Detection of terminals on board aircraft in flight, such as drones

Abstract

The present invention relates to detecting a terminal likely to be on board an aircraft during flight, the terminal being connected to a mobile network comprising a plurality of cells covered by respective antennas. It relates, in particular, to: - obtaining (S1) current data of the connection of the terminal to successive cells of the mobile network, the current data comprising at least data of the cells involved in the connection of the terminal to the mobile network; - comparing (S2) the current data to reference data of the connection of terminals on land and involving at least one cell of the current data; and - according to the comparison, determining (S3) whether the terminal is on board an aircraft in flight.

Description

Description Title: Detection of terminals embedded in flying devices, such as drones Technical field

[0001] This disclosure relates to the field of detection of in-flight devices such as drones. Prior art

[0002] Typically, drones carry terminals, referred to hereinafter as "air terminals". An air terminal can be used to transmit a stream of video data (acquired, for example, by a camera embedded in the drone) via a mobile network to which the air terminal is connected. It can also be used to directly pilot the drone by transmitting instructions received from another terminal available to a user, the pilot of the drone. Thus, in these different situations, an air terminal is connected to a network, for example a cellular mobile network.

[0003] Detecting a drone, in particular to check whether it is authorized to fly, often requires a complex installation of radiofrequency sensors, and / or radars, and / or means to implement detection of radiofrequency beacons broadcast by the drone, this broadcast being called "ADS-B" (for "Automatic Dependent Surveillance-Broadcast").

[0004] Drone operators can use consumer mobile devices connected to a mobile network to control their drones. However, a drone must have a flight plan to be authorized to fly in an area. In addition, the drone operator must be issued a valid flight license. Failure to do so may result in a fine and license revocation. However, to do this, the drone must be detected and the drone operator's identity determined.

[0005] Furthermore, a malicious drone user may have disabled the aforementioned beacons for ADS-B broadcasting.

[0006] One approach may be to rely on a mobile cellular network to detect the presence of an aerial terminal. However, cellular networks, designed for transmissions for ground terminals, are not optimized for aerial connectivity. An aerial terminal (hereinafter referred to as a "drone") may attach to antenna side lobes close to the drone or to main lobes distant from the drone. As a result, mobile modems used to connect drones are not necessarily identified as "drone" type, which complicates the detection of genuine drones.

[0007] In addition, the identity of a drone owner, used by a pilot who may be very distant ("Beyond Visible Line Of Sight" for example) may not be known. Similarly, the identity of a pilot who uses a drone in visual flight and who broadcasts live, via a mobile network on the Internet for example, image data captured by the drone, may not be known. Summary

[0008] This disclosure improves the situation.

[0009] To this end, it proposes a method for detecting a terminal likely to be on board a device during flight (typically a drone, but possibly a microlight or others). The terminal is connected to a mobile network comprising a plurality of cells covered by respective antennas (typically base stations in a conventional cellular network, but also possibly wifi hotspots (for example wifi relay gateways), or others.

[0010] In particular, this process includes: - obtain current connection data from the terminal to successive cells of the mobile network, said current data comprising at least data from the cells involved in the connection of the terminal to the mobile network, - comparing the current data with reference data for connection of terrestrial terminals and involving at least one cell of said current data, and - based on said comparison, determine whether the terminal is on board an aircraft in flight.

[0011] These reference data may correspond to connection data of terrestrial terminals or to theoretical connection values ​​specific to a terrestrial terminal in conventional movement on the ground (in accordance with a chart for example). Indeed, as detailed further on with reference to figures 1 and 2, the connectivity to the cellular network of a terminal in flight is different from the conventional connectivity of a terminal on the ground. For example, the attachment to the base stations in connection transfer situations (or "handovers") has been observed to be different for a terminal in flight and for a terrestrial terminal in movement.

[0012] Thus, it is proposed here to analyze the data sent by mobile networks to detect in particular undeclared drones and carrying terminals using cellular connectivity to broadcast media streams (in particular image, video and / or audio data) and / or exploit piloting data.

[0013] In an embodiment exploiting the aforementioned handover situations, the method may comprise: - obtaining current data of connection transfers from the terminal from one cell to another cell, said current data comprising at least data from the cells involved in said transfers, and - compare the current data with reference data of connection transfers of terrestrial terminals and involving at least one cell of said current data.

[0014] In particular, if two cells of the network are deemed to be adjacent because they provide a connection transfer from a terrestrial terminal of one of the two cells to the other, then said comparison of the data may include the detection in the current data of a connection transfer between two cells which are not deemed to be adjacent.

[0015] It will thus be understood that the detection of a handover between two cells which never occurs for a terrestrial terminal can characterize the presence of a terminal in flight.

[0016] In such an embodiment, if the current data also includes respective times of these transfers, then the current data can be analyzed to detect whether the terminal connects several times successively to two cells which are not deemed to be adjacent and in a time period less than a threshold (this situation being called hereinafter “ping-pong” between two cells).

[0017] Alternatively or additionally, in this embodiment, if the current data also includes respective times of the transfers, the current data can be analyzed to detect whether a trajectory carried out by the terminal passes through successive cells which are not deemed to be adjacent.

[0018] Thus, the terminal's trajectory in flight can be determined by handover situations.

[0019] However, alternatively or as a variation of handover detections, the current data may include in particular data on the strength of the radiofrequency link connecting the terminal to each of the aforementioned successive cells of the mobile network, to estimate successive positions of the terminal (possibly but optionally in 3D coordinates).

[0020] Here, "link strength" is understood to mean, generically, any physical parameter characterizing this link (intensity or power, or even signal-to-noise ratio, of the radiofrequency link).

[0021] Thus, by determining the successive positions occupied by the terminal, it is possible to determine its trajectory.

[0022] In the embodiment using handover data or the embodiment using radiofrequency link strength, the current data may then further comprise respective times of connection to said successive cells, and the aforementioned current data may be analyzed to determine a trajectory of the terminal and detect, for example, whether a speed of movement of the terminal on this trajectory is greater than a threshold.

[0023] For the study of the aforementioned radio frequency link strengths, a mobile network entity can perform a GMLC type function for “Gateway Mobile Location Center” (the references of which are detailed later).

[0024] Alternatively or in addition, the study of terminal connections to successive cells, particularly in handover situations, can be implemented by a network entity executing a NWDAF type function for “Network Data Analytic Function” (detailed further below).

[0025] The network entity capable of implementing such functions is typically the core network (or “Core Network” hereinafter), or an entity dedicated to this air terminal detection application and connected to the Core Network, as presented further on with reference to figure 4.

[0026] In one embodiment, the aforementioned current data may typically include at least one terminal identifier and at least one terminal user identifier.

[0027] Indeed, as soon as the data managed by the network (core network) is accessible, the terminal identifiers and the identity of its user are also accessible.

[0028] In such an implementation, it is then possible to: - consult a platform (for example of the “USS” type, as detailed below) of pilot recordings and / or flight plans of devices carrying terminals, and - determine at least whether the terminal user ID is declared among said platform records.

[0029] It is also possible to: - consult this platform to determine whether at least the terminal's trajectory complies with a flight plan declared for said terminal, and / or - check whether the detected trajectory includes at least one sensitive site, associated with restrictive overflight conditions (which had not been authorized, for example, by an administration managing this sensitive site).

[0030] Other examples of control can be carried out, in particular to control the time of overflight of a site during an authorized or unauthorized period (for example due to a one-off event). The detailed description below details these various examples.

[0031] The present disclosure also relates to a computer program comprising instructions for implementing the method according to the present invention, when executed by a processing circuit. According to another aspect, there is provided a non-transitory recording medium, readable by a processing circuit, on which such a program is recorded.

[0032] The present disclosure also relates to a device comprising a processing circuit for implementing the above method.

[0033] This device may typically include a connection to a core of the mobile network, on the one hand, and to a platform of the aforementioned type, for pilot recordings and / or flight plans of devices carrying terminals, on the other hand.

[0034] Such a device can then be arranged to perform at least one function among at least one NWDAF - “Network Data Analytic Function” type function and one GMLC - “Gateway Mobile Location Center” type function.

[0035] This type of function interfaces advantageously with a NEF type function (for “Network Exposition Function”) implemented by a USS type gateway.

[0036] Thus, the device can be arranged to execute said NWDAF or GMLC type function, in connection with the USS platform, via such an NEF type function. Brief description of the drawings

[0037] Other features, details and advantages will become apparent upon reading the detailed description below, and upon analyzing the attached drawings, in which: Fig. 1

[0038] [Fig. 1] illustrates an example of connecting an on-board terminal to an aircraft in flight. Fig. 2

[0039] [Fig. 2] illustrates an example of connections from on-board terminal B to successive antennas of the cellular mobile network, to be compared with the usual connections from terminal A on the ground. Fig. 3

[0040] [Fig. 3] illustrates an example of a method within the meaning of the present disclosure, according to one embodiment. Fig. 4

[0041] [Fig. 4] illustrates an example of a system including a SER device within the meaning of the present disclosure, according to one embodiment. Description of the embodiments

[0042] Hereinafter, the term "normal terminal" refers to a conventional, terrestrial terminal. It is denoted UE_A (for "User Equipment" (UE) terrestrial), and it is assumed to be connected to the antenna of a base station in direct visibility of the terminal on the ground. A conventional terminal is, for example, a smartphone-type terminal or an loT (Internet of Things) type terminal.

[0043] In contrast, the term "abnormal terminal" is hereinafter understood to mean a terminal on board an "aircraft". It is denoted UE_B. Being on board a craft at altitude, the UE_B terminal does not necessarily attach to the antenna usually closest to a ground terminal but can connect to much more distant cells because the secondary lobe of the closest antenna emits less for the abnormal terminal than a distant antenna that the normal terminal could not have in sight.

[0044] This situation is illustrated in the example of Figure 1 where a terminal in flight UE_B is covered by the main lobe LP2 (in dotted lines) of an antenna here of a more distant base station BS2, whereas on the ground it would have been covered by the main lobe LP1 (in solid line) of the nearest station BS1, as is the situation of a normal terminal UE_A. The secondary lobe(s) LS1 of the nearest base station BS1 are not sufficient to cover the terminal UE_B in flight, so that it naturally attaches itself to the station BS2, even if it is more distant than the station BS1 for the terminal UE_B.

[0045] An embodiment of the present disclosure proposes to take advantage of this observation to detect on-board terminals, in particular by drones.

[0046] The analysis of connections to the base stations of the cellular network (or more generally "antennas" hereinafter) makes it possible to study in particular the trajectories of the terminals (as well as the so-called “ping-pong” effects and the speeds of terminal movement, as we will see later).

[0047] In the present embodiment, it is proposed to detect trajectories which do not correspond to a normal trajectory for a normal terrestrial terminal UE_A.

[0048] For example, with reference to Figure 2, the list of antennas used to connect a terminal is collected during mobility procedures, also called transfers, between antennas (called "handover" to move from a cell covered by one station to a cell covered by another station). This list of antennas defines a trajectory as illustrated in Figure 2, with in particular: - for terminal UE_A, trajectory A defined by: 3,t1; 2,t2; 3,t3; 2,t4; 4,t5; 5,t6, and - for terminal UE_B, trajectory B defined by: 10, t1; 3t2; 10t3; 3,t4; 1,t5; 8,t6; 10, t7.

[0049] It then appears that trajectory B shows the use of cells which are not necessarily "adjacent" (in the sense of their usual use for terminals “normal” on the ground) during handovers, which then makes it possible to detect an anomaly characterizing the re-embarkation of terminal UE_B in a flying machine.

[0050] Thus, the analysis of mobile network control data between each antenna used and the mobile core network via probes can, in certain circumstances, allow the movement of abnormal terminals to be analyzed.

[0051] Mobile network monitoring (or "CDR") provides metering data needed for billing purposes in particular. These CDRs can be generated by analyzing signaling, for example, on S1 interfaces to the core network between the network's base stations or "eNodeBs" and the S-GW ("Serving Gateway") or the MME (Mobility Management Entity) at the core of the mobile network, with the help of probes.

[0052] The signaling between the MME manager and the eNodeBs contains the information of cell identifier, terminal identity IMEI, session identity @IP, identity of the use (USIM / ISIM). Thus, from the set of cells used to connect a terminal, it is possible to reconstruct the trajectory of the vehicle that carries the terminal. The signaling data can thus provide information such as the identity of the terminal, the name of the operator, the connection date, the identifiers of the cells (current and previous) that were used to connect the terminal, the type of service, etc.

[0053] The analysis of signaling data (control plane, transfer plane) can be carried out in a function of the NWDAF type (for "Network Data Analytic Function" according to the TS 23.288 standard) or in a management plane with the MDAF function (for "management data analytic function" according to the TS 28.533 standard, of the 5G network). The 5G MDAF, NWDAF functions can also collect desired data from the control plane and transfer plane functions of the 5G network without using probes. The inventory of antenna locations at given times provides information on the distances traveled between cells, and the area geographic area that was flown over. Each antenna can be identified with longitude and latitude information.

[0054] It is then proposed to enrich a function of this type NWDAF to analyze the trajectories of the terminals, from a list of antennas to which the terminal has been connected with the associated connection dates, this list being taken from the analysis of the signaling data of the mobile network.

[0055] In this context, a service offering to detect abnormal terminal behaviors (“abnormal behavior”) can be created in order to detect abnormal mobile trajectories. This detection can be notified to an application having: - subscribed to the “abnormal behavior” service, and - requested statistics or trajectory predictions over a given area and duration, the list of terminal types or the identities of terminals with abnormal behavior (@IP, IMEI, IMSI, name-first name, etc.).

[0056] Such a service can offer tools such as: - “Unexpected UE location” (detection of an unexpected position of the terminal), - “Ping-ponging across neighboring cells” (exchanges between cells supposed to be neighbors, particularly for a handover), - “Unexpected wakeup” (unexpected appearance of a terminal at a given cell), - “Unexpected radio link failures” (unexpected interruption of a radio link with a cell).

[0057] The table below gives (left column) a more exhaustive list of these possible tools and (right column) the description of the pre-existing, standardized parameters allowing these tools to be implemented.

[0058] The implementation of a function of type NWDAF allows access to these parameters (right column), and from there, by an enrichment within the meaning of the present disclosure of this function, to access incident detections (left column) characterizing the presence of a terminal embedded in a flying device such as a drone.

[0059] Alternatively or in addition (to reinforce trajectory determination), a GMLC type location function (for "Gateway Mobile Location Center" - as presented in particular in the 3GPP document TS 29.515 V18.1.0 (2023-03)) can also be used. Such a function determines the current position of a terminal more precisely by relying on the power measurement of the radiofrequency link with the neighboring antennas. The determination of the terminal's position is then carried out by trilateration.

[0060] More generally, for a function such as MDAF, NWDAF or GMLC, data collection can be carried out on existing mobile networks for all terminals, usually to generate billing tickets. This collection is implemented for regulatory needs (emergency service, or others). With the 5G standard, collection can be carried out on demand and for a given area for any terminal.

[0061] This data can then be analyzed to determine the identity of the terminal, as well as the identity of its user. It can typically be verified whether the user has declared himself to be piloting a drone to an air surveillance authority and, if applicable, whether he has declared to this authority a flight plan consistent with the trajectory observed for this drone by implementing the aforementioned analysis function (for example NWDAF). Furthermore, in addition or as a variant, it can also be verified whether the type of flight plan itself is consistent with a prior declaration by its pilot.For example, a high-altitude flight (whatever the precise trajectory of the drone), in particular for surveillance, police and / or military applications, can be detected by an analysis function (for example NWDAF) if a rate of abnormal behavior (for example a rate of attachments to unusual cells, in particular in handover situations) is greater than a first threshold THR1. On the other hand, a flight at a lower altitude, in particular for goods delivery applications or others, can be characterized by a rate of abnormal behavior between the first threshold THR1 and a second threshold THR2, lower than the threshold THR1, and below which the terminal could be qualified as terrestrial, for example.

[0062] In one embodiment, this analysis function (for example NWDAF and / or GMLC) can interface with a USS (for “UAS Service Supplier”, “UAS” for “UAV system” and “UAV” for “Unmanned aerial vehicle”) type platform. This is a service provider for drone systems (such systems comprising drones and the infrastructure to control them). Such a USS platform manages the use of airspace by providing services to the operator and / or pilot of a drone to meet the operational requirements of a drone manager called “UTM” (for “Uncrewed Aerial System Traffic Management”).

[0063] The interfacing of the NWDAF type analysis function and the USS then makes it possible to check whether the conditions of subscription to third-party services are verified, and otherwise to alert an authority wishing to detect drones in a given space-time or to detect and identify drones which have not declared themselves or which are flying over an unauthorized zone, or others.

[0064] Reference is now made to Figure 3 illustrating a possible form of a method within the meaning of the present disclosure.

[0065] It is proposed to analyze mobile network control plane data for at least one of the following actions: - detect drones connected to the mobile network, - identify that a terminal is potentially a UAV drone (for “Unmanned Aerial Vehicle”, therefore an aerial vehicle without a human on board, typically a drone), - detect non-compliant behavior of the drone in relation to flight parameters declared for this drone, - find the identity of the drone operator (for example the pilot) who controls a device identified as being of the “UAV” type via a service subscription profile associated with this device, - issue notifications to air security services that a drone not using an air service managed by a USS has been detected, and possibly that this drone arrives or has been detected in an unauthorized flight zone or one requiring special subscription.

[0066] For example, a subscriber to such a drone detection service may be an entity linked to the USS and imposing surveillance of a given airspace (for example a sensitive site, such as a military or nuclear site, a prison, or others) or an entity in charge of certain air corridors (in particular for civil aviation).

[0067] Thus, the pilot of a drone can declare himself at a subscription step S0, via an application function, to an air traffic control service managed by one or more USS entities in conjunction with one or more authorities such as a general directorate of civil aviation of a given country, one or more actors in charge of the security of sensitive sites (nuclear power plants, prisons, etc.). The USS platform (or ultimately the pilot via the USS) can further declare the association between the drone, the identity of the pilot and the authorized flight zones to a mobile network operator to typically detect these drones via a mobile network service exposure function (called NEF for "network exposure function"). During a flight of such a declared drone, the aforementioned NWDAF function can then interface with the USS, more precisely via this NEF-type function, in order to identify, for example, whether the drone is crossing an unauthorized zone.

[0068] More generally, actors requesting the USS may wish to: - detect undeclared drones, by subscribing to a service with a mobile network operator, and - identify drones that may have been declared but are flying over sensitive sites (for example, by default around an airport if the only one of these actors is a civil aviation authority).

[0069] The USS, via the NEF function, then requests a NWDAF type function (for “Network Data Analytic Function”) for the drone detection service, which can then operate: - in a given 3D geographic area, and possibly - for a given duration (for an event such as a festival for example).

[0070] Referring then to Figure 3, after this initial SO step of service declaration and subscription, during a step S1 of current implementation of the service, the NEF function requests data from a NWDAF type function to obtain abnormal terminal behaviors, i.e. abnormal for terrestrial terminals and likely to correspond then to behaviors of drones (or terminal in a drone) in a current geographical area. This operation makes it possible to obtain a list of antennas requested or directly the geographical coordinates of each terminal with erratic behavior during a given time interval, as detailed below.

[0071] A network entity such as the Core Network can then implement the NWDAF function in the next general step S2 by collecting mobile network data and analyzing abnormal terminal behaviors. At least one of the following test operations can be implemented for this purpose.

[0072] A first possible operation S21 proposes to analyze the probability of so-called "ping-pong" effects when the terminals attach themselves several times to neighboring antennas during a short period of time (typically less than 5 seconds). This situation can be detected because the terminal connects to antennas having the same identifiers several times in a short period of time. For example, the ping-pong effect sequence for a normal terrestrial terminal UE_A (3:2:3:2) which is quasi-static can make it possible to identify abnormal behavior with the following sequence of the in-flight terminal UE_B (10:3:10:3). Indeed, the probability of finding ping-pong effects with 3:10 antennas is low, or even zero, for a terrestrial terminal. Moreover, the distance between antenna 3 and 10 is higher than the ping-pong distances for terrestrial terminals in this area.A terrestrial terminal cannot, in principle, attach to the 10:3 antennas in a short period of time (typically less than 5 seconds). The distance between the antennas is analyzed with the coordinates of the different antennas of the mobile network.

[0073] A second possible operation S22 proposes to compare a current trajectory with the probabilities of trajectories analyzed for terrestrial terminals (a normal trajectory being 3:2:3:2:4:5 according to the example in Figure 2). A terminal that obtains a trajectory outside the probabilities specific to terrestrial terminals can enter the category of “probably aerial terminals”. This probability of abnormal trajectory is identified in operation S22 as abnormal because the probability that a terminal passes from antenna 10 to antenna 3 is very low and, similarly, between antennas 8 and 10 (10:3:10:3:1:8:10). The probability that a trajectory is probably aerial can be validated / confirmed by analyzing the distance between the antennas. For example, if the distance between antennas 8 and 10 is greater than 20 km, this sequence of antennas is improbable for a terrestrial terminal and its use characterizes the trajectory of a drone.

[0074] A third possible operation S23 proposes to analyze the speed of terminals by estimating the distances traveled between non-adjacent cells (10:3:1:8:10). Such an operation also makes it possible to improve the predictions of detection of drones which do not, in principle, have the same movement speeds as terrestrial terminals (pedestrians, cyclists, automobiles often on roads with high traffic density, etc.), the speed of movement of a drone is generally greater.

[0075] Thus, the implementation of the NWDAF function makes it possible to obtain, after the implementation of the general step S2, a list of terminals with the associated probabilities that they are embarked in respective drones, by observations of abnormal ping-pong, and / or abnormal trajectory (of handover), and / or abnormal speed (for a terrestrial terminal). This list can be provided to the air traffic controller with the identifiers of the terminals having such abnormal behavior, in association with the information on the identity of the respective owners of the subscriptions to the network (in particular in the case where this air traffic control authority already has such information on the users, such as for example an air traffic control authority police, civil security, or others).

[0076] So, these three types of analysis can be added to the usual analysis services that the NWDAF function is already responsible for: - the detection of an abnormal ping-pong S21 (repetitive mobility towards identical antennas in a very short period of time, for example below a threshold of five seconds or less), - the detection of an abnormal trajectory S22 (series of antennas ensuring successive handovers with a very low probability compared to a set of trajectories of terminals considered to be terrestrial), - the detection of an abnormal speed of the S23 terminal (characterized by a distance traveled, according to the handovers recorded by the antennas, greater than a threshold (for example 20 km) for a latency lower than a threshold, for example a few minutes).

[0077] It should be noted that these different detections can be carried out for a drone that is piloted via a mobile network implementing this NWDAF function, but also for a drone that uses a mobile connection to transmit a media stream (such as a video stream captured by a drone camera) and which is not necessarily controlled by a pilot connected via mobile access.

[0078] Following this step S2, in the following general step S3, these analyses make it possible to find the identities of each terminal (the determined type (hardware, software provider, etc.), the unique terminal identifier (for example IMEI type), the subscription identifier (for example IMSI type), the identity of the SIM card allowing connection to the cellular network), which makes it possible to find the identities of the pilot of the drone possibly in violation (mobile service subscription profile identity (ISIM), connection identity used such as the IP address on the network, billing identity associated with the pilot (surname, first name, address, postal code, etc.)).

[0079] It is then possible in step S4 to typically determine whether or not the pilot has subscribed to a service of the aforementioned type in accordance with civil aviation, by searching for the pilot's identity in a database generated for example in the declaration step S0. If he is not identified as having subscribed to this service in step S0, an alert can be generated in step S6.

[0080] It is then possible at step S5 to determine whether the drone, even declared at step S0, is authorized to fly over a current area. Thus, at this step S5, the terminal is identified as 72 potentially embarked in a drone and if it is predicted that this drone is approaching a restricted flight zone by analyzing the data collected from the antennas of the mobile network, an alert can be generated in step S6.

[0081] In step S5, for a more general and automated verification of flight zones with respect to prior declarations of authorized flight plans, it can be provided that the NWDAF function verifies in the message exchanges sent by a network entity in charge of UDM management (for "Unified data management") that an identified drone does indeed have flight authorization. Such an implementation typically makes it possible to retrieve a flight identifier of the drone and an authorization level of zones that can be flown over or not.

[0082] In addition, an exchange of messages between a USS entity and the NWDAF function can provide information on the drone's expected trajectory, in order to compare it with an expected trajectory (called "Expected UE Moving Trajectory") for confirmation. Thus, the NWDAF function can inform the USS entity of the exact location of the drone, or notify a third party that has subscribed to a surveillance service for one of its sensitive sites that a drone has been detected there in order to monitor its own airspace.

[0083] At the end of this S6 alert step, if the USS consultation confirms that the terminal (detected as aerial) is not authorized to be carried on a drone, the air traffic controller can decide to block the terminal by asking the mobile network to suspend the drone's connectivity. The air traffic controller can declare the terminal as a drone to block any attachment to the mobile network if it does not have an expected flight plan by adding the terminal to a list of blacklisted terminals with mobile operators. The air traffic controller can order the drone operator to provide the identity of the user who used mobile connectivity in a prohibited area, or any other action aimed at ensuring the security of an airspace.

[0084] Figure 4 illustrates a possible embodiment of a system for implementing the method described above. The mobile network RESM is controlled by an entity such as the core network CN, to which a device SER can be connected for implementing the method presented above with reference to Figure 3. This device can for example be produced in the form of a server SER comprising a processing circuit equipped with: - a COM communication interface (notably with the CN network core), - a PROC processor, and - a memory MEM storing instruction data of a computer program for implementing the above method, when these instructions are read and executed by the processor PROC.

[0085] The server's COM communication interface allows a connection to be established (for example via a wide area network RE), with a USS platform of the aforementioned type. Typically, the SER server, connecting to the CN core network, can implement NWDAF or GMLC type functions, which can address a NEF type function of the USS platform, as described previously.

[0086] Of course, the USS platform can also receive data from authorities such as civil aviation (AC), or even military authorities (MIL) or industrialists (IND) wishing to restrict overflight of sensitive areas (typically military or industrial sites).

[0087] This disclosure is not limited to the embodiments described above as examples: it extends to other variants.

[0088] Thus, for example, the aforementioned SER server may be an integral part of the CN core network, which may directly execute the NWDAF and / or GMLC functions in connection with the NEF function of the USS. Thus, the device within the meaning of the present disclosure may directly be the CN core network.

[0089] Furthermore, the interconnection of the SER server (or directly of the CN core network) to a USS type platform is an example of an implementation. In addition or as a variant, manufacturers or military authorities (or others) can share a SER interconnection server with the CN core network to directly receive alerts from network monitoring by implementing the NWDAF function in particular, typically to determine whether a drone carrying a terminal with abnormal behavior has flown over a sensitive area (military or industrial).

[0090] Furthermore, steps S21 to S23 above are illustrated as examples of what the execution of a function of type NWDAF allows: on the one hand, another type of function such as GMLC can be implemented, as an alternative or in addition to the function of type NWDAF, and, on the other hand, the implementation of only one or two of these steps S21 to S23 is possible.

[0091] Furthermore, the embedded UE object was previously referred to as "terminal". In 3GPP terminology, the terminal can also be referred to as "user agent" (and always referred to as UE).

[0092] Furthermore, in step S0 presented above, during subscription, the user may be asked whether the drone is associated with only one user agent (or terminal) or with a SIM / IMEI pair associated with a drone, in this flight space, as described above. Otherwise (if the user carries several terminals or user agents), the user provides the identifiers of the other user agents (SIM, IMEI, PEI) in order to complete the analysis on trajectories of different user agents carried together in the same device.

Claims

74 Claims

1. Method implemented by an entity of a mobile network comprising a plurality of cells covered by respective antennas of the network, for detecting a terminal likely to be on board an aircraft during flight, the terminal being connected to the mobile network, the method comprising: - obtaining (S1) current data of connection transfers from the terminal of one cell to another cell of the mobile network, said current data comprising at least respective instants of said transfers, and data of the cells involved in said transfers, - comparing (S2) the current data with reference data for connection of terrestrial terminal transfers and involving at least one cell of said current data, and - determine (S3) that the terminal is on board a device in flight if the current data does not respect the reference data.

2. Method according to claim 1, in which, two cells of the network being deemed to be adjacent if they ensure a connection transfer from a terrestrial terminal of one of the two cells to the other, said comparison of the data comprises the detection (S21; S22) in the current data of a connection transfer between two cells which are not deemed to be adjacent.

3. Method according to claim 2, in which the current data further comprises respective instants of said transfers, and said current data is analyzed to detect (S21) whether the terminal connects several times successively to two cells which are not deemed to be adjacent and in a time period less than a threshold.

4. Method according to one of claims 2 and 3, in which the current data further comprises respective instants of said transfers, and said current data is analyzed to detect (S22) whether a trajectory carried out by the terminal passes through successive cells which are not deemed to be adjacent.

5. Method according to one of the preceding claims, in which the current data comprises data on the strength of the radiofrequency link connecting the terminal with each of said successive cells of the mobile network, for estimating successive positions of the terminal.

6. Method according to one of claims 1 to 5, in which the current data further comprises respective times of connection to said successive cells, and said current data is analyzed to determine a trajectory of the terminal and detect whether a speed of movement of the terminal on said trajectory is greater than a threshold.

7. Method according to one of claims 1 to 4 and 6, implemented by an entity of the mobile network and by the execution of a function of the NWDAF type - “Network Data Analytic Function”.

8. Method according to one of claims 5 and 6, implemented by an entity of the mobile network and by the execution of a GMLC - “Gateway Mobile Location Center” type function.

9. Method according to one of the preceding claims, in which said current data comprises (S3) at least one identifier of the terminal and at least one user identifier of the terminal.

10. The method of claim 9, further comprising: - consult a platform (USS) of pilot recordings and / or flight plans of devices carrying terminals, and - determining (S4) at least whether the user identifier of the terminal is declared among said records of the platform.

11. A method according to claim 10, taken in combination with one of claims 4 and 6, further comprising: - consult said platform to determine (S5) whether at least the trajectory of the terminal is in accordance with a flight plan declared for said terminal.

12. A method according to one of claims 10 and 11, taken in combination with one of claims 1 and 4, further comprising: - check (S5) whether said trajectory includes at least one sensitive site, associated with restrictive overflight conditions.

13. Computer program comprising instructions for implementing the method according to one of the preceding claims, when executed by a processing circuit.

14. Device comprising a processing circuit for implementing the method according to one of claims 1 to 12.

15. Device according to claim 14, comprising a connection (COM) to a core (CN) of the mobile network, on the one hand, and to a platform (USS) for recording pilots and / or flight plans of devices carrying terminals, on the other hand.

16. Device according to one of claims 14 and 15, arranged to execute at least one function among at least one function of the NWDAF type - “Network Data Analytic Function” and one function of the GMLC type - “Gateway Mobile Location Center”.

17. Device according to claim 15 combined with claim 16, the device being arranged to perform said function, in connection with said USS platform, via a function of the NEF type - “Network Exposition Function”.

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