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1. WO2020113260 - A METHOD AND A SYSTEM FOR ASSESSING ASPECTS OF AN ELECTROMAGNETIC SIGNAL

Note: Text based on automatic Optical Character Recognition processes. Please use the PDF version for legal matters

[ EN ]

CLAIMS:

1. A method of assessment of aspects of one or more electromagnetic signals, the method including, in an electronic processing device:

receiving one or more data feeds relating to one or more of: cosmic, atmospheric, and local environmental conditions;

receiving one or more data feeds relating to the one or more electromagnetic signals; determining a plurality of metrics at least partially using the one or more data feeds; and, identifying a likely source of interference in the electromagnetic signals by assessing relationships among the plurality of metrics.

2. A method according to claim 1 , wherein the one or more data feeds are at least partially indicative of observable characteristics of an electromagnetic signal receiver.

3. A method according to claim 2, wherein the observable characteristics include any one or more of an altitude, a height, a vibration, a temperature, frequency response, and power.

4. A method according to any one of claims 1 to 3, wherein the method includes, in the electron ic processing device, determining a reference model at least partially indicative of relationships among metrics, the reference model being usable in assessing the relationships.

5. A method according to claim 4, wherein the reference model is generated using a System or Systems (SoS) approach.

6. A method according to claim 4 or claim 5, wherein generating a reference model includes using one or more regression methods, wherein the relationships are at least partially indicative of causality.

7. A method according to claim 6, wherein generating the reference model includes quantifying functional attributes using the relationships.

8. A method according to any of the claims 4 to 7, wherein the reference model includes a system of systems (SoS) model.

9. A method according to any one of claims 1 to 8, wherein the method includes, in the processing device, normalizing the metrics.

10.A method according to claim 9, wherein the normalizing includes performing at least one regression using at least one numerical technique.

11.A method according to claim 9 or claim 10, wherein the normalizing includes using at least one statistical tool to normalize the metrics, each of the at least one metric being scaled according to a common scale.

.A method according to any one of claim 9 to 11 , wherein the common scale includes a nu merical range between 0 and 1.

.A method according to any one of claims 9 to 12, wherein the normalizing includes normalizing raw values of the at least one data feeds and an absolute maximum of the raw values. .A method according to any one of the claims 9 to 13, wherein the normalizing includes numerical conversion.

.A method according to any one of the claims 9 to 14, wherein the normalizing is at least partially performed using one or more machine learning models.

.A method according to any one of the claims 9 to 15, wherein the one or more metrics is determined at least in part using data indicative of at least one or more of a local magnetic field, space weather, an electromagnetic signal quality, an electromagnetic signal receiver quality, a GPS position accuracy and a GPS.

.A method according to any one of claims 10 to 16, wherein the identification includes determining at least one machine learning algorithm to thereby assess relationships between at least one of: the metrics; and, a time step.

.A method according to claim 17, wherein the machine learning algorithm is supervised. .A method according to claim 17, wherein the machine learning is unsupervised.

.A method according to any one of claims 1 to 19, wherein the identification includes clustering the metrics to thereby determine at least one state in accordance with the determined clusters, the state being at least partially indicative of a qualitative relationship between metrics.

.A method according to claim 20, wherein the clustering includes performing, in the computer processor, at least one of k-means clustering, mean-shift clustering, DBSCAN, expectation maximization by Gaussian mixture modelling, and agglomerative hierarchical clustering. .A method according to any one of claims 4 to 21 , wherein the reference model includes an at least partially trained machine learning model.

.A method according to any one of the claims 4 to 22, wherein the determining the reference model includes at least one of:

generating the reference model;

receiving the reference model from a remote processing device; and,

retrieving the reference model from a store.

.A method according to claim 23, wherein generating the reference model includes training the reference model using at least one of:

at least one of the plurality of metrics; and,

at least one pre-determined metric.

.A method according to claim 24, wherein generating the training includes at least one of on line and offline training.

.A method according to any one of claims 4 to 25, wherein the reference model is indicative of qualitative and quantitative relationships among metrics.

.A method according to claim 26, wherein the reference model is at least partially indicative of causality among the relationships.

.A method according to any one of claims 4 to 27, wherein the reference model includes at least one feature extraction reference model and at least one regression reference model. .A method according to any one of claims 20 to 28, wherein the identifying includes, in the electronic processing device, performing a numerical relationship regression for at least one of the clusters to thereby at least partially determine a causal relationship.

.A method according to claim 29, wherein method includes, in the processing device, identify ing the source of interference using at least one of the state and the causal relationship. .A method according to any one of claims 1 to 30, wherein the identification includes, in the computer processor, generating a representation indicative of at least one of:

the at least one state; and,

the at least one causal relationship.

.A method according to claim 31 , wherein the method includes, in the computer processor, displaying the representation on a display.

.A method according to any one of claims 31 to 32, wherein the representation includes a di rected acyclic graph (DAG) indicative of the causal relationship.

.A method according to claim 33, wherein the representation includes a graphical representa tion indicative of the DAG.

.A method according to any one of claims 33 to 34, wherein the representation includes a matrix indicative of the DAG.

.A method according to any one of claims 1 to 35, wherein the regression techniques include at least one of a Dynamic Bayesian Network and a Gaussian Mixture Model.

.A method according to any one of claims 1 to 36, wherein the method includes, in the com puter processor, storing results of at least one of cluster regression and relationship regres sion.

.A method according to claim 37, wherein the method includes, in the computer processor, determining at least of the pre-determined cluster regression and the relationship regression, and performing the identifying in real-time using the predetermined cluster regression and/or the relationship regression.

.A method according to any one of claims 1 to 38, wherein the method includes, in a comput er processor, assessing the quantitative relationship indicators over time by comparing at least one of the predetermined cluster regression and the predetermined relationship regres sion with at least one of the cluster regression and the relationship regression, respectively. .A method according to any one of claims 1 to 39, wherein the data feeds include data indica tive of at least one of a local temperature, cosmic radiation and atmospheric radiation.

.A method according to any one of claims 1 to 40, wherein the data feeds are at least partially received from sensors in electrical communication with the computer processor.

.A method according to any one of the claims 1 to 41 , wherein the data feeds received via an aggregator remote from the computer processor.

.A method according to any one of the claims 1 to 42, wherein the electromagnetic signal is at least partially received by a device disposed in a selected location on or near the Earth’s surface.

.A method according to any one of the claims 1 to 43, wherein the electromagnetic signal is a radio frequency signal.

.A method according to claim 44, wherein the radio frequency signal is received from one or more satellites or aircraft.

.A method according to claim 44 or claim 45, wherein the radio frequency signal relates to terrestrial position data obtained from one or more satellites or aircraft.

.A method according to any one of claims 1 to 446, wherein the method includes, in a com puter processor, determining quality of at least one of the signal and the data feeds from an aggregator.

.A method for at least partially identifying at least one source of interference associated with the electromagnetic, the method according to any one of the claims 1 to 47.

.A system for assessing aspects of an electromagnetic signal, the system including:

one or more receivers for receiving one or more data feeds from one or more sources relating to cosmic, atmospheric and/or local environmental conditions;

one or more receivers for receiving data relating to one or more electromagnetic signals; a mapping engine for mapping metrics derived from the data feeds; and

a regression engine for assessing relationships between selected mapped metrics so as to identify likely sources of signal changes.