivers capable of providing real-time monitoring of GNSS signal phase and amplitude with a rate of, at least, 20 Hz.

4. Method according to any one claim 1 to 3, wherein in step F the weights are calculated through a parametrized mitigation function based on a risk function which is derived from a loss function for the respective satellite-rover receiver link .

5. Method according to claim 4, wherein the loss and risk functions are coincident.

6. Method according to claim 5, wherein the risk function is a function: 0^^^^ ^^1 < ^^4 < ^^2^^(^^4, ^^) = { 0.5 ^^^^ ^^2 ≤ ^^4 <^^3 0.98 ^^^^ ^^4 > ^^3Where T is a threshold which is a configurable vector, T = [T1, T2, T3] for each respective satellite-rover receiver link and the weights are given by:

7. Method according to claim 6, wherein the risk function is: Jacobacci & Partners / ANP0 ^^^^ ^^4 < 0.4^^(^^4, ^^) = { 0.5 ^^^^ 0.4 ≤ ^^4 <1 0.98 ^^^^ ^^4 > 1Where T = [0, 0.4, 1] for each respective satellite- rover receiver link.

8. Method according to claim 5, wherein the risk function is: 0^^^^ ^^1 < ^^4 < ^^2^^(^^4, ^^) = { 0.4^^ ^^^^ ^^2 ≤ ^^4 <^^3 0.98 ^^^^ ^^4 > ^^3Where the threshold T = [T1, T2, T3] is a configurable vector for each respective satellite-rover receiver link and 0.5 < j < 1.0 is an empiric configurable parameter within the indicated range, and the weights are given by:

9. Method according to claim 8, wherein the risk function is: 0^^^^ ^^4 < 0.4^^(^^4, ^^) = { 0.4^^ ^^^^ 0.4 ≤ ^^4 <1 0.98 ^^^^ ^^4 > 1Where T = [0, 0.4, 1] for each respective satellite- rover receiver link. Jacobacci & Partners / ANP

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