Systems, methods, and computer-readable media for identifying navigation signal interference

Abstract

This disclosure relates to systems, methods, and computer-readable media for identifying navigation signal interference. The system includes one or more processors configured to acquire navigation system quality information from multiple data sources. The one or more processors are further configured to generate navigation signal interference map information based on the navigation system quality information. For example, the one or more processors may be configured to aggregate first data from a first data source among the multiple data sources based on one or more parameters, and to identify one or more areas associated with low-quality navigation signals based on second data from a second data source among the multiple data sources. A portion of the aggregated first data may be discarded based on the one or more areas to generate the navigation signal interference map information. The one or more processors may also be configured to output the navigation signal interference map information.

Description

Technical Field

[0001] This disclosure generally relates to the identification of navigation signal interference. Background Technology

[0002] Global Navigation Satellite Systems (GNSS) (such as Galileo from Europe, the Global Positioning System (GPS) or Wide Area Augmentation System (WAAS) from the United States, the Global Navigation Satellite System (GLONASS) from Russia, the BeiDou Navigation Satellite System (BDS) from China, the Indian Regional Navigation Satellite System (IRNSS) from India, or the Quasi-Zenith Satellite System (QZSS) from Japan) are satellite networks that transmit positioning and timing data to GNSS receivers. These GNSS receivers are configured to use this positioning and timing data to determine the location associated with the GNSS receiver.

[0003] Conditions of a satellite navigation system or the environment of a GNSS receiver can prevent a GNSS receiver from receiving sufficient signals (e.g., positioning and timing data) from GNSS satellites. For example, the arrangement of a GNSS system's satellite constellation in orbit may create one or more areas of low-quality signal. As another example, interference in the environment (such as jamming signals (e.g., intentional radio frequency interference to GNSS signals)) or spoofing signals can affect a GNSS receiver's ability to receive signals from GNSS. To illustrate, jamming signals can disrupt a GNSS receiver's ability to lock onto signals from GNSS, causing the GNSS system to become ineffective or degraded for GNSS receivers in affected areas. Spoofing involves broadcasting false satellite signals (i.e., "spoofing" signals) that provide false positioning and / or timing data to a GNSS receiver to deceive or mislead it. Due to interference (e.g., jamming and / or spoofing), a GNSS receiver may be unable to calculate correct position, navigation, and timing (PNT) data. Summary of the Invention

[0004] In a particular implementation, a system includes one or more processors configured to acquire navigation system quality information from multiple data sources. The navigation system quality information includes first data acquired from a first data source among the multiple data sources and second data acquired from a second data source among the multiple data sources. The one or more processors are further configured to aggregate the first data based on one or more parameters to generate aggregated first data, and to identify one or more areas associated with low-quality navigation signals based on the second data. The one or more processors are further configured to discard portions of the aggregated first data based on the one or more areas to generate navigation signal interference map information. The one or more processors are configured to output the navigation signal interference map information.

[0005] In another specific implementation, a method for identifying navigation signal interference includes acquiring navigation system quality information from multiple data sources. The navigation system quality information includes first data acquired from a first data source among the multiple data sources and second data acquired from a second data source among the multiple data sources. The method further includes aggregating the first data based on one or more parameters to generate aggregated first data, and identifying one or more areas associated with low-quality navigation signals based on the second data. The method further includes discarding portions of the aggregated first data based on the one or more areas to generate navigation signal interference map information. The method includes outputting the navigation signal interference map information.

[0006] In another particular implementation, a non-transitory computer-readable medium stores instructions that, when executed by one or more processors, cause one or more processors to initiate, execute, or control operations to identify navigation signal interference. These operations include acquiring navigation system quality information from multiple data sources. The navigation system quality information includes first data acquired from a first data source among the multiple data sources and second data acquired from a second data source among the multiple data sources. These operations also include aggregating the first data based on one or more parameters to generate aggregated first data, and identifying one or more areas associated with low-quality navigation signals based on the second data. These operations further include discarding portions of the aggregated first data based on the one or more areas to generate navigation signal interference map information. These operations include outputting the navigation signal interference map information.

[0007] In another specific implementation, an apparatus includes components for acquiring navigation system quality information from multiple data sources. The navigation system quality information includes first data acquired from a first data source among the multiple data sources and second data acquired from a second data source among the multiple data sources. The apparatus also includes components for aggregating the first data based on one or more parameters to generate aggregated first data, and components for identifying one or more areas associated with low-quality navigation signals based on the second data. The apparatus further includes components for discarding portions of the aggregated first data based on the one or more areas to generate navigation signal interference map information. The apparatus includes components for outputting navigation signal interference map information.

[0008] The features, functions, and advantages described herein can be implemented independently in various ways or combined in other ways, and further details can be found in the following description and figures. Attached Figure Description

[0009] Figure 1 This is a diagram illustrating an example of a system according to some examples of this disclosure, which is configured to identify interference associated with navigation signals.

[0010] Figure 2 The diagram illustrates another example of a system according to some examples of this disclosure, which is configured to identify interference associated with navigation signals.

[0011] Figure 3 This is a functional diagram illustrating another example of a system according to some examples of this disclosure, which is configured to identify interference associated with navigation signals.

[0012] Figure 4 This is an example illustration of a map according to some examples of this disclosure, which represents the predicted satellite coverage of a navigation system.

[0013] Figure 5 This is an example diagram illustrating a representation of a region according to some examples of this disclosure, the representation of which indicates interference associated with navigation signals.

[0014] Figure 6 This is an example illustration of a map according to some examples of this disclosure, which represents interference associated with navigation signals.

[0015] Figure 7 This is a diagram illustrating another example of a map according to some examples of this disclosure, showing interference associated with navigation signals.

[0016] Figure 8 This is a further example of a map illustrating some examples of the map according to this disclosure, which represents interference associated with navigation signals.

[0017] Figure 9 This is a further example of a map illustrating some examples of maps according to this disclosure, which represents interference associated with navigation signals.

[0018] Figure 10 This is an example diagram illustrating a graph of factors associated with blocking interference, according to some examples of this disclosure.

[0019] Figure 11 This is a diagram illustrating examples of blocking patterns generated by a blocking source according to some examples of this disclosure.

[0020] Figure 12 This is an example illustration of a map according to some examples of this disclosure, which represents interference associated with navigation signals.

[0021] Figure 13 This is an example illustration of a map according to some examples of the present disclosure, which represents interference associated with the navigation signals of an aircraft.

[0022] Figure 14This is a flowchart illustrating examples of methods for identifying interference associated with navigation signals according to some examples of this disclosure.

[0023] Figure 15 It shows Figure 1 A flowchart illustrating an example of an aircraft's lifecycle.

[0024] Figure 16 yes Figure 1 A block diagram illustrating a specific implementation method for an aircraft.

[0025] Figure 17 This is a block diagram of a computing environment according to the present disclosure, which includes computing devices configured to support aspects of computer-implemented methods and computer-executable program instructions (or code). Detailed Implementation

[0026] Interference in the environment (such as jamming signals (e.g., intentional radio frequency interference to Global Navigation Satellite System (GNSS) signals) or spoofing signals) can affect a GNSS receiver's ability to receive signals from GNSS. Interference (e.g., jamming and / or spoofing) can reduce or eliminate a GNSS receiver's ability to calculate position, navigation, and timing (PNT) data. In some cases, the GNSS receiver or its operator may not be aware that the GNSS receiver is located in a geographic area where interference is occurring.

[0027] The aspects disclosed herein provide systems and methods for identifying interference associated with navigation signals. For example, the system is configured to acquire navigation system quality information from multiple data sources and generate navigation signal interference map information based on the navigation system quality information. For illustration, the system can aggregate first data from a first data source among multiple data sources. In some implementations, the first data is associated with information from one or more aircraft. In some such implementations, the first data source among multiple data sources may include an Automatic Dependent Surveillance-Broadcast (ADS-B) data source, a Quick Access Recorder (QAR) data source, or a combination thereof. Additionally or alternatively, the first data includes Navigation Integrity Category (NIC) data, Navigation Accuracy Category-Location (NACp) data, or a combination thereof. In some implementations, the system aggregates the first data about one or more areas (such as one or more geographic regions). For illustration, the system can aggregate the first data based on one or more parameters (such as integrity threshold, area size, vertical granularity, time period, or a combination thereof) to generate aggregated first data.

[0028] The system can also identify one or more areas associated with low-quality navigation signals based on second data from a second data source among multiple data sources. The second data source among the multiple data sources may include a Receiver Autonomous Integrity Monitoring (RAIM) data source, a System Wide Area Information Management (SWIM) data source, or a combination thereof. The system can discard portions of the aggregated first data based on one or more identified areas to generate navigation signal interference map information indicating radio frequency interference areas associated with GNSS navigation signals. Therefore, the system is capable of generating data (e.g., navigation signal interference map information) associated with one or more interference areas of the GNSS system, where the data is based on multiple data sources.

[0029] In some implementations, the system is configured to identify sources of interference, areas where spoofed navigation signals exist, or combinations thereof. For example, to identify sources of interference, the system can apply a model to navigation signal interference map information to identify interference patterns and corresponding locations of sources. As another example, to identify the presence of spoofed navigation signals, the system can detect changes in the signal quality of navigation signals in an area over time. To illustrate, the presence of spoofed navigation signals in an area can be identified based on determining that a first signal quality in the area before interference is detected is less than a second signal quality in the area after interference is detected. In some other implementations, the system can also track / detect aircraft in flight in real-time or near real-time and generate notifications of data integrity events (e.g., navigation signal interference).

[0030] In some implementations, the system is configured to output navigation signal interference map information. For example, the navigation signal interference map information may be output to a GNSS receiver or another device. In some implementations, the navigation signal interference map information is provided to a flight planning system to avoid potential congestion areas, to the cockpit (e.g., the electronic flight bag (EFB)) for crew situational awareness of congestion / spoofing activities in the flight path, or to maintenance personnel for additional checks of the aircraft's GNSS equipment after a congestion has occurred. Additionally or alternatively, the navigation signal interference map information may be used to generate an interference map that indicates navigation signal degradation in an area (e.g., a geographic area and / or one or more vertical altitude layers of the geographic area). In some implementations, the interference map may be presented or adjusted based on the selection of one or more parameters such as integrity threshold, area size, vertical granularity, time period, or combinations thereof.

[0031] One benefit of the disclosed systems and methods is that they can identify one or more areas of interference (e.g., jamming or spoofing) involving GNSS signals, based on data from multiple sources (e.g., ADS-B data sources and RAIM data sources). The identified areas may correspond to geographic areas (and the airspace above these geographic areas) with degraded GNSS integrity. Additionally or alternatively, the disclosed systems and methods can also identify the locations of sources generating jamming and / or spoofing signals. In some implementations, the navigation signal interference map information generated by the systems and methods can enable the operator of a GNSS receiver to determine when the GNSS receiver is approaching the affected area and to use conventional navigation aids other than the GNSS receiver to determine location information, navigation information, timing information, or combinations thereof.

[0032] The accompanying drawings and the following description illustrate specific exemplary embodiments. It should be understood that those skilled in the art will be able to design various arrangements, although not explicitly described or shown herein, that embody the principles described herein and are included within the scope of the claims following this description. Furthermore, any examples described herein are intended to aid in understanding the principles of this disclosure and are not intended to be limiting. Therefore, this disclosure is not limited to the specific embodiments or examples described below, but is defined by the claims and their equivalents.

[0033] The specific implementation is described herein with reference to the accompanying drawings. Throughout the description and the drawings, common features are indicated by common reference numerals. In some drawings, multiple instances of a particular type of feature are used. Although these features are physically and / or logically different, the same reference numerals are used for each feature, and different instances are distinguished by the addition of letters to the reference numerals. When a feature is mentioned herein as a group or type (e.g., when a specific feature is not mentioned), reference numerals are used without distinguishing letters. However, when a specific feature among multiple features of the same type is referenced herein, reference numerals are used with distinguishing letters. For example, referring to… Figure 6 Multiple representations are shown and associated with reference numerals 605A and 605B. When referring to a particular one of these designs, such as the first representation 605A, the distinguishing letter "A" is used. However, when referring to any one of these representations or as a group, reference numeral 605 is used without the distinguishing letter.

[0034] As used herein, different terms are used only for the purpose of describing a particular implementation and are not intended to be restrictive. For example, unless the context clearly indicates otherwise, the singular forms “a,” “an,” and “the” are intended to also include the plural forms. Furthermore, some features described herein are singular in some implementations and plural in others. For illustration, Figure 1 It describes a system that includes one or more data sources ( Figure 1 The term "data source 108" in System 100 indicates that in some implementations, System 100 includes a single data source 108, while in other implementations, System 100 includes multiple data sources 108. For ease of reference herein, this feature is generally introduced as "one or more" features and is subsequently referred to in the singular or optional plural unless an aspect relating to multiple features is described.

[0035] The terms “comprise,” “comprises,” and “comprising” are used interchangeably with “include,” “includes,” or “including.” Furthermore, the term “wherein” is used interchangeably with the term “where.” As used herein, “exemplary” indicates an example, implementation, and / or aspect, and should not be construed as limiting or indicating a preference or preferred implementation. As used herein, ordinal terms used to modify elements (such as structures, components, operations, etc.) (e.g., “first,” “second,” “third,” etc.) do not themselves indicate any priority or order of that element relative to another element, but merely distinguish that element from another element with the same name (other than the use of ordinal terms). As used herein, the term “set” refers to a grouping of one or more elements, and the term “multiple” refers to multiple elements.

[0036] As used herein, the terms “generate,” “calculate,” “use,” “select,” “access,” “aggregate,” and “determine” are interchangeable unless the context otherwise indicates. For example, “generate,” “calculate,” or “determine” a parameter (or signal) can refer to actively generating, calculating, or determining a parameter (or signal) or can refer to using, selecting, or accessing a parameter (or signal) that has already been generated, such as through another component or device. As used herein, “coupled” can include “communication coupling,” “electrical coupling,” or “physical coupling,” and can also (or alternatively) include any combination thereof. Two devices (or components) can be coupled directly or indirectly (e.g., communicationally coupled, electrically coupled, or physically coupled) via one or more other devices, components, wires, buses, networks (e.g., wired networks, wireless networks, or combinations thereof). Electrically coupled devices (or components) can be in the same or different devices and, by way of illustrative, non-limiting example, can be connected via electronics, one or more connectors, or inductive coupling. In some implementations, two devices (or components) that are communicationally coupled (e.g., in electrical communication) can directly or indirectly send and receive electrical signals (digital or analog signals), for example, via one or more wires, buses, networks, etc. As used herein, "direct coupling" is used to describe two devices coupled (e.g., communication coupling, electrical coupling, or physical coupling) without any intermediate components.

[0037] As used herein, the term “machine learning” should be understood to have either its usual or conventional meaning within the fields of computer science and data science, including, for example, the process or technique by which one or more computers can learn to perform an operation or function without being explicitly programmed to do so. As a typical example, machine learning can be used to enable one or more computers to analyze data to identify patterns in the data and generate results based on that analysis. For some types of machine learning, the generated results include data indicating the underlying structure or patterns of the data itself. Such techniques include, for example, so-called “clustering” techniques, which identify clusters (e.g., groupings of data elements).

[0038] For some types of machine learning, the resulting output includes a data model (also known as a "machine learning model" or simply a "model"). Typically, a model is generated using a first dataset to facilitate the analysis of a second dataset. For example, the first portion of a large dataset can be used to generate a model that can then be used to analyze the remaining portion of the large dataset. As another example, historical datasets can be used to generate models that can be used to analyze future data.

[0039] Because a model can be used to evaluate a collection of data different from the data used to generate the model, it can be viewed as a type of software (e.g., instructions, parameters, or both) that is automatically generated by a computer during the machine learning process. Accordingly, a model can be portable (e.g., it can be generated at a first computer and then moved to a second computer for further training, for use, or both). Additionally, a model can be combined with one or more other models to perform a desired analysis. For illustration, first data can be provided as input to a first model to generate first model output data, and this first model output data can be provided as input (alone, with the first data, or with other data) to a second model to generate second model output data indicating the results of the desired analysis. Depending on the analysis and data involved, different combinations of models can be used to generate such results. In some examples, multiple models can provide input to the model output of a single model. In some examples, a single model provides its model output as input to multiple models.

[0040] Examples of machine learning models include, but are not limited to, perceptrons, neural networks, support vector machines, regression models, decision trees, Bayesian models, Boltzmann machines, adaptive neurofuzzy inference systems, and combinations, sets, and variants of these and other types of models. Variations of neural networks include, for example, but not limited to, prototype networks, autoencoders, transformers, self-attention networks, convolutional neural networks, deep neural networks, deep belief networks, etc. Variations of decision trees include, for example, but not limited to, random forests, reinforced decision trees, etc.

[0041] Because machine learning models are generated by computers based on input data, they can be discussed in terms of at least two distinct time windows—the creation / training phase and the runtime phase. During the creation / training phase, a model is created, trained, adapted, validated, or otherwise configured by a computer based on the input data (often referred to as "training data" during the creation / training phase). Note that a trained model corresponds to software that has been generated and / or refined during the creation / training phase to perform a specific operation (such as classification, prediction, encoding, or other data analysis or data synthesis operations). During the runtime phase (or "inference" phase), the model is used to analyze the input data to generate model outputs. The content of the model outputs depends on the type of model. For example, as a non-limiting example, a model can be trained to perform a classification task or a regression task. In some implementations, the model can be updated continuously, periodically, or occasionally, in which case training and runtime can be interleaved, or a version of the model can be used for inference while a copy is being updated, and subsequently, the updated copy can be deployed for inference.

[0042] In some implementations, machine learning techniques are used to train (or retrain) a previously generated model. In this context, "training" refers to adapting a model or its parameters to a specific dataset. Unless otherwise clarified from the specific context, the term "training" as used herein includes "retraining" or refining the model for a specific dataset. For example, training can include so-called "transfer learning." In transfer learning, a base model is trained using a general or typical dataset, and the base model can subsequently be refined (e.g., retrained or further trained) using a more specific dataset.

[0043] The dataset used during training is called the "training dataset" or simply "training data." This dataset can be labeled or unlabeled. "Labeled data" refers to data that has been assigned category labels indicating the groups or categories associated with that data, and "unlabeled data" refers to data that is not labeled. Typically, "supervised machine learning processes" use labeled data to train machine learning models, while "unsupervised machine learning processes" use unlabeled data; however, it should be understood that the labels associated with the data are simply another data element that can be used in any appropriate machine learning process. For illustration, many clustering operations can be performed using unlabeled data; however, such clustering operations can use labeled data by ignoring the labels assigned to the data or by treating the labels as the same as other data elements.

[0044] Training a model on a training dataset generally involves modifying the model's parameters with the goal of making the model's output possess specific characteristics based on the data input to the model. To distinguish it from model generation operations, model training may be referred to as optimization or optimization training in this paper. In this context, "optimization" refers to improving a metric and does not imply finding an ideal value for that metric (e.g., a global maximum or global minimum). Examples of optimization trainers include, but are not limited to, backpropagation trainers, derivative-free optimizers (DFO), and extreme learning machines (ELM). As an example of training a model, during supervised training of a neural network, input data samples are associated with labels. When input data samples are fed to the model, the model generates output data, comparing this output data with the labels associated with the input data samples to generate error values. The model's parameters are modified to attempt to reduce (e.g., optimize) the error values. As another example of training a model, during unsupervised training of an autoencoder, data samples are fed to the autoencoder as input, and the autoencoder reduces the dimensionality of the data samples (a lossy operation) and attempts to reconstruct the data samples as output data. In this example, the output data is compared with the input data samples to generate the reconstruction loss, and the parameters of the autoencoder are modified in an attempt to reduce (e.g., optimize) the reconstruction loss.

[0045] Figure 1 This is a diagram illustrating an example of a system 100 according to some examples of the present disclosure, configured to identify interference associated with navigation signals. In some implementations, system 100 includes a computing device 102, a vehicle (e.g., an aircraft 103), one or more data sources 108 (hereinafter referred to as "data source 108"), and GNSS 106. Although the vehicle is described herein as an aircraft 103, by way of illustrative and non-limiting example, the vehicle may additionally or alternatively include a car, a ship, a spacecraft, an unmanned aerial vehicle, or other vehicles. Devices or components of system 100 may communicate via network 140. Network 140 may include a wired network, a wireless network, or a combination thereof.

[0046] GNSS 106 includes one or more satellites, such as representative satellite 107. GNSS 106 is configured to transmit positioning and timing data to one or more devices, such as devices that include GNSS receivers.

[0047] Aircraft 103 includes one or more components, such as navigation system 104 and data source 105. Aircraft 103 may also include one or more additional components, such as those referred to herein at least as described above. Figure 16 Further described. Navigation system 104 includes one or more means configured to provide flight information for automatic, semi-automatic, or manual flight operations of aircraft 103. For example, navigation system 104 may include components of GNSS 106 (e.g., a GNSS receiver), an inertial reference system (IRS) or inertial reference unit (IRU), a flight management system (FMS), a distance measurement device (DME), a very high frequency omnidirectional beacon (VOR) device, a localizer (LOC), or a combination thereof. The IRS may include accelerometers and gyroscopes and is configured to detect displacement on one or more axes and calculate the position of aircraft 103. The FMS is configured to determine and / or generate route data associated with the flight path of aircraft 103. Data source 105 may be configured to generate and record flight data associated with aircraft 103. For example, flight data may include or correspond to flight plans, weather information, airport information, or a combination thereof. In some implementations, data source 105 includes a fast access recorder (QAR) configured to generate QAR data. For example, the QAR can be configured to receive data from the Flight Data Acquisition Unit (FDAU). In some implementations, the data source 105 is coupled to or included in the navigation system 104.

[0048] Data source 108 may include an ADS-B data source, a RAIM data source, a SWIM data source, one or more historical data sources, one or more ground-based data sources, other data sources, or combinations thereof. In some implementations, data source 108 includes or corresponds to a service provider such as Flightradar24 or GPSJam.

[0049] ADS-B is a surveillance technology that uses satellite-based navigation technology and a broadcast communication data link for tracking aircraft (such as aircraft 103). ADS-B requirements can be defined by at least 14 CFR § 91.225 and / or 14 CFR § 91.227. An aircraft that is an ADS-B-enabled aircraft can use an ADS-B receiver (e.g., a GNSS receiver) to derive its precise geographic location from satellites of GNSS 106 (e.g., satellite 107) and combine that geographic location with status information such as altitude, track, speed, and flight number. An aircraft including an ADS-B receiver can receive (from other aircraft) one or more ADS-B position reports and transmit one or more ADS-B reports to other aircraft, ground stations, etc. ADS-B data sources can include one or more ADS-B performance reports (e.g., one or more ADS-B position reports), such as ADS-B performance reports (e.g., ADS-B position reports) from aircraft 103. In some implementations, the ADS-B data source includes real-time ADS-B data, historical ADS-B data, or a combination thereof. In some implementations, as illustrative and non-limiting examples, ADS-B data includes or indicates flight ID data, surface position, air position, barometric altitude data, altitude information, Navigation Integrity Category (NIC) data, Navigation Accuracy Category-Position (NACp) data, Navigation Accuracy Category-Speed ​​(NACv) data, Source Integrity Level (SIL) data, Source Integrity Level Supplements (SILs) data, System Design Assurance (SDA) data, Signal Quality Level (SQL), speed and position increment (speed / position Δ) data, airspeed, vertical speed data, or a combination thereof. Additionally or alternatively, ADS-B data may indicate whether the aircraft is on the ground, stationary on the ground, moving on the ground, or in the air.

[0050] RAIM is a technique configured to assess the integrity of individual signals collected and integrated by receiver units (e.g., GNSS receivers) employed in GNSS 106. In some implementations, RAIM data can provide satellite constellation accuracy information. For example, depending on the positions of GNSS 106 satellites, one or more areas may suffer from poor signal quality. Therefore, RAIM data can indicate the positions of satellites, the signal quality of the area, or a combination thereof. In some implementations, RAIM data that can be provided by RAIM data sources includes real-time RAIM data, historical RAIM data, predictive RAIM data (e.g., predictions / estimates of the future positions of one or more satellites), or a combination thereof.

[0051] A SWIM data source is an information-sharing platform configured to provide aviation, flight, weather, and surveillance information, such as near real-time information. SWIM requirements can be defined by one or more FAA standards and specifications, such as FAA-STD-0065 Rev. B, FAA-STD-073A, FAA-STD-074, FAA-STD-075, SWIM-002, or SWIM-005, as illustrative and non-limiting examples. As illustrative and non-limiting examples, SWIM data provided by a SWIM data source may include one or more data types, such as the Aeronautical Information Exchange Model (AIXM), Flight Information Exchange Model (FIXM), Weather Information Exchange Model (WXXM), International Aviation Meteorological Information Exchange Model (IWXXM), Aviation Industry Data Exchange (AIDX), or combinations thereof.

[0052] One or more ground-based data sources may include or correspond to Multi-Point Positioning (MLAT) data sources, Wide-Area Multi-Point Positioning (WAM) data sources, primary radar data sources, or combinations thereof. Additionally or alternatively, one or more ground-based data sources may include or correspond to Mode S radar data sources, Flight Warning (FLARM) data sources, Open Glider Network (OGN) data sources, or combinations thereof.

[0053] Computing device 102 is configured to communicate with one or more devices of system 100 (e.g., satellite 107 or aircraft 103) or data sources (e.g., data sources 105 or 108). In some implementations, computing device 102 includes a processing system. For illustration, computing device 102 may include one or more memories, one or more processors, or combinations thereof, as at least referred to herein. Figure 2Further description. In some examples, one or more processors are coupled to one or more memories, which include instructions that, when executed by the one or more processors, cause the one or more processors to perform the operations described herein. In some implementations, computing device 102 includes a computer, a server, a cloud computing device, or a combination thereof. A “computer” is generally a programmable or programmed machine to perform functions or operations. A server may include a distributed server (e.g., a group of servers), a cloud server, or a combination thereof. For illustration, computing device 102 may include multiple devices that are co-located or directly coupled to each other, or alternatively, multiple devices that communicate with each other across one or more computer networks (such as network 140).

[0054] The computing device 102 includes navigation system quality information 110 and navigation signal interference map information 112. The computing device 102 is configured to acquire the navigation system quality information 110. For example, the computing device 102 is configured to acquire the navigation system quality information 110 from one or more data sources (e.g., data source 105 or 108). The navigation system quality information 110 may include various types of data, such as first data having a first data type and second data having a second data type, wherein the first data type and the second data type are different data types. For illustration, the first data type may be an ADS-B data type and the second data type may be a RAIM data type.

[0055] In some implementations, computing device 102 is configured to acquire navigation system quality information 110 from multiple data sources. For illustration, computing device 102 may acquire (e.g., receive) first data acquired from a first data source among the multiple data sources and second data acquired from a second data source among the multiple data sources. For example, the first data source may include or correspond to an ADS-B data source, a QAR data source, or a combination thereof. In some examples, the first data source includes an ADS-B data source and the first data includes or indicates NIC data, NACp data, or a combination thereof. Additionally or alternatively, the second data source may include or correspond to a RAIM data source, a SWIM data source, or a combination thereof. In some examples, the second data source includes a RAIM data source and the second data includes or indicates a prediction / estimate of the integrity of navigation signals for the future positions of one or more satellites of GNSS 106.

[0056] Additionally or alternatively, computing device 102 is configured to generate navigation signal interference map information 112 based on navigation system quality information 110. Navigation signal interference map information 112 indicates one or more radio frequency interference areas associated with the navigation signals of GNSS 106. For illustration, in some examples, computing device 102 is configured to aggregate first data based on one or more parameters to generate aggregated first data. In some implementations, the first data is aggregated based on one or more parameters (as illustrative, non-limiting examples, such as integrity threshold, area size, vertical granularity, time period, or combinations thereof). Additionally or alternatively, computing device 102 may filter (e.g., remove) ground data from the first data to generate (e.g., retain) filtered first data, and aggregate the first filtered data. Computing device 102 is also configured to identify one or more areas associated with low-quality navigation signals based on second data, and discard portions of the aggregated first data based on these one or more areas to generate navigation signal interference map information 112. As an example, navigation signal interference map information 112 includes the remaining portion of the aggregated first data after a portion of the aggregated first data has been discarded.

[0057] In some implementations, computing device 102 is configured to output navigation signal interference map information 112. For example, the navigation signal interference map information 112 may be output to a GNSS receiver or another device. In some implementations, the navigation signal interference map information 112 is provided to a flight planning system, an air traffic control approval system, aircraft 103 (e.g., the cockpit of aircraft 103), aircraft operator, maintenance department (to perform maintenance and overhauls on aircraft 103), or a combination thereof. Additionally or alternatively, computing device 102 may include or be associated with an application programming interface (API) that enables one or more other devices or systems to acquire the navigation signal interference map information 112.

[0058] In some implementations, navigation signal interference map information 112 is used to generate an interference map indicating the degradation of navigation signals in an area (e.g., a geographic area and / or one or more vertical altitude layers of the geographic area). For example, the interference map may be generated by computing device 102 or by another device (such as a flight planning system, air traffic control approval system, aircraft 103, or a device of the aircraft operator or maintenance department) that receives the navigation signal interference map information 112. The interference map may be divided into geographic areas (which may include airspace) and may indicate the navigation signal quality of one or more areas. References herein are at least... Figures 5 to 9 and Figure 12Further describe examples or aspects of interference maps. In some implementations, interference maps may be presented or adjusted based on the selection of one or more parameters such as integrity threshold, region size, vertical granularity, time period, or combinations thereof.

[0059] In some implementations, computing device 102 is configured to identify sources of interference, areas where spoofed navigation signals exist, or combinations thereof. For example, to identify sources of interference, computing device 102 can apply a model to navigation signal interference map information to identify interference patterns and corresponding locations of interference sources, as at least referred to herein. Figure 2 or Figure 11 Further described. As another example, to identify the presence of spoofed navigation signals, computing device 102 can detect changes in the signal quality of navigation signals in an area over time. For illustration, the presence of spoofed navigation signals in an area can be identified based on the fact that the signal quality of the area before interference was detected is less than the signal quality of the area after the interference disappeared. In some such implementations, navigation signal interference map information 112 can indicate the location of the source of the interference, the area containing the spoofed navigation signal, or a combination thereof. In some other implementations, computing device 102 can also track / detect aircraft in flight in real-time or near real-time and generate notifications of data integrity events (e.g., navigation signal interference). As an illustrative, non-limiting example, computing device 102 can be configured to output notifications to aircraft in flight (e.g., aircraft 103), air traffic control approval systems, or maintenance departments.

[0060] For reference Figure 1 As described, this disclosure provides techniques to support the identification of interference associated with navigation signals. For example, navigation signal interference map information 112 can indicate one or more areas (and the airspace above one or more areas) where GNSS navigation signals are degraded. Navigation signal interference map information 112 enables a GNSS receiver operator to determine when the GNSS receiver is approaching an affected area and to use conventional navigation aids other than the GNSS receiver to determine position information, navigation information, timing information, or combinations thereof. Additionally or alternatively, this disclosure provides the identification of the location of sources generating blocking signals and / or areas including spoofing signals. For example, navigation signal interference map information 112 may also include or provide additional information indicating the location of sources generating blocking signals and / or indicating areas where spoofing signals exist.

[0061] Figure 2This is a diagram illustrating another example of a system 200 according to some examples of the present disclosure, configured to identify interference associated with navigation signals. System 200 may include, be included in, or correspond to system 100. System 200 includes a computing device 102 coupled to a plurality of data sources 260 and one or more devices 280 (hereinafter referred to as “devices 280”). The computing device 102 may be configured to communicate via one or more networks (such as...) Figure 1 The network 140 communicates with multiple data sources 260 and devices 280.

[0062] Multiple data sources 260 may include or correspond to Figure 1 The data sources 260 include data source 105, data source 108, or combinations thereof. Multiple data sources 260 include a first data source 262 and a second data source 264. For example, the first data source 262 may include an ADS-B data source, a QAR data source, or a combination thereof. The first data source 262 includes or is configured to generate first data 266. As an illustrative, non-limiting example, the first data 266 includes NIC data, NACp data, or a combination thereof. Additionally or alternatively, the second data source 264 may include a RAIM data source, a SWIM data source, or a combination thereof. The second data source 264 includes or is configured to generate second data 268. In some implementations, the first data 266 has a first data type and the second data 268 has a second data type different from the first data type. For example, the first data type may be an ADS-B data type, and the second data type may be a RAIM data type.

[0063] Device 280 may be any suitable electronic device configured to communicate with computing device 102 and / or at least one of the plurality of data sources 260. For example, device 280 may include or correspond to a flight planning system, an air traffic control approval system, aircraft 103 (e.g., corresponding to the cockpit of aircraft 103), devices associated with aircraft operators, devices associated with maintenance departments (to perform maintenance and overhauls on aircraft 103), or combinations thereof. In some implementations, device 280 includes or corresponds to at least one of the plurality of data sources 260, such as a first data source 262.

[0064] The device 280 may include one or more processors 282 (hereinafter referred to as "processor 282") coupled to memory 284, which includes instructions that, when executed by processor 282, cause processor 282 to perform specific functions (e.g., one or more operations). Processor 282 may be implemented as a single processor or multiple processors, such as in a multi-core configuration, a multi-processor configuration, a distributed computing configuration, a cloud computing configuration, or any combination thereof.

[0065] In some aspects, device 280 is configured to receive navigation signal interference map information 112 from computing device 102. For example, device 280 can generate an interference map indicating the degradation of navigation signals in an area (e.g., a geographic area and / or one or more vertical height layers of a geographic area) based on the navigation signal interference map information 112. Reference is made herein at least to Figures 5 to 9 and Figure 12 Further describe examples or aspects of the interference map.

[0066] The computing device 102 may include one or more processors 208 (hereinafter referred to as "processor 208") coupled to memory 206. Processor 208 may be implemented as a single processor or multiple processors, such as in a multi-core configuration, a multi-processor configuration, a distributed computing configuration, a cloud computing configuration, or any combination thereof. Memory 206 includes a computer-readable medium storing instructions that can be executed by processor 208. These instructions may be executed to initiate, perform, or control operations to aid in the identification of disturbances associated with navigation signals. For example, when executed by processor 208, these instructions cause processor 208 to perform specific functions (e.g., one or more operations).

[0067] Memory 206 includes navigation system quality information 110, one or more parameters 212 (hereinafter referred to as "parameter 212"), aggregated data 214, navigation signal interference map information 112, data integrity event information 218, one or more models or patterns 220, interference sources 221, spoofing information 225, or combinations thereof. Navigation system quality information 110 may be received from multiple data sources 260. Parameter 212 may include or indicate a threshold 222, area size 224, vertical granularity 226, and time period 228. Threshold 222 may include or indicate a GNSS signal quality rating. In some implementations, threshold 222 (e.g., a NIC / NACp threshold) corresponds to the NACp encoding and may be defined by 14 CFR § 91.227. For illustration, threshold 222 may be an integrity threshold with values ​​between 0 and 11, where 0 indicates poor or terrible quality, and 11 indicates good quality. As an illustrative, non-limiting example, threshold 222 may be set to a value of 7. In some implementations, the threshold may include a range of values.

[0068] Region size 224 may include or indicate dimensions (e.g., length), area, or a combination thereof. In some implementations, the region has a shape, and region size 224 indicates the characteristics of the shape. For example, the shape may be hexagonal, such as a perfect hexagon with six equal sides and six equal angles. In some implementations, multiple regions may be arranged to form a grid, such as a hexagonal grid. Vertical granularity 226 may include or indicate the number of vertical layers (e.g., count). For example, the airspace above a region of a map may be divided into one or more layers (e.g., one or more vertical height layers). In some implementations, each of the one or more layers corresponds to a different elevation and / or elevation range. As an illustrative, non-limiting example, vertical granularity 226 may include one hundred different layers. It should be noted that, as referenced herein at least... Figure 11 Furthermore, the number of vertical layers enables a search to be performed on the navigation signal interference map information 112 to identify the location of the source of the interference. As an illustrative and non-limiting example, time period 228 can indicate a time quantity, such as minutes, hours, days, weeks, months, etc. Time period 228 can indicate a previous time quantity, a future time quantity, or a combination thereof.

[0069] Aggregated data 214 may include or indicate navigation system quality information 110 aggregated based on parameter 212. Data integrity event information 218 includes or indicates changes in interference with navigation signals associated with the aircraft in flight. For illustration, data integrity event information 218 may indicate when the quality of navigation signals received by the aircraft in flight is less than or equal to a threshold, such as threshold 222. In some implementations, processor 208 is configured to track the aircraft in flight and generate data integrity event information 218 based on the quality of navigation signals received by the aircraft in flight being less than or equal to a threshold. One or more models or patterns 220 include or indicate the corresponding locations of interference patterns and sources of interference. References herein are at least... Figure 11 Further description of one or more examples of models or patterns 220. Interference source 221 indicates the location of the source of the interference. Spoofing information 225 indicates the area with spoofed navigation signals.

[0070] Processor 208 may be implemented as a single processor or multiple processors, such as in a multi-core configuration, a multi-processor configuration, a distributed computing configuration, a cloud computing configuration, or any combination thereof. Processor 208 may include one or more components or modules (e.g., a computer program or a set of computer program instructions) configured to perform one or more functions, including aggregator 240, one or more filters 242 (hereinafter referred to as "filter 242"), map generator 244, source identifier 246, and spoofing identifier 248.

[0071] Aggregator 240 is configured to aggregate navigation system quality information 110 to generate aggregated data 214. In some examples, aggregator 240 aggregates navigation system quality information 110 about one or more regions (such as one or more geographic regions). For illustration, aggregator 240 may aggregate navigation system quality information 110 based on parameter 212 to generate aggregated data 214. For illustration, navigation system quality information 110 may be aggregated based on threshold 222, region size 224, vertical granularity 226, time period 228, or a combination thereof.

[0072] Filter 242 is configured to filter data such as navigation system quality information 110, aggregated data 214, or a combination thereof. In some examples, filter 242 is configured to filter navigation system quality information 110 to remove (e.g., discard) ground information, such as information associated with an aircraft not in flight. After removing ground information, aggregator 240 can aggregate the remaining navigation system quality information 110. As another example, filter 242 is configured to filter aggregated data 214 based on one or more of a plurality of data sources 260 (such as a second data source 264) to generate navigation signal interference map information 112. For illustration, the second data source 264 may include or correspond to a RAIM data source, a SWIM data source, or a combination thereof. In some implementations, data may indicate one or more areas of poor-quality navigation signals based on or associated with the positions of one or more satellites of GNSS 106. In some of these implementations, in order to generate navigation signal interference map information 112, filter 242 is configured to filter aggregated data 214 based on the positions of one or more GNSS 106 satellites to remove (e.g., discard) data corresponding to poor-quality navigation signals.

[0073] Map generator 244 is configured to generate a map based on navigation signal interference map information 112. Although map generator 244 is described as being included in computing device 102, in other implementations, map generator 244 may be additionally or alternatively included in device 280. Examples of one or more maps generated by map generator 244 are referred to herein at least in part. Figures 5 to 9 , Figure 12 or Figure 13 Let me describe it further.

[0074] Source identifier 246 is configured to determine interference source 221 indicating the location of a source of interference. For example, source identifier 246 may be configured to perform a pattern search on navigation signal interference map information 112 to identify the source of interference. To illustrate, source identifier 246 may determine, based on navigation signal interference map information 112, the interference value associated with each vertical height layer in the set of vertical height layers, for a geographic region. Source identifier 246 then performs a comparison between the set of interference values ​​for the set of vertical height layers and interference patterns (such as one or more models or patterns of pattern 220). Source identifier 246 may identify the source of interference based on the result of the comparison, such as a match between the set of interference values ​​for the set of vertical height layers and the interference pattern. Additionally or alternatively, in some other implementations, source identifier 246 applies a model (one or more models or patterns of pattern 220) to navigation signal interference map information 112 to identify the source of interference. To illustrate, the model may be a trained model configured to determine the source of interference based on navigation signal interference map information 112. For example, a model may include one or more artificial intelligence (AI) or machine learning (ML) models.

[0075] Spoofing identifier 248 is configured to determine spoofing information 225 indicating an area with spoofed navigation signals. It should be noted that spoofing attacks may be difficult to detect on an aircraft (e.g., aircraft 103) based on ADS-B generated by the aircraft. However, the presence of a spoofing signal may occur after a jamming attack in which a GNSS receiver is unlocked (e.g., disconnected) from navigation signals from GNSS and then reconnects to the spoofing signal received at the GNSS receiver with more power than the navigation signals from GNSS. Therefore, spoofing identifier 248 can attempt to detect spoofing signals associated with a jamming attack. For example, spoofing identifier 248 can identify spoofed navigation signals based on navigation signal interference map information 112. For illustration, spoofing identifier 248 can identify areas with jamming events associated with radio frequency interference of one or more navigation signals based on navigation signal interference map information 112. In some implementations, spoofing identifier 248 determines a first signal quality value of a first navigation signal associated with the area prior to the jamming event. The deception detector 248 identifies the end of a blocking event and determines a second signal quality value of a second navigation signal associated with the area after the end of the blocking event. The deception detector 248 performs a comparison based on the first and second signal quality values ​​and can determine whether the second navigation signal is a deception navigation signal based on the comparison result. For illustration, the deception detector 248 can determine that the area includes a deception navigation signal based on a result indicating that the second signal quality value is greater than the first signal quality value.

[0076] System 200 may also include Figure 2 Components not shown. For example, computing device 102 may also include a receiver configured to receive navigation system quality information 110 from multiple data sources 260. As another example, system 200 may also include one or more input / output interfaces, one or more network interfaces, etc. Furthermore, although... Figure 2 The memory 206 is shown to store specific data, but without departing from the scope of this disclosure, more, less and / or different data may exist in the memory 206.

[0077] Furthermore, despite Figure 2 Specific operations occurring within computing device 102 are illustrated, but these operations can be performed by other components of system 200 without departing from the scope of this disclosure. For example, one or more components external to computing device 102 may be configured to control or otherwise incorporate some or all of the following: navigation system quality information 110, parameters 212, aggregated data 214, navigation signal interference map information 112, data integrity event information 218, one or more models or patterns, interference sources 221, spoofing information 225, or combinations thereof. Such components may be located remotely from computing device 102 and accessed via a modem of computing device 102.

[0078] Furthermore, despite Figure 2 The computing device 102 and the multiple data sources 260 and device 280 are shown as separate, but other configurations are possible without departing from the scope of this disclosure. For example, the computing device 102 may be integrated into device 280, such as an air traffic control approval system. As another example, one or more components of the computing device 102 may be distributed across multiple computing devices (e.g., a group of servers).

[0079] In some implementations, device 280 may request navigation signal interference map information 112, data integrity event information 218, interference source 221, spoofing information 225, or a combination thereof from computing device 102. The request may include parameters 212, such as values ​​for threshold 222, region size 224, vertical granularity 226, time period 228, or a combination thereof.

[0080] In some implementations, device 280 may be configured to generate a map based on navigation signal interference map information 112, data integrity event information 218, interference source 221, deception information 225, or a combination thereof. For example, the map may include or correspond to at least those referenced herein. Figures 5 to 9 , Figure 12 or Figure 13One or more maps are described further. In some implementations, device 280 may select or adjust parameters 212 in association with navigation signal interference map information 112, data integrity event information 218, interference source 221, deception information 225, or a combination thereof received from computing device 102. Therefore, an operator of device 280 may be able to adjust the map to identify information of interest to the operator.

[0081] Reference Figure 3 , Figure 3 This is a functional diagram illustrating an example of a system 300 according to some examples of the present disclosure, configured to identify interference associated with navigation signals. System 300 may include or correspond to system 100 or 200. System 300 includes a computing device 102 and a plurality of data sources 260. The computing device 102 may be configured to communicate with the plurality of data sources 260 and another device (such as device 280) via one or more networks.

[0082] Multiple data sources 260 include ADS-B data source 362, historical ADS-B data source 364, QAR data source 366, RAIM data source 368, historical RAIM data source 370, SWIM data source 372, or combinations thereof. In some implementations, ADS-B data source 362, historical ADS-B data source 364, QAR data source 366, or combinations thereof correspond to a first data source 262. Additionally or alternatively, RAIM data source 368, historical RAIM data source 370, SWIM data source 372, or combinations thereof correspond to a second data source 264.

[0083] Computing device 102 is configured to receive first data from ADS-B data source 362, historical ADS-B data source 364, or a combination thereof. For example, the first data may be received by processor 208, memory 206, or a combination thereof. In some implementations, the first data includes one or more ADS-B reports. The first data may include or correspond to at least a first portion of navigation system quality information 110. Data from ADS-B data source 362 may include real-time or near-real-time data, and data from historical ADS-B data source 364 may include previously generated (e.g., non-real-time) ADS-B data reports. ADS-B reports may include information about GNSS position uncertainty. For illustration, typical indications of GNSS position uncertainty include NIC, NACp, or a combination thereof.

[0084] In box 310, computing device 102 is configured to filter ground information from the first data. For example, Figure 2 The processor 208 (e.g., Figure 2Filter 242) can remove one or more ADS-B reports associated with ground aircraft from the first data to generate filtered first data.

[0085] In block 312, computing device 102 is configured to aggregate the filtered first data. For example, computing device 102 (e.g., aggregator 240) can aggregate the filtered first data to generate aggregated data 214. For illustration, computing device 102 can aggregate the filtered first data based on parameter 212. In some implementations, the filtered first data is aggregated based on a time period 228 (such as one hour).

[0086] In some implementations, computing device 102 may also receive QAR data from QAR data source 366. The QAR data may also be aggregated with filtered first data to generate aggregated data 214. In some implementations, the aggregated data 214 is aggregated into one or more regions based on region size 224, such that the aggregated data 214 has a resolution less than or equal to the ADS-B receiver detection range and / or standard airway spacing. In some implementations, the aggregated data 214 is generated according to or based on a geospatial indexing system, such as H3 (Hexagonal Hierarchical Geospatial Index) developed by Uber, as an illustrative, non-limiting example.

[0087] The computing device 102 is configured to receive second data from RAIM data source 368, historical RAIM data source 370, SWIM data source 372, or a combination thereof. For example, the second data may be received by processor 208, memory 206, or a combination thereof. In some implementations, the second data includes one or more RAIM data (e.g., GNSS outage information) or RAIM predictions (e.g., low-quality prediction information based on satellite constellation positions). The second data may include or correspond to at least a second portion of the navigation system quality information 110. Data from RAIM data source 368 may include real-time or near-real-time data, and data from historical RAIM data source 370 may include previously generated (e.g., non-real-time) RAIM data.

[0088] Reference Figure 4 , Figure 4 This is an illustration of an example map 400 according to some examples of this disclosure, representing the predicted satellite coverage of a navigation system. For example, the predicted satellite coverage may be based on RAIM data from RAIM data source 368, historical RAIM data source 370, or a combination thereof. Map 400 uses shading to indicate the degree of low-quality navigation signaling, wherein one or more areas 402 (shaded black) are identified as having or being predicted to have GNSS outage information or navigation signals less than or equal to a threshold due to satellite constellation positioning.

[0089] Return to reference Figure 3 In block 314, computing device 102 is configured to filter aggregated data 214 based on a second data filter. For example, processor 208 (e.g., filter 242) can remove data associated with one or more areas (e.g., one or more areas 402) that have or are predicted to have GNSS outages or navigation signals less than or equal to a threshold. Thus, data from low-quality areas can be removed as attributable to satellite movement rather than due to interference / blockage, thereby reducing or eliminating false alarms of interference. By removing data associated with one or more geographic areas (e.g., one or more areas 402), in block 316, computing device 102 can be configured to generate navigation signal interference map information 112. Computing device 102 can also output navigation signal interference map information 112. For example, navigation signal interference map information 112 can be output to another device, such as aircraft 103, device 280, or a combination thereof. The other device can generate or present an interference map based on navigation signal interference map information 112.

[0090] In block 318, computing device 102 is configured to perform an interference source search. For example, computing device 102 (e.g., source identifier 246 or spoofing identifier 248) may perform an interference source search to identify or generate an interference source 322 (e.g., interference source information). Interference source 322 may indicate the location of the source of interference or an area where spoofed navigation signals exist. For example, interference source 322 may include or correspond to interference source 221 or spoofing information 225. For illustration, computing device 102 may use one or more models or patterns 220 to perform an interference source search on navigation signal interference map information 112. Computing device 102 may also output interference source 322. For example, interference source 322 may be output to another device, such as aircraft 103, device 280, or a combination thereof. Other devices may generate or present a map (such as an interference map) that indicates the location of the source of interference or an area where spoofed navigation signals exist based on interference source 322.

[0091] In some implementations, one or more operations described with reference to blocks 312, 314, 316, 318, or combinations thereof can be performed in real time or near real time. For example, computing device 102 can generate navigation signal interference map information 112, interference source 322, or a combination thereof, and output it to another device.

[0092] In some implementations, at block 330, computing device 102 is configured to determine whether to track an aircraft, such as an aircraft in flight. For example, an aircraft in flight may include or correspond to aircraft 103.

[0093] In block 332, computing device 102 is configured to monitor an aircraft. For example, computing device 102 can track and / or detect an aircraft to identify whether the aircraft is experiencing navigation signal congestion. For illustration, computing device 102 can detect the aircraft based on ADS-B data, QAR data, or a combination thereof received from the aircraft. As part of monitoring the aircraft, computing device 102 can compare navigation signal quality information (from ADS-B data, QAR data, or a combination thereof) with a threshold to identify data integrity event information 218. For example, computing device 102 can determine that the aircraft is experiencing navigation signal congestion based on navigation signal quality information being less than or equal to a threshold. Computing device 102 can also output data integrity event information 218. For example, data integrity event information 218 can be output to another device, such as aircraft 103, device 280, or a combination thereof. Other devices can generate or present a map (such as an interference map) that indicates, based on data integrity event information 218, that the aircraft is experiencing navigation signal interference. In some implementations, the map can indicate the level of interference along the aircraft's flight path.

[0094] For reference Figure 3 As described, this disclosure provides techniques to support the identification of interference associated with navigation signals. For example, navigation signal interference map information 112 can indicate one or more areas (and the airspace above one or more areas) where GNSS navigation signals are degraded. Navigation signal interference map information 112 enables the operator of a GNSS receiver to determine when the GNSS receiver is approaching an affected area and to use conventional navigation aids other than the GNSS receiver to determine position information, navigation information, timing information, or combinations thereof. Additionally or alternatively, this disclosure provides the identification of the location of sources generating jamming signals and / or areas including spoofing signals. In some implementations, this disclosure may also receive navigation system quality information 110 from multiple data sources 260 and aggregate that data to identify one or more areas where jamming is occurring. This disclosure may also provide the navigation signal interference map information 112, interference source 322, and / or data integrity event information 218 to devices (e.g., aircraft 103 and / or device 280) to enable the devices to generate maps or alerts that can be used by the operator of the device.

[0095] Reference Figure 5 , Figure 5This is an illustration of an example representation 500 of a region according to some examples of this disclosure, the representation of which indicates interference associated with navigation signals. For example, the region may correspond to a geographic area or block included in a map, such as an interference map generated based on navigation signal interference map information 112. In some implementations, the representation is based on or associated with a geospatial indexing system, such as the H3-Hexagonal Hierarchical Geospatial Index developed by Uber (a registered trademark of Uber Technologies, Inc., Delaware).

[0096] As an illustrative and non-limiting example, it is suggested that 500 has a shape such as a triangle, square, circle, pentagon, hexagon, or other shapes. In some implementations, the shape includes those having... Figure 2 The region size 224 is associated with or is determined by Figure 2 The size of the region is indicated by the length of at least one edge. In some implementations, each edge of the region has the same length.

[0097] The designation 500 may include one or more layers (e.g., one or more vertical height layers corresponding to the airspace above the surface). In some implementations, the number (e.g., count) of the one or more layers is associated with or indicated by a vertical granularity 226. For example, as shown, designation 500 includes three layers: a first layer 502, a second layer 504, and a third layer 506. Each of the one or more layers corresponds to a different elevation (e.g., height) above the area. For illustration, the first layer 502 may correspond to an elevation of 0 to 15,000 feet (ft), the second layer 504 may correspond to an elevation of 15,000 to 30,000 ft, and the third layer 506 may correspond to an elevation of 30,000 to 45,000 ft. Additionally or alternatively, each layer may be colored, shaded, and / or cross-lined to indicate a quality value, such as the quality value of a navigation signal. In some implementations, each of the one or more layers representing 500 indicates the same quality value. In other implementations, at least one of the layers representing 500 indicates a different quality value than another of the layers representing 500. In some implementations, representing 500 may have a single layer, and the single layer may be two-dimensional or three-dimensional.

[0098] Reference Figure 6 , Figure 6 This is an illustration of an example of map 600 according to some examples of the present disclosure, which represents interference associated with navigation signals. Map 600 may include an interference map generated based on navigation signal interference map information 112.

[0099] Map 600 includes geographic region 602 and legend 604. Legend 604 indicates different values ​​of the quality of GNSS navigation signals, such as GNSS 106, using colors, shading, intersecting lines, or combinations thereof. In some implementations, the values ​​of the navigation signals range from 0 to 11, where 0 indicates poor or terrible quality, and 11 indicates good quality. Additionally or alternatively, these values ​​may be associated with or defined by 14 CFR § 91.227. It should be noted that map 600 and legend 604 may include portions of this range, such as values ​​less than or equal to 6.5, such as... Figure 6 As shown in the image.

[0100] Geographic region 602 may include or indicate representations 605A to 605D of multiple regions, such as a first representation 605A, a second representation 605B, a third representation 605C, and a fourth representation 605D. Representations 605A to 605D may include or correspond to representation 500. As shown, each of representations 605A to 605D includes multiple layers. The multiple layers may include or correspond to... Figure 5 One or more layers 502 to 506. A first designation 605A corresponds to a quality value between 6.25 and 6.5, a second designation 605B corresponds to a quality value between 5.7 and 6.25, and a third designation 605C corresponds to a quality value between 5.7 and 1. A fourth designation 605D includes a first layer 606 corresponding to a quality value between 6.25 and 6.5 and a second layer 608 corresponding to a quality value between 5.7 and 6.25.

[0101] Reference Figure 7 and Figure 8 , Figure 7 and Figure 8 This is an example illustration of a map based on some examples of this disclosure, which represents interference associated with navigation signals. Figure 7 This is a diagram showing another example of map 700, and Figure 8 This is another example of a map 800.

[0102] Maps 700 and 800 may include interference maps generated based on navigation signal interference map information 112. For example, maps 700 and 800 may include or correspond to map 600. Each of maps 700 and 800 includes a geographic region and a legend. For example, map 700 includes geographic region 702 and legend 704, and map 800 includes geographic region 802 and legend 804. Legends 704 and 804 may include or correspond to legend 604.

[0103] Map 700 has a first resolution, and map 800 has a second resolution. For illustration, each of maps 700 and 800 can be associated with the same time period (e.g., Figure 2 The second resolution is associated with a time period 228, and is a higher resolution than the first resolution. For example, the first resolution may be associated with a first value of parameter 212, and the second resolution may be associated with a second value of parameter 212. In some implementations, parameter 212 is vertical granularity 226. Additionally or alternatively, parameter 212 is region size 224. In some implementations, parameter 212 is set by computing device 102, aircraft 103, device 280, or its user.

[0104] Figure 9 This is an illustration of another example of a map 900 according to some examples of the present disclosure, representing interference associated with navigation signals. Map 900 may include an interference map generated based on navigation signal interference map information 112. Map 900 may include or correspond to maps 600, 700, or 800. In some implementations, map 900 has a third resolution. Compared to map 700 having a first resolution and / or map 800 having a second resolution, the third resolution of map 900 is higher than the first and / or second resolutions.

[0105] Map 900 includes geographic region 902 and legend 904. Geographic region 902 may include or correspond to geographic regions 602, 702, or 802. Legend 904 may include or correspond to legend 604, 704, or 804.

[0106] Legend 904 indicates colors, shading, crosshairs, or combinations thereof for different values ​​of the quality of navigation signals for GNSS (such as GNSS 106). In some implementations, the values ​​of the navigation signals range from 0 to 11, where 0 indicates poor or terrible quality, and 11 indicates good quality. Additionally or alternatively, these values ​​may be associated with or defined by 14 CFR § 91.227. It should be noted that Map 900 and Legend 904 may include portions of this range, such as values ​​less than or equal to 7.0. Figure 9 As shown in the image.

[0107] Geographic region 902 may include or indicate representations of multiple regions, such as representative representation 906. Representation 906 may include or correspond to representations 500 or 605. Representation 906 may include one or more layers.

[0108] Reference Figure 10 , Figure 10This is an example diagram of a graph 1000 illustrating factors associated with jamming interference according to some examples of this disclosure. Jamming can be based on transmitting jamming signals at high power to disrupt aircraft (such as...). Figure 1 The source of the GNSS-based equipment on the aircraft (103) generates a jamming signal (e.g., ground-based or air / space-based interference). Under free-space conditions, the jamming signal can propagate according to the Fries propagation formula, such that the power received at the aircraft's antenna is inversely proportional to the square of the distance. Therefore, the farther the jamming signal propagates from the source across the altitude range, the higher the power required to account for the spread of the jamming signal and the desired interference effect of the jamming signal on GNSS navigation signals (such as the navigation signals of GNSS 106).

[0109] Reference Figure 10 Chart 1000 illustrates an example of the blocking power, in watts (W), required for a navigation signal from a blocking source to completely disappear relative to a distance in meters (m) and an altitude in feet (ft). To generate Chart 1000, -30 dBm was used as the threshold power for complete disappearance of the navigation signal. In Chart 1000, blocking power levels below 1 W are depicted with triangles, blocking power levels between 1 W and 5 W are depicted with rectangles, and blocking power levels above 5 W are depicted with circles.

[0110] Reference Figure 11 , Figure 11 This is a diagram illustrating an example of a blocking pattern 1100 generated by a blocking source according to some examples of this disclosure. Blocking pattern 1100 may include or correspond to one or more models or patterns 220.

[0111] Blocking mode 1100 is associated with a blocking signal emitted from source 1101. The blocking signal propagates through different altitudes, as indicated by a first region 1102 at a first altitude, a second region 1104 at a second altitude, and a third region 1106 at a third altitude. It should be noted that each of the first region 1102, the second region 1104, and the third region 1106 is associated with a different vertical altitude layer of the same region. In other words, the first region 1102, the second region 1104, and the third region 1106 of blocking mode 1100 are associated with vertical granularity (e.g.,...). Figure 2 The vertical granularity 226 is associated with this. It should be noted that the blocking mode 1100 of the blocking signal is generally considered to be a conical mode. Therefore, the cross section of the blocking mode 1100 at the first region 1102 is smaller than the cross section of the blocking mode 1100 at the second region 1104 and / or the third region 1106.

[0112] In order to detect the source of interference (e.g., by...) Figure 2The computing device 102 (such as source identifier 246) can process or search the navigation signal interference map information 112 based on the obstruction pattern 1100, indicated by interference source 221. As an illustrative example, the computing device 102 (e.g., source identifier 246) can search for highly obstructed regions, such as regions with a quality value less than or equal to 7, by iteratively performing lower-height layers (which may be based on multiple layers of vertical granularity 226). Each highly obstructed region can be considered a candidate region. For each candidate region, the computing device 102 (e.g., source identifier 246) can analyze one or more regions in the next layer (e.g., the region above and / or one or more adjacent regions) to identify the spread of interference. The computing device 102 (e.g., source identifier 246) can iteratively continue analyzing each layer to identify a spread pattern that provides a match with obstruction pattern 1100 (e.g., one or more models or pattern 220). Based on the detected matches, the computing device 102 (e.g., source identifier 246) can identify the candidate regions as associated with the location of the source of interference. It should be noted that although the computing device 102 (e.g., source identifier 246) is described as analyzing one or more layers from lower to higher layers, the computing device 102 (e.g., source identifier 246) may also analyze one or more layers from higher to lower layers to identify sources of interference based on air or space.

[0113] Figure 12 This is an illustration of an example map 1200 according to some examples of this disclosure, which represents interference associated with navigation signals. Map 1200 corresponds to... Figure 9 Map 900. Map 1200 includes geographic region 1202 and legend 1204. Geographic region 1202 may include or correspond to geographic regions 602, 702, 802 or 902. Legend 1204 may include or correspond to legend 604, 704, 804 or 904.

[0114] Compared to map 900, map 1200 indicates a first location 1206 of a first source of interference, a second location 1207 of a second source of interference, and an area 1208 where a spoofing signal exists. The first location 1206 and the second location 1207 may include or correspond to interference source 221, interference source 322, or a combination thereof. The area 1208 where a spoofing signal exists may include or correspond to spoofing information 225, interference source 322, or a combination thereof.

[0115] Reference Figure 13 , Figure 13 This is an illustration of an example map 1300, which represents interference associated with the aircraft's navigation signals. Map 1300 may include or be generated based on navigation signal interference map information 112, data integrity event information 218, or a combination thereof.

[0116] Map 1300 includes geographic region 1302 and legend 1304. Geographic region 1302 may include or correspond to geographic regions 602, 702, 802, or 902. Legend 1304 may include or correspond to legend 604, 704, 804, or 904. Legend 904 indicates different values ​​of color, shading, intersecting lines, or combinations thereof for the quality of navigation signals for GNSS (such as GNSS 106). In some implementations, the values ​​of navigation signals range from 0 to 11, where 0 indicates poor or terrible quality, and 11 indicates good quality. Additionally or alternatively, these values ​​may be associated with or defined by 14 CFR § 91.227. It should be noted that map 900 and legend 904 may include portions of this range, such as values ​​less than or equal to 8.0, such as... Figure 13 As shown in the image.

[0117] As shown in the figure, map 1300 indicates the direction from east to west along the plane (such as... Figure 1 The regions are areas of varying quality values ​​for the navigation signals along the flight path of aircraft 103. For example, the regions include a first region 1310 associated with quality values ​​between 7.33 and 8.0, a second region 1312 associated with quality values ​​between 4.00 and 4.67, a third region 1314 associated with quality values ​​between 4.67 and 5.33, a fourth region 1316 associated with quality values ​​between 6.00 and 6.67, a fifth region 1318 associated with quality values ​​between 6.67 and 7.33, and a sixth region 1320 associated with quality values ​​between 7.33 and 8.0.

[0118] In some implementations, navigation signal interference map information 112, data integrity event information 218, or a combination thereof associated with map 1300 are generated in real time for use by aircraft 103. For illustration, computing device 102 can monitor aircraft 103 based on ADS-B data (e.g., NIC data), QAR data, or a combination thereof received from aircraft 103. Computing device 102 can generate and / or transmit alerts based on changes in the quality of navigation signals received by the aircraft. Computing device 102 can detect changes relative to the aircraft 103's travel along a flight path and generate alerts indicating quality changes, such as quality deterioration or quality improvement. As a first illustrative example, computing device 102 can detect changes relative to the aircraft 103's travel from a first area 1310 to a second area 1312 and generate a first alert indicating quality deterioration. As another illustrative example, computing device 102 can detect changes relative to the aircraft 103's travel from a fifth area 1318 to a sixth area 1320 and generate a second alert indicating quality improvement. In some implementations, alarms may be generated and / or transmitted to aircraft 103, device 280, or a combination thereof.

[0119] Figure 14 This is a flowchart illustrating an example of a method 1400 for identifying interference associated with navigation signals according to some examples of this disclosure. Method 1400 may be initiated, executed, or controlled by one or more processors that run instructions or by circuitry configured to cause the execution of one or more operations (such as that present in computing device 102, processor 208, or a combination thereof).

[0120] In some implementations, method 1400 includes, at block 1402, acquiring navigation system quality information from multiple data sources. For example, the multiple data sources include or correspond to data source 105 or 108, multiple data sources 260 (e.g., first data source 262 or second data source 264), ADS-B data source 362, historical ADS-B data source 364, QAR data source 366, RAIM data source 368, historical RAIM data source 370, SWIM data source 372, or combinations thereof. The navigation system quality information includes or corresponds to navigation system quality information 110. The navigation system quality information includes first data acquired from a first data source among the multiple data sources and second data acquired from a second data source among the multiple data sources. For example, the first data source may include or correspond to first data source 262, ADS-B data source 362, historical ADS-B data source 364, QAR data source 366, or combinations thereof. The second data source may include or correspond to second data source 264, RAIM data source 368, historical RAIM data source 370, SWIM data source 372, or combinations thereof.

[0121] In some implementations, method 1400 includes obtaining first data from navigation system quality information. For example, the first data source among multiple data sources includes the first data. The first data source among multiple data sources includes an ADS-B data source (e.g., ADS-B data source 362, historical ADS-B data source 364), a QAR data source (e.g., ADS-B data source 362, historical ADS-B data source 364), or a combination thereof. In some examples, the first data source includes an ADS-B data source. Additionally or alternatively, the first data includes NIC data, NACp data, or a combination thereof.

[0122] In some implementations, method 1400 includes obtaining second data from navigation system quality information. For example, the second data source among multiple data sources includes the second data. The second data source among multiple data sources includes a RAIM data source (e.g., RAIM data source 368 or historical RAIM data source 370), a SWIM data source (e.g., SWIM data source 372), or a combination thereof. In some examples, the second data source includes a RAIM data source. Additionally or alternatively, the first data has a first data type and the second data has a second data type. The second data type may be different from the first data type.

[0123] Method 1400 further includes, at block 1404, aggregating the first data based on one or more parameters to generate aggregated first data. For illustration, aggregator 240 may generate aggregated first data. For example, aggregated first data may include or correspond to aggregated data 214. One or more parameters may include or correspond to parameter 212, threshold 222, region size 224, vertical granularity 226, time period 228, or a combination thereof.

[0124] Method 1400 further includes, in block 1406, identifying one or more regions associated with the low-quality navigation signal based on the second data. The one or more regions may include or correspond to region 402.

[0125] Method 1400 includes, at block 1408, discarding portions of the aggregated first data based on the one or more regions to generate navigation signal interference map information. For illustration, filter 242 may discard portions of the aggregated first data. The navigation signal interference map information includes the remaining portion of the aggregated first data after the discarded portions. For example, the navigation signal interference map information may include or correspond to navigation signal interference map information 112. The navigation signal interference map information indicates areas of degraded navigation signals of GNSS (such as GNSS 106). For example, the navigation signal interference map information indicates radio frequency interference areas associated with GNSS navigation signals.

[0126] Method 1400 includes, in block 1410, outputting navigation signal interference map information. For example, the navigation signal interference map information may be output to aircraft 103, device 280, or a combination thereof.

[0127] In some implementations, method 1400 includes filtering ground data from first data to generate filtered first data. For illustration, filter 242 may filter ground data to generate filtered first data. In some such implementations, method 1400 may include aggregating the filtered first data based on one or more parameters to generate aggregated first data. For illustration, aggregator 240 may aggregate the filtered first data to generate aggregated first data, such as aggregated data 214.

[0128] In some implementations, method 1400 includes identifying an aircraft in flight that is at least partially associated with the filtered first data. The aircraft in flight may include or correspond to aircraft 103 or device 280. In some such implementations, method 1400 further includes retrieving additional data associated with the aircraft in flight from an ADS-B data source and comparing the additional data with an integrity threshold. For example, the integrity threshold may include or correspond to threshold 222. Based on the additional data that satisfies (e.g., is less than or equal to) the integrity threshold, a notification of a data integrity event is generated. Method 1400 may also include transmitting the notification. For example, the notification may be transmitted to the aircraft in flight or another device. The notification of the data integrity event may include or correspond to data integrity event information 218.

[0129] In some implementations, method 1400 includes performing a pattern search on navigation signal interference map information to identify sources of interference. For illustration, source identifyer 246 may perform a pattern search on navigation signal interference map information 112. For example, a pattern may include or correspond to one or more models or patterns 220. In some implementations, method 1400 generates interference source information indicating the location of a source of interference. The source information may include or correspond to interference source 221. Method 1400 may include outputting the interference source information together with the navigation signal interference map information. In some implementations, to perform the pattern search, method 1400 includes determining, for a geographic region and for each vertical height layer in a set of vertical height layers for that geographic region, an interference value associated with a vertical height layer based on the navigation signal interference map information. Method 1400 may perform a comparison between the set of interference values ​​in the set of vertical height layers and the interference patterns. The source of interference may be identified based on the result of the comparison.

[0130] In some implementations, method 1400 includes identifying spoofed navigation signals based on navigation signal interference map information. For illustration, spoofing detector 248 can identify spoofed navigation signals, such as spoofing information 225. To identify spoofed navigation signals, method 1400 may include identifying areas with congestion events (e.g., degradation of GNSS navigation signals) associated with radio frequency interference of one or more navigation signals based on navigation signal interference map information. Based on the identified congestion event, method 1400 determines a first signal quality value of a first navigation signal associated with the area prior to the congestion event. Method 1400 also includes identifying the end of the congestion event and determining a second signal quality value of a second navigation signal associated with the area after the end of the congestion event. In some implementations, method 1400 performs a comparison based on the first signal quality value and the second signal quality value, and determines whether the second navigation signal is a spoofed navigation signal based on the result of the comparison. For example, the second navigation signal may be determined to be a spoofed navigation signal based on a result indicating that the second signal quality value is greater than or equal to the first signal quality value.

[0131] In some implementations, an interference map is generated based on navigation signal interference map information. For example, method 1400 may include generating an interference map based on navigation signal interference map information. For illustration, map generator 244 may generate an interference map. Method 1400 may also include outputting an interference map. In some implementations, the interference map may include a plurality of hexagonal regions. At least one of the plurality of hexagonal regions indicates the amount of navigation signal degradation at each of one or more vertical height layers for the at least one hexagonal region. Additionally or alternatively, interference may indicate a source of interference, an area with deceptive navigation signals, or a combination thereof.

[0132] The above references can be implemented. Figure 14 The described methods are intended to achieve one or more of the technical advantages described in more detail above. For example, method 1400 can enable the identification of interference associated with navigation signals. For example, navigation signal interference map information can indicate one or more areas (and the airspace above one or more areas) where GNSS navigation signals are degraded. Furthermore, the navigation signal interference map information can enable the operator of a GNSS receiver to determine when the GNSS receiver is approaching an affected area and to use conventional navigation aids other than the GNSS receiver to determine position information, navigation information, timing information, or combinations thereof. Additionally or alternatively, the navigation signal interference map information may also include or provide additional information indicating the location of the source generating the blocking signal and / or indicating the area where spoofing signals exist.

[0133] Reference Figure 15 , showed Figure 1A flowchart illustrating an exemplary method 1500 for the lifecycle of an aircraft 103. The aircraft 103 includes a navigation system 104.

[0134] During pre-production, exemplary method 1500 includes, at 1502, the aircraft (such as reference) Figure 1 The specifications and design of the aircraft 103 are described. During the specification and design of the aircraft, method 1500 may include the specifications and design of the navigation system 104. In 1504, method 1500 includes material procurement, which may include procuring materials for the navigation system 104.

[0135] During production, method 1500 includes, at 1506, the manufacturing of parts and sub-components, and at 1508, the system integration of the aircraft. For example, method 1500 may include the manufacturing of parts and sub-components for navigation system 104 and the system integration of navigation system 104. At 1510, method 1500 includes the certification and delivery of the aircraft, and at 1512, putting the aircraft into service. Certification and delivery may include the certification of navigation system 104 to put navigation system 104 into service. When used by the customer, routine maintenance and overhauls of the aircraft may be scheduled (which may also include modifications, reconfigurations, refurbishments, etc.). At 1514, method 1500 includes performing maintenance and overhauls on the aircraft, which may include performing maintenance and overhauls on navigation system 104.

[0136] Each process of Method 1500 may be performed or conducted by a system integrator, a third party, and / or an operator (e.g., a customer). For the purposes of this specification, a system integrator may include, but is not limited to, any number of aircraft manufacturers and main system subcontractors; a third party may include, but is not limited to, any number of vendors, subcontractors, and suppliers; and an operator may be an airline, leasing company, military entity, service organization, etc.

[0137] Various aspects of this disclosure can be described in the context of examples of vehicles. Specific examples of vehicles are as follows: Figure 16 The aircraft shown is 1600.

[0138] exist Figure 16 In the example, aircraft 1600 includes a fuselage 1618 and an interior 1622 having multiple systems 1620. Examples of the multiple systems 1620 include one or more of a propulsion system 1624, an electrical system 1626, an environmental system 1628, a hydraulic system 1630, and a navigation system 104. Any number of other systems may be included. Any number of other systems may be included and / or may be omitted. Figure 16 One or more of the systems described herein. Figure 16In this example, navigation system 104 is configured to receive navigation signal interference map information 112 from computing device 102. Additionally or alternatively, navigation system 104 is configured to generate or present an interference map based on the navigation signal interference map information 112. For example, the interference map may include or correspond to... Figures 6 to 9 , Figure 12 or Figure 13 One or more maps. In some implementations, navigation system 104 is configured to receive notifications of data integrity events. The notification of a data integrity event may include or correspond to data integrity event information 218. For example, navigation system 104 may receive the notification from computing device 102.

[0139] Figure 17 This is a block diagram of a computing environment 1700 according to the present disclosure, which includes computing device 1710 configured to support aspects of computer-implemented methods and computer-executable program instructions (or code). For example, computing device 1710 or a portion thereof is configured to execute instructions to initiate, execute, or control references. Figures 1 to 16 One or more operations are described. Computing device 1710 may include or correspond to computing device 102.

[0140] Computing device 1710 includes one or more processors 1720. The one or more processors may include or correspond to processor 208. Processor 1720 is configured to communicate with system memory 1730, one or more storage devices 1740, one or more input / output interfaces 1750, one or more communication interfaces 1760, or any combination thereof. System memory 1730 may include or correspond to memory 206. System memory 1730 includes volatile memory devices (e.g., random access memory (RAM) devices), non-volatile memory devices (e.g., read-only memory (ROM) devices, programmable read-only memory, and flash memory), or both. System memory 1730 stores operating system 1732, which may include a basic input / output system for booting computing device 1710 and a complete operating system enabling computing device 1710 to interact with users, other programs, and other devices. System memory 1730 stores program data 1736 (system data), such as navigation system quality information 110, navigation signal interference map information 112, or combinations thereof.

[0141] System memory 1730 includes one or more application programs 1734 (e.g., instruction sets) that can be run by processor 1720. As an example, one or more application programs 1734 include those that can be run by processor 1720 to initiate, control, or execute references. Figures 1 to 16Instructions 1735 describe one or more operations. For illustration, one or more applications 1734 include instructions 1735 that can be run by processor 1720 to initiate, control, or execute one or more operations described by reference aggregator 240, filter 242, map generator 244, source identifier 246, deception identifier 248, or combinations thereof.

[0142] In a particular implementation, system memory 1730 includes a non-transitory computer-readable medium storing instructions that, when executed by processor 1720, cause processor 1720 to initiate, execute, or control operations to identify interference associated with navigation signals. These operations include acquiring navigation system quality information 110 from multiple data sources. Navigation system quality information 110 includes first data acquired from a first data source among the multiple data sources and second data acquired from a second data source among the multiple data sources. These operations also include aggregating the first data based on one or more parameters to generate aggregated first data, and identifying one or more areas associated with low-quality navigation signals based on the second data. These operations further include discarding portions of the aggregated first data based on the one or more areas to generate navigation signal interference map information 112. These operations include outputting navigation signal interference map information 112.

[0143] One or more storage devices 1740 include non-volatile storage devices, such as disks, optical disks, or flash memory devices. In a particular example, storage device 1740 includes both removable and non-removable memory devices. Storage device 1740 is configured to store an operating system, an image of the operating system, applications (e.g., one or more of applications 1734), and program data (e.g., program data 1736). In a particular aspect, system memory 1730, storage device 1740, or both include tangible computer-readable media. In a particular aspect, one or more of storage devices 1740 are external to computing device 1710.

[0144] One or more input / output interfaces 1750 enable computing device 1710 to communicate with one or more input / output devices 1770 to facilitate user interaction. For example, one or more input / output interfaces 1750 may include a display interface, an input interface, or both. For example, input / output interface 1750 is adapted to receive input from a user, input from another computing device, or a combination thereof. In some implementations, input / output interface 1750 conforms to one or more standard interface protocols, including serial interfaces (e.g., Universal Serial Bus (USB) interfaces or Institute of Electrical and Electronics Engineers (IEEE) interface standards), parallel interfaces, display adapters, audio adapters, or custom interfaces (“IEEE” is a registered trademark of Institute of Electrical and Electronics Engineers, Piscavenge, New Jersey, Inc.). In some implementations, input / output device 1770 includes one or more user interface devices and displays, including combinations of buttons, keyboards, pointing devices, displays, speakers, microphones, touchscreens, and other devices.

[0145] Processor 1720 is configured to communicate with device or controller 1780 via one or more communication interfaces 1760. For example, the one or more communication interfaces 1760 may include a network interface. Device or controller 1780 may include, for example, aircraft 103, device 280, one or more other devices, or any combination thereof. Device or controller 1780 may include or be configured to generate or display a jamming map 1782. This jamming map may include or correspond to... Figures 6 to 9 , Figure 12 or Figure 13 One or more of the maps. Additionally or alternatively, the device or controller 1780 may be configured to request navigation signal interference map information 112 from the computing device 1710. In some such implementations, the device or controller 1780 may generate an interference map based on the navigation signal interference map information 112.

[0146] In conjunction with the described system and method, an apparatus for assisting object design is disclosed, comprising components for acquiring navigation system quality information from multiple data sources. The navigation system quality information includes first data acquired from a first data source among the multiple data sources and second data acquired from a second data source among the multiple data sources. In some implementations, the components for acquiring the navigation system quality information may include or correspond to computing device 102, memory 206, processor 208, computing device 1710, processor 1720, system memory 1730, input / output interface 1750, communication interface 1760, one or more other circuits or devices configured to acquire navigation system quality information, or combinations thereof.

[0147] The device also includes components for aggregating first data based on one or more parameters to generate aggregated first data. For example, the components for aggregating the first data may include or correspond to computing device 102, processor 208, aggregator 240, computing device 1710, processor 1720, one or more other devices configured to aggregate the first data, or combinations thereof.

[0148] The device also includes components for identifying one or more areas associated with low-quality navigation signals based on second data. For example, the components for identifying one or more areas may include or correspond to computing device 102, processor 208, computing device 1710, processor 1720, one or more other devices configured to identify one or more areas, or combinations thereof.

[0149] The device also includes components for discarding a portion of the aggregated first data based on the one or more regions to generate navigation signal interference map information. For example, the components for discarding a portion of the aggregated first data may include or correspond to computing device 102, processor 208, filter 242, computing device 1710, processor 1720, one or more other devices configured to discard a portion of the aggregated first data, or combinations thereof.

[0150] The device also includes components for outputting navigation signals that interfere with map information. For example, the components for outputting navigation signals that interfere with map information may include or correspond to computing device 102, processor 208, computing device 1710, processor 1720, one or more other devices configured to output navigation signals that interfere with map information, or combinations thereof.

[0151] In some implementations, a non-transitory computer-readable medium stores instructions that, when executed by one or more processors, cause one or more processors to initiate, execute, or control operations to perform some or all of the functions described above. For example, these instructions can be executed to implement... Figures 1 to 17 One or more of the operations or methods. In some implementations, Figures 1 to 17 One or more of the operations or methods may be implemented by one or more processors (e.g., one or more central processing units (CPUs), one or more graphics processing units (GPUs), one or more digital signal processors (DSPs)) that execute the instructions, by dedicated hardware circuitry, or by any combination thereof.

[0152] Specific aspects of this disclosure are described in the following set of relevant examples: According to Example 1, a system includes one or more processors configured to: acquire navigation system quality information from multiple data sources, the navigation system quality information including first data acquired from a first data source among the multiple data sources and second data acquired from a second data source among the multiple data sources; aggregate the first data based on one or more parameters to generate aggregated first data; identify one or more areas associated with low-quality navigation signals based on the second data; discard portions of the aggregated first data based on the one or more areas to generate navigation signal interference map information; and output the navigation signal interference map information.

[0153] Example 2 includes the system of Example 1, wherein navigation signal interference map information indicates radio frequency interference areas associated with navigation signals of the Global Navigation Satellite System (GNSS).

[0154] Example 3 includes the system of Example 1 or Example 2, wherein the first data source among the plurality of data sources includes an Automatic Dependent Surveillance-Broadcast (ADS-B) data source, a Quick Access Recorder (QAR) data source, or a combination thereof; and the second data source among the plurality of data sources includes a Receiver Autonomous Integrity Monitoring (RAIM) data source, a System Wide Area Information Management (SWIM) data source, or a combination thereof.

[0155] Example 4 includes a system of any one of Examples 1 to 3, wherein a first data source among a plurality of data sources includes first data having a first data type, wherein a second data source among a plurality of data sources includes second data having a second data type, and wherein the second data type is different from the first data type.

[0156] Example 5 includes a system of any one of Examples 1 to 4, wherein the first data includes navigation integrity category (NIC) data, navigation accuracy category-location (NACp) data, or a combination thereof.

[0157] Example 6 includes a system of any one of Examples 1 to 5, wherein one or more parameters include an integrity threshold, region size, vertical granularity, time period, or a combination thereof.

[0158] Example 7 includes a system of any one of Examples 1 to 6, wherein one or more processors are further configured to: acquire first data from navigation system quality information, the first data source including an Automatic Dependent Surveillance-Broadcast (ADS-B) data source; filter ground data from the first data to generate filtered first data; and aggregate the filtered first data based on one or more parameters to generate aggregated first data.

[0159] Example 8 includes the system of Example 7, wherein one or more processors are further configured to identify an aircraft in flight that is at least partially associated with the filtered first data; obtain additional data associated with the aircraft in flight from an ADS-B data source; compare the additional data with an integrity threshold; generate a notification of a data integrity event based on the additional data meeting the integrity threshold; and transmit the notification.

[0160] Example 9 includes the system of Example 7 or Example 8, wherein one or more processors are further configured to acquire second data from navigation system quality information, the second data source including receiver autonomous integrity monitoring (RAIM) data source; and navigation signal interference map information including the remainder of the aggregated first data after a portion of the aggregated first data has been discarded.

[0161] Example 10 includes a system of any one of Examples 1 to 9, wherein an interference map is generated based on navigation signal interference map information.

[0162] Example 11 includes the system of Example 10, wherein the interference map comprises a plurality of hexagonal regions, and wherein at least one of the plurality of hexagonal regions indicates the amount of degradation of the navigation signal at each of one or more vertical height layers for the at least one hexagonal region.

[0163] Example 12 includes a system of any one of Examples 1 to 11, wherein one or more processors are further configured to: perform a pattern search on navigation signal interference map information to identify the source of interference; and generate interference source information indicating the location of the source of interference.

[0164] Example 13 includes the system of Example 12, wherein, in order to perform pattern search, one or more processors are further configured, for a geographic region, for each vertical height layer in the set of vertical height layers, to determine an interference value associated with the vertical height layer based on navigation signal interference map information; to perform a comparison between the set of interference values ​​of the set of vertical height layers and the interference pattern; and to identify the source of the interference based on the result of the comparison.

[0165] Example 14 includes a system of Example 12 or Example 13, wherein one or more processors are further configured to identify deceptive navigation signals based on navigation signal interference map information.

[0166] Example 15 includes the system of Example 14, wherein, in order to identify spoofed navigation signals, one or more processors are further configured to: identify areas having congestion events associated with radio frequency interference of one or more navigation signals based on navigation signal interference map information; determine a first signal quality value of a first navigation signal associated with the area prior to the congestion event; identify the end of the congestion event; determine a second signal quality value of a second navigation signal associated with the area after the end of the congestion event; perform a comparison based on the first signal quality value and the second signal quality value; and determine whether the second navigation signal is a spoofed navigation signal based on the result of the comparison.

[0167] According to Example 16, a method for identifying navigation signal interference includes: acquiring navigation system quality information from multiple data sources, the navigation system quality information including first data acquired from a first data source among the multiple data sources and second data acquired from a second data source among the multiple data sources; aggregating the first data based on one or more parameters to generate aggregated first data; identifying one or more areas associated with low-quality navigation signals based on the second data; discarding portions of the aggregated first data based on the one or more areas to generate navigation signal interference map information; and outputting the navigation signal interference map information.

[0168] Example 17 includes the method of Example 16, which further includes acquiring first data from navigation system quality information, the first data source including an Automatic Dependent Surveillance-Broadcast (ADS-B) data source.

[0169] Example 18 includes the method of Example 16 or Example 17, which further includes acquiring second data from navigation system quality information, the second data source including a receiver autonomous integrity monitoring (RAIM) data source.

[0170] Example 19 includes the method of any one of Examples 16 to 18, the method further including performing a pattern search on navigation signal interference map information to identify the source of interference.

[0171] Example 20 includes the method of Example 19, wherein performing a pattern search includes, for a geographic region: for each vertical height layer in the set of vertical height layers, determining an interference value associated with the vertical height layer based on navigation signal interference map information; and performing a comparison between the set of interference values ​​of the set of vertical height layers and the interference pattern.

[0172] Example 21 includes the method of Example 20, which further includes identifying the source of interference based on the results of the comparison.

[0173] Example 22 includes a method of any one of Examples 19 to 21, the method further including generating interference source information indicating the location of a source of interference.

[0174] Example 23 includes the method of Example 22, which further includes outputting interference source information together with navigation signal interference map information.

[0175] Example 24 includes the method of any one of Examples 16 to 23, the method further including: identifying deceptive navigation signals based on navigation signal interference map information.

[0176] Example 25 includes the method of Example 24, wherein identifying spoofed navigation signals includes identifying areas with congestion events associated with radio frequency interference of one or more navigation signals based on navigation signal interference map information.

[0177] Example 26 includes the method of Example 25, which further includes determining a first signal quality value of a first navigation signal associated with a region prior to a blocking event.

[0178] Example 27 includes the method of Example 26, which further includes: identifying the end of a blocking event; and determining a second signal quality value of a second navigation signal associated with the region after the end of the blocking event.

[0179] Example 28 includes the method of Example 27, which further includes performing a comparison based on a first signal quality value and a second signal quality value.

[0180] Example 29 includes the method of Example 28, which further includes: determining whether a second navigation signal is a deceptive navigation signal based on the result of a comparison.

[0181] Example 30 includes the method of any one of Examples 16 to 29, wherein navigation signal interference map information indicates radio frequency interference areas associated with navigation signals of a Global Navigation Satellite System (GNSS).

[0182] Example 31 includes a method of any one of Examples 16 to 30, wherein the first data source among a plurality of data sources includes an Automatic Dependent Surveillance Broadcast (ADS-B) data source, a Quick Access Recorder (QAR) data source, or a combination thereof.

[0183] Example 32 includes the method of any one of Examples 16 to 31, wherein the second data source among the plurality of data sources includes a receiver autonomous integrity monitoring (RAIM) data source, a system wide area information management (SWIM) data source, or a combination thereof.

[0184] Example 33 includes a method of any one of Examples 16 to 32, wherein a first data source among a plurality of data sources includes first data having a first data type, wherein a second data source among a plurality of data sources includes second data having a second data type, and wherein the second data type is different from the first data type.

[0185] Example 34 includes the method of any one of Examples 16 to 32, wherein the first data includes navigation integrity category (NIC) data, navigation accuracy category-position (NACp) data, or a combination thereof.

[0186] Example 35 includes a method of any of Examples 16 to 34, wherein one or more parameters include an integrity threshold, region size, vertical granularity, time period, or a combination thereof.

[0187] Example 36 includes a method of any one of Examples 16 to 35, the method further comprising: acquiring first data from navigation system quality information, the first data source including an Automatic Dependent Surveillance-Broadcast (ADS-B) data source; filtering ground data from the first data to generate filtered first data; and aggregating the filtered first data based on one or more parameters to generate aggregated first data.

[0188] Example 37 includes the method of Example 36, which further includes identifying an aircraft in flight that is at least partially associated with the filtered first data.

[0189] Example 38 includes the method of Example 37, which further includes retrieving additional data associated with the aircraft in flight from an ADS-B data source.

[0190] Example 39 includes the method of Example 38, which further includes comparing additional data with an integrity threshold.

[0191] Example 40 includes the method of Example 39, which further includes generating a notification of a data integrity event based on additional data meeting an integrity threshold.

[0192] Example 41 includes the method of Example 40, which further includes transmitting notification.

[0193] Example 42 includes a method of any one of Examples 16 to 41, the method further including acquiring second data from navigation system quality information, the second data source including a receiver autonomous integrity monitoring (RAIM) data source.

[0194] Example 43 includes a method of any one of Examples 16 to 42, wherein the navigation signal interference map information includes the remainder of the aggregated first data after a portion of the aggregated first data has been discarded.

[0195] Example 44 includes the method of any one of Examples 16 to 43, the method further including generating an interference map based on navigation signal interference map information.

[0196] Example 45 includes the method of Example 44, wherein the output navigation signal interference map information includes the output interference map.

[0197] Example 46 includes the method of Example 44 or Example 45, wherein the interference map comprises multiple regions; and at least one region among the multiple regions indicates the amount of degradation of the navigation signal at each of one or more vertical height layers for the at least one region.

[0198] Example 47 includes the method of Example 46, wherein the multiple regions comprise multiple hexagonal regions.

[0199] According to Example 48, an apparatus includes: a memory configured to store instructions; and a processor configured to execute instructions to perform the method of any one of Examples 16 to 47.

[0200] According to Example 49, a non-transitory computer-readable medium storage instruction is provided that, when executed by a processor, causes the processor to perform any of the methods in Examples 16 to 47.

[0201] According to Example 50, an apparatus includes a component for performing a method as described in any of Examples 16 to 47.

[0202] According to Example 51, a non-transitory computer-readable medium storing instructions executable by one or more processors to perform operations including: acquiring navigation system quality information from a plurality of data sources, the navigation system quality information including first data acquired from a first data source among the plurality of data sources and second data acquired from a second data source among the plurality of data sources; aggregating the first data based on one or more parameters to generate aggregated first data; identifying one or more areas associated with low-quality navigation signals based on the second data; discarding portions of the aggregated first data based on the one or more areas to generate navigation signal interference map information; and outputting the navigation signal interference map information.

[0203] Example 52 includes the non-transitory computer-readable medium of Example 51, wherein navigation signal interference map information indicates areas of deterioration of navigation signals of a Global Navigation Satellite System (GNSS).

[0204] The illustrations of the examples described herein are intended to provide a general understanding of the structure of various implementations. These illustrations are not intended to serve as a complete description of all elements and features of devices and systems utilizing the structures or methods described herein. Many other implementations will be apparent to those skilled in the art upon review of this disclosure. Other implementations can be utilized and derived from this disclosure, allowing structural and logical substitutions and changes to be made without departing from the scope of this disclosure. For example, method operations may be performed in a different order than those shown in the figures, or one or more method operations may be omitted. Therefore, this disclosure and the accompanying drawings are to be considered illustrative rather than restrictive.

[0205] Furthermore, while specific examples have been shown and described herein, it should be understood that any subsequent arrangements designed to achieve the same or similar results may replace the specific implementations shown. This disclosure is intended to cover any and all subsequent adaptations or variations of the various implementations. After reviewing this specification, combinations of the above implementations, as well as other implementations not specifically described herein, will be apparent to those skilled in the art.

[0206] The abstract of this disclosure is provided to be understood not to be construed as limiting the scope or meaning of the claims. Furthermore, in the foregoing detailed embodiments, various features may be combined together or described in a single implementation for the purpose of simplification. The foregoing examples are illustrative but not limiting of this disclosure. It should also be understood that many modifications and variations are possible based on the principles of this disclosure. As reflected in the following claims, the claimed subject matter may involve fewer features than all features of any of the disclosed examples. Therefore, the scope of this disclosure is defined by the appended claims and their equivalents.

Claims

1. A system for identifying navigation signal interference, comprising: One or more processors are configured as follows: Navigation system quality information is obtained from multiple data sources, including first data obtained from a first data source among the multiple data sources and second data obtained from a second data source among the multiple data sources; The first data is aggregated based on one or more parameters to generate aggregated first data; Based on the second data, one or more areas associated with low-quality navigation signals are identified; Based on the one or more regions, a portion of the aggregated first data is discarded to generate navigation signal interference map information; as well as The navigation signal interference map information is output.

2. The system of claim 1, wherein, The navigation signal interference map information indicates radio frequency interference areas associated with navigation signals of the Global Navigation Satellite System.

3. The system according to claim 1 or 2, wherein: The first data source among the plurality of data sources includes an Autocorrelation Monitoring Broadcast data source, a Fast Access Recorder data source, or a combination of both; and The second data source among the plurality of data sources includes a receiver autonomous integrity monitoring data source, a system wide-area information management data source, or a combination of the two.

4. The system according to claim 1 or 2, wherein: The first data source among the plurality of data sources includes the first data having a first data type; The second data source among the plurality of data sources includes the second data having a second data type; and The second data type is different from the first data type.

5. The system of claim 1 or 2, wherein, The first data includes navigation integrity category data, navigation accuracy category-location data, or a combination of both.

6. The system of claim 1 or 2, wherein, The one or more parameters include an integrity threshold, region size, vertical granularity, time period, or a combination thereof.

7. The system of claim 1 or 2, wherein, The one or more processors are further configured to: The first data is obtained from the navigation system quality information, wherein the first data source includes an automatic correlation monitoring broadcast data source; Ground data is filtered out from the first data to generate filtered first data; as well as The filtered first data is aggregated based on one or more parameters to generate the aggregated first data.

8. The system of claim 7, wherein, The one or more processors are further configured to: Identify aircraft in flight that are associated with at least a portion of the filtered first data; Additional data associated with the aircraft in flight is obtained from the Automatic Dependent Surveillance Broadcast (ADS) data source; The additional data is compared with the integrity threshold; A notification of a data integrity event is generated based on the additional data satisfying the integrity threshold; as well as The notification is transmitted.

9. The system according to claim 7, wherein: The one or more processors are further configured to acquire the second data from the navigation system quality information, the second data source including a receiver autonomous integrity monitoring data source; as well as The navigation signal interference map information includes the remaining portion of the aggregated first data after the portion in the aggregated first data has been discarded.

10. The system of claim 1 or 2, wherein, The interference map is generated based on the navigation signal interference map information.

11. The system according to claim 10, wherein: The interference map includes multiple hexagonal regions; and At least one of the plurality of hexagonal regions indicates the amount of degradation of the navigation signal at each of one or more vertical height layers of the at least one hexagonal region.

12. The system of claim 1 or 2, wherein, The one or more processors are further configured to: Perform a pattern search on the navigation signal interference map information to identify the source of interference; and Generate interference source information indicating the location of the source of interference.

13. The system according to claim 12, wherein, In order to perform the pattern search, the one or more processors are further configured to: For geographical regions: For each vertical height layer in the set of vertical height layers, the interference value associated with the vertical height layer is determined based on the navigation signal interference map information; Perform a comparison between the set of interference values ​​and the interference pattern of the vertical height layer set; as well as The source of the interference is identified based on the results of the comparison.

14. A method for identifying navigation signal interference, the method comprising: Navigation system quality information is obtained from multiple data sources, including first data obtained from a first data source among the multiple data sources and second data obtained from a second data source among the multiple data sources; The first data is aggregated based on one or more parameters to generate aggregated first data; Based on the second data, one or more areas associated with low-quality navigation signals are identified; Based on the one or more regions, a portion of the aggregated first data is discarded to generate navigation signal interference map information; as well as The navigation signal interference map information is output.

15. A non-transitory computer-readable medium storing instructions executable by one or more processors to perform operations including: Navigation system quality information is obtained from multiple data sources, including first data obtained from a first data source among the multiple data sources and second data obtained from a second data source among the multiple data sources; The first data is aggregated based on one or more parameters to generate aggregated first data; Based on the second data, one or more areas associated with low-quality navigation signals are identified; Based on the one or more regions, a portion of the aggregated first data is discarded to generate navigation signal interference map information; as well as The navigation signal interference map information is output.

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