Flying object monitoring system and method

Abstract

To provide a flying object monitoring system that visualizes a state of a flying object when its flight is unstable, in order to make it easier to detect the flying object which is not functioning properly.SOLUTION: A flying object monitoring system according to an embodiment, comprises: a receiving unit that receives flight information of a flying object; a calculation unit that calculates an acceleration of the flying object for each flight direction from the flight information; a representing unit that represents the calculated acceleration as a distribution image plotted along the flight direction; and a determination unit that determines whether the flying object is malfunctioning or not based on the acceleration distribution image.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] An embodiment of the present invention relates to an air vehicle monitoring system and method for monitoring an air vehicle such as a drone. [Background technology]

[0002] In recent years, the use of drones has increased for personal, commercial and military purposes.

[0003] Accordingly, the development of UTM (drone traffic management system) is progressing. UTM is a drone traffic management system adopted by ISO 23629-5 UAS traffic management (UTM)-Part 5:UTM functional structure. Therefore, linking with UTM will be essential for flying drones in the future. Linking with UTM makes it possible to obtain registration information, which in turn makes it possible to obtain information about airspace including properly registered drones.

[0004] Information that can be obtained by UTM includes, for example, permitted airspace, information on manned aircraft air traffic control systems, drone aircraft and pilot information, drone location information in flight, drone incidents, accident information, weather information, and map information.

[0005] In addition to the installation of UTM, it will also be mandatory from June 2022 for drones to be equipped with a function to transmit remote IDs. This means that drones will be able to fly while transmitting remote IDs.

[0006] Information that can be obtained by the Remote ID includes, for example, registration number, serial number, location information (eg, latitude and longitude), speed, altitude, and time.

[0007] In this way, information about drones currently in flight can be obtained not only through UTM but also through Remote ID. [Prior art documents] [Patent documents]

[0008] [Patent Document 1] JP 2023-051403 A [Patent Document 2] Patent Publication No. 2021-043598 [Patent Document 3] JP 2022-141240 A Summary of the Invention [Problem to be solved by the invention]

[0009] However, the drone information obtained from UTM and remote ID is based on the assumption that the drone is in good working order, meaning that there are no abnormalities such as equipment problems and that the drone is flying normally.

[0010] For example, if the drone is damaged, has a switch malfunction, has a mechanical failure such as a broken wire, has lost communication with the pilot, has a bug in the control software, has failures in various sensors not involved in transmitting the Remote ID, has a battery malfunction such as a loss of power, or has partial errors in the Remote ID information (for example, a discrepancy in flight time), the drone will be unable to fly normally and the information from the UTM and Remote ID will become unreliable.

[0011] However, for drones that are already registered or have a known remote ID, currently there is no choice but to treat even malfunctioning drones as normal drones. Therefore, even if a drone is malfunctioning, it cannot be detected, and there is a risk that it will not be possible to detect in advance the possibility of a crash, etc.

[0012] Therefore, a health check function is needed to ensure that the drone is in good condition. However, a drone's health check cannot be performed based only on information such as UTM and remote ID. For example, a drone's health check can be performed by displaying the drone's flight track and checking that there is no unstable flight. However, when there are a large number of drones, the flight tracks overlap and cannot be distinguished, so this is not always an effective method.

[0013] As such, it is not easy to detect malfunctioning drones at present.

[0014] The present invention has been made in consideration of these circumstances, and aims to provide an aircraft monitoring system and method that visualizes the state of an aircraft whose flight is unstable, in order to make it easier to discover malfunctioning aircraft. [Means for solving the problem]

[0015] The flying object monitoring system of the embodiment includes a receiving unit that receives flight information of the flying object, a calculation unit that calculates the acceleration of the flying object for each flight direction from the flight information, a representation unit that represents the calculated acceleration in an acceleration distribution image formed by plotting it with respect to the flight direction, and a determination unit that determines whether the flying object is malfunctioning based on the acceleration distribution image. [Brief description of the drawings]

[0016] [Figure 1] FIG. 1 is a block diagram showing an example of the configuration of a flying object monitoring system to which a flying object monitoring method according to a first embodiment is applied. [Diagram 2] FIG. 2 is a schematic diagram showing some examples of representations of radar chart images. [Diagram 3] FIG. 3 is a schematic diagram showing an example of a radar chart image in which the density of the fill is changed according to the magnitude of the acceleration in the vertical direction. [Figure 4] Figure 4 is a diagram explaining an example of similarity determination using AI. [Diagram 5] FIG. 5 is a diagram showing an example of a map screen on which a drone icon is arranged superimposed on a malfunction degree display image in the first embodiment. [Figure 6] FIG. 6 is a diagram showing how the color of the malfunction level display image changes when a malfunctioning drone changes into a suspicious drone. [Figure 7] FIG. 7 is a flowchart showing an example of the operation of the flying object monitoring system of the first embodiment. [Figure 8] FIG. 8 is a diagram showing an example of a map screen on which an icon of a drone that has been determined to be malfunctioning or suspicious and detailed information are arranged in the second embodiment. [Figure 9] FIG. 9 is a flowchart showing an operation example of the flying object monitoring system of the second embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0017] Hereinafter, each embodiment of the present invention will be described with reference to the drawings. The drawings are schematic or conceptual, and the relationship between the thickness and width of each part, the size ratio between parts, etc. are not necessarily the same as the actual ones. Even when the same part is shown, the dimensions and ratios of each part may be different depending on the drawing. In this specification and each drawing, elements similar to those described above with respect to the previous drawings are given the same reference numerals, and detailed explanations and duplicated explanations are omitted as appropriate.

[0018] (First embodiment) First, a first embodiment of the present invention will be described.

[0019] FIG. 1 is a block diagram showing an example of the configuration of a flying object monitoring system to which a flying object monitoring method according to a first embodiment is applied.

[0020] The airborne object monitoring system 10 of the first embodiment includes a CPU 12, a recording medium reading unit 14, an input unit 15, a display screen 16, a receiving unit 17, an antenna 18, a memory 20, and a storage device 30, all connected together by a bus 11.

[0021] In the following description of this embodiment, the flying object will be described as a drone, but this is merely an example, and the flying object is not necessarily limited to a drone as long as it can be flown by remote control.

[0022] The CPU 12 is a computer that controls the operation of each part in the airborne object monitoring system 10 in accordance with various programs stored in the memory 20 .

[0023] The input unit 15 may be, for example, a keyboard, a mouse, a track board, etc. A user can input necessary operation information to the airborne object monitoring system 10 via the input unit 15.

[0024] The display screen 16 displays image information provided by the flying object monitoring system 10. The display screen 16 can be, for example, a display such as a liquid crystal display. In addition, if the display screen 16 is a liquid crystal display, it may be a liquid crystal display with a touch panel function. In this case, both the input unit 15 and the display screen 16 can be realized by a liquid crystal display with a touch panel function.

[0025] The receiver 17 receives drone flight information a from the UTM 40 via a communication network such as the Internet, and outputs the received flight information a to the memory 20. As described above, the drone flight information a from the UTM 40 includes information on permitted airspace, information on the manned aircraft air traffic control system, the drone's body during flight, pilot information, position information of the drone during flight, drone incidents, accident information, weather information, and map information.

[0026] The antenna 18 receives the remote ID continuously transmitted from the drone D in flight, and outputs the received remote ID to the memory 20. As described above, the remote ID includes a registration number, a serial number, location information (e.g., latitude and longitude), speed, altitude, and time.

[0027] Note that this configuration including the receiver 17 and antenna 18 is just one example, and the drone D in flight can continuously transmit a remote ID (simply indicated as "ID" in FIG. 1) to the UTM 40, and the receiver 17 can receive both the flight information a of the drone in flight and the remote ID from the UTM 40. In such a case, the antenna 18 can be omitted.

[0028] The storage device 30 is, for example, a solid state drive (SSD) or a hard disk drive (HDD), and stores a radar chart database 31, which will be described later.

[0029] The memory 20 stores various programs for implementing the flying object monitoring system 10, and these programs implement a calculation unit 21, a representation unit 22, a determination unit 23, a display control unit 24, and a simulation unit 25.

[0030] The programs that realize the calculation unit 21, the expression unit 22, the determination unit 23, the display control unit 24, and the simulation unit 25 may be pre-stored in the memory 20, or may be read from an external recording medium 13 such as a memory card via the recording medium reading unit 14 and stored in the memory 20. These programs cannot be rewritten.

[0031] In addition to such an area that cannot be rewritten by the user, the memory 20 also has a writable data area 26 secured therein as an area for storing rewritable data.

[0032] The calculation unit 21 calculates the acceleration b for each flight direction of the drone D using the flight information a and the information contained in the remote ID.

[0033] The representation unit 22 represents the acceleration b for the past few seconds for each flight direction of the drone D calculated by the calculation unit 21, in a radar chart image formed by plotting the acceleration b for each flight direction on a horizontal plane. That is, the radar chart image is an acceleration distribution image showing the acceleration distribution.

[0034] FIG. 2 is a schematic diagram showing some examples of representations of radar chart images.

[0035] 2(a), 2(b), and 2(c) all show arrows extending in four directions at 90° intervals, with the direction of travel of drone D on a horizontal plane being the 0° direction, and radar chart images c1, c2, and c3 (hereinafter sometimes collectively referred to as "radar chart images c") are formed by plotting the acceleration b for each flight direction of drone D calculated by the calculation unit 21 with respect to the flight direction on the horizontal plane. The radar chart image c thus formed is superimposed on the icon of drone D by the representation unit 22 and displayed on the display screen 16.

[0036] In the representation in FIG. 2, no arrow is displayed indicating the acceleration in the vertical direction, which is the altitude direction. However, the representation unit 22 can represent the acceleration in the vertical direction by changing the density of the fill in the radar chart image c according to the magnitude of the acceleration, and display it on the display screen 16.

[0037] FIG. 3 is a schematic diagram showing an example of a radar chart image in which the density of the fill is changed according to the magnitude of the acceleration in the vertical direction.

[0038] In the case of a drone D that is not stable and is flying unsteadily, the radar chart image c1 has a large horizontal display area, as shown in Fig. 2(a). Furthermore, the smaller the vertical acceleration of the drone D, the lighter the radar chart image c1 is displayed as being filled in, as shown in Fig. 3(a), and the larger the vertical acceleration, the darker the radar chart image c1 is displayed as being filled in, as shown in Fig. 3(b).

[0039] In the case of drone D hovering in the vertical direction, as shown in Fig. 2(b), a round radar chart image c2 with a small display area is displayed. This radar chart image c2 is also displayed with a lighter fill as the vertical acceleration of drone D decreases, and with a darker fill as the vertical acceleration increases.

[0040] In the case of drone D having a straight-line component in the horizontal direction, as shown in Fig. 2(c), the radar chart image c3 has an acute apex in the straight-line direction, for example, an isosceles triangle. This radar chart image c3 is also displayed with a lighter fill as the vertical acceleration of drone D decreases, and with a darker fill as the vertical acceleration increases.

[0041] The determination unit 23 determines whether the drone D is malfunctioning or not based on the radar chart image c represented as in Figures 2 and 3 .

[0042] The determination unit 23 can first determine whether a drone is malfunctioning based on the density of the fill in the radar chart image c. Specifically, when the radar chart image c is filled in with a dark color, the corresponding drone D can be determined to be malfunctioning. This is because a large acceleration in the vertical direction means a high risk of crashing.

[0043] The value of the vertical acceleration that is the boundary between malfunction and non-malfunction can be arbitrarily or empirically determined by the user. Therefore, based on the radar chart image c that is filled in darker than the darkness corresponding to this boundary acceleration, the determination unit 23 determines that the drone D is malfunctioning. Conversely, based on the radar chart image c that is filled in lighter than the darkness corresponding to this boundary acceleration, the determination unit 23 determines that the drone D is not malfunctioning.

[0044] The determination unit 23 can further determine whether the drone D is malfunctioning from the shape of the radar chart image c. For this determination, the determination unit 23 compares the shape of the radar chart image c expressed by the expression unit 22 with the shape of a comparative radar chart image. In this manner, the radar chart image c is used to determine the malfunction degree of the drone D, and therefore in this specification, the radar chart image formed by the expression unit 22 is also referred to as a malfunction degree display image.

[0045] The comparative radar chart image C is accumulated in a radar chart database 31 stored in the storage device 30. In the radar chart database 31, radar chart images showing malfunctioning drones and radar chart images showing non-malfunctioning drones are accumulated as comparative radar chart images C. In addition, radar chart images c previously determined by the determination unit 23 are also associated with the determination result of malfunction or non-malfunction and accumulated as comparative radar chart images C.

[0046] For example, a radar chart image with a large horizontal display area, such as the malfunction level display image c1, means that the movement of the drone D is unstable and the drone D is flying unsteadily. Such radar chart images are stored in association with malfunctions.

[0047] The determination unit 23 compares the shape of the radar chart image c, i.e., the shape of the malfunction level display image c, with the shape of the radar chart image C stored in the radar chart database 31. If the radar chart image C having a shape similar to the shape of the malfunction level display image c is associated with a malfunction, the determination unit 23 determines that the corresponding drone D is malfunctioning, and if the radar chart image C is associated with a non-malfunction, the determination unit 23 determines that the corresponding drone D is not malfunctioning.

[0048] If the radar chart database 31 does not store a radar chart image C having a shape similar to the shape of the malfunction degree display image c, the judgment unit 23 judges whether the drone D is malfunctioning or not based on the results of the simulation by the simulation unit 25.

[0049] The simulation unit 25 simulates the flight path of the drone D using the flight information a and the remote ID of the drone D. Then, if the flight path obtained by the simulation has a movement that seems to be unnatural, the determination unit 23 can determine that the drone D is malfunctioning, and if there is no unnatural movement, the determination unit 23 can determine that the drone D is not malfunctioning.

[0050] In this manner, the determination unit 23 determines whether the drone D is malfunctioning or not. Then, the determined malfunction degree display image c is stored in the radar chart database 31 as a new radar chart image C for comparison in association with the determination result.

[0051] It should be noted that AI may be incorporated in the determination unit 23, and the determination unit 23 may use the AI ​​to determine whether or not the shape of the malfunction degree display image c and the shape of the radar chart image C are similar.

[0052] An example of similarity determination using AI is explained using Figure 4.

[0053] As described above, the radar chart database 31 stores radar chart images indicating malfunctioning drones and radar chart images indicating non-malfunctioning drones, associated with the judgment results, i.e., whether the drone is malfunctioning or not.

[0054] In other words, the radar chart database 31 stores radar chart images c1 of a drone flying “unsteadily” as described in FIG. 2(a), radar chart images c2 of a drone “hovering” as described in FIG. 2(b), and radar chart images c3 of a drone “flying in a straight line” as described in FIG. 2(c), associated with whether they are malfunctioning or not.

[0055] The radar chart images thus stored in the radar chart database 31 are used as learning data and are learned in advance by the AI.

[0056] When the AI ​​thus learned is input with an unknown malfunction display image (d), the AI ​​outputs an image similar to the unknown malfunction display image (d). If the similar image indicates a malfunction drone, the determination unit 23 can determine that the drone D corresponding to the unknown malfunction display image (d) is a malfunction drone. Conversely, if the similar image indicates a non-malfunction drone, the determination unit 23 can determine that the drone D corresponding to the unknown malfunction display image (d) is a non-malfunction drone.

[0057] Next, the display control unit 24 will be described.

[0058] FIG. 5 is a diagram showing an example of a map screen on which an icon of a drone D is arranged superimposed on a malfunction level display image c in the first embodiment.

[0059] As illustrated in Figure 5, the display control unit 24 places an icon of drone D on which an inconvenience display image c filled with a darkness corresponding to the vertical acceleration is superimposed, at a position on a map determined based on the flight information a of drone D and the remote ID, and displays it on the display screen 16.

[0060] 5, eight icons of drones D1 to D8 are displayed on the map M. The positions on the map M where the icons of drones D1 to D8 are displayed correspond to positions determined based on the flight information a and remote ID of each of the drones D1 to D8. The map M may or may not be an aerial photograph.

[0061] A malfunction level display image c for each of the drones D1 to D8 is superimposed on each icon of the drones D1 to D8. Each malfunction level display image c is filled with a density according to the acceleration in the vertical direction.

[0062] For example, the malfunction level display image c2 as shown in Fig. 2(b) is lightly filled and superimposed on the drones D1 and D5. By seeing this, the user can recognize that the drone D1 is a drone that is hovering slowly and is not malfunctioning.

[0063] In addition, the malfunction level display image c3 as shown in Fig. 2(c) is lightly filled and superimposed on the drones D3, D4, D6, and D7. By seeing this, the user can recognize that the drones D3, D4, D6, D7, and D8 are not malfunctioning drones that are flying straight without large acceleration in the vertical direction.

[0064] On the other hand, a malfunction level display image c1 as shown in FIG. 2(a) is superimposed on the drone D2, with a dark fill. By seeing this, the user can recognize that the drone D2 is a malfunctioning drone that is flying unsteadily with a large acceleration in the vertical direction. Furthermore, for the drone D2 that is determined to be a malfunctioning drone, detailed information E is also displayed near the icon of the drone D2 on the map M, indicating "malfunction (needs capture)", "model number", "pilot information", "speed", and "altitude". With these, the user can obtain detailed information about the drone D2 in addition to the determination result that the drone D2 is a malfunctioning drone.

[0065] Furthermore, the drone D8 has entered a prohibited area R. The prohibited area R is an area where entry is restricted or prohibited, such as a government agency, private property, military facility, or airport. Based on the flight information a, the determination unit 23 determines that the drone D that has entered the prohibited area R is a suspicious drone that should be captured, regardless of whether it is malfunctioning or not. Note that the prohibited area R is determined in advance, so the determination unit 23 can immediately identify the drone D that has approached / entered the prohibited area R as a suspicious drone based on the position information of the drone D.

[0066] The determination unit 23 also determines, as suspicious drones, drones that have not been registered, drones that are prohibited from flying due to an incident, etc., and drones that have not transmitted a remote ID. Drones that have not been registered, drones that are prohibited from flying due to an incident, etc., and drones that have not transmitted a remote ID can be easily detected by checking the UTM and remote ID information.

[0067] The display control unit 24 changes the color of the malfunction level display image c of the drone D determined by the determination unit 23 to be a suspicious drone to a clear color such as red (displayed with hatching in Figs. 5 and 6) and displays it on the display screen 16. In addition, the display control unit 24 may also highlight the detailed information E by displaying it in a clear color such as red, in bold, or in a font such as italics.

[0068] In this way, the user can immediately recognize the suspicious drone by seeing the malfunction level display image c, which has changed to, for example, red, and / or the detailed information E on the display screen 16. For example, as illustrated in Fig. 6(a), the user can immediately recognize that the drone D has changed from a malfunctioning drone to a suspicious drone by seeing that the malfunction level display image c1 superimposed on the icon of the drone D that was determined to be malfunctioning on the display screen 16 changes to, for example, red, as illustrated in Fig. 6(b).

[0069] The drone D8 shown in FIG. 5 has entered a no-entry area R and has been determined to be a suspicious drone, so the malfunction level display image c3 is displayed on the display screen 16 in red, for example.

[0070] All suspicious drones are subject to capture, so users can immediately begin preparations for capture.

[0071] Specific capture methods include those using noise radio waves, those using a net, and those using a capture drone (counter drone), as disclosed in Patent Document 3. Therefore, by incorporating the flying object monitoring system 10 of the present embodiment into the system disclosed in Patent Document 3, it becomes possible to quickly discover and capture a malfunctioning or suspicious drone.

[0072] As described above, in the flying object monitoring system 10, hardware such as the bus 11, CPU 12, recording medium reading unit 14, input unit 15, display screen 16, receiving unit 17, antenna 18, memory 20, and storage device 30 operate in cooperation with software which is each program stored in memory 20.

[0073] Next, an example of the operation of the flying object monitoring system according to the first embodiment configured as above will be described.

[0074] FIG. 7 is a flowchart showing an example of the operation of the flying object monitoring system of the first embodiment.

[0075] In step S1, data related to the drone D, such as flight information a of the drone D and the remote ID, is received. This is performed by the receiver 17 receiving the flight information a of the drone D from the UTM 40, and the antenna 18 receiving the remote ID from the drone D in flight. Note that when the drone D in flight transmits a remote ID to the UTM 40, this is performed by the receiver 17 receiving both the flight information a of the drone in flight and the remote ID from the UTM 40.

[0076] In step S2, the calculation unit 21 calculates the acceleration of the drone D. This is performed by the calculation unit 21 calculating the acceleration b for each flight direction of the drone D using the flight information a and the information included in the remote ID.

[0077] In step S3, the expression unit 22 generates a malfunction level display image c of the drone D. The malfunction level display image c is formed by plotting the acceleration b for the past few seconds for each flight direction of the drone D with respect to the flight direction on a horizontal plane. That is, the malfunction level display image c is a radar chart image c showing the acceleration distribution of the drone D. Also, the malfunction level display image c is displayed with a lighter fill as the vertical acceleration of the drone D decreases, and is displayed with a darker fill as the vertical acceleration increases.

[0078] In step S4, the judgment unit 23 judges whether the drone D is malfunctioning or not based on the malfunction degree display image c.

[0079] First, in the judgment unit 23, if the malfunction level display image c is filled in with a darkness darker than the darkness corresponding to the boundary acceleration that is the boundary between malfunction and non-malfunction, the corresponding drone D is judged to be malfunctioning.

[0080] The determination unit 23 further determines whether the drone D is malfunctioning or not malfunctioning from the shape of the malfunction level display image c. For this determination, the determination unit 23 compares the shape of the malfunction level display image c with the shape of the radar chart image C stored in the radar chart database 31. If the radar chart image C, which has a shape similar to the malfunction level display image c, is associated with a malfunction, the drone D is determined to be malfunctioning, and if it is associated with a non-malfunction, the drone D is determined to be not malfunctioning. The determination unit 23 can also use AI to determine whether the shape of the malfunction level display image c and the shape of the radar chart image C are similar.

[0081] In addition, if the radar chart database 31 does not store a radar chart image C having a similar shape to the malfunction degree display image c, the judgment unit 23 judges whether the drone D is malfunctioning or not based on the results of the simulation by the simulation unit 25.

[0082] In step S5, the health check results of each drone D in flight are visualized and displayed on the display screen 16, as illustrated in FIG.

[0083] By looking at a screen such as the example shown in Figure 5, the user can easily understand the status of each drone D during flight.

[0084] In step S6, if an unknown malfunction level display image c is displayed in the image as exemplified in FIG. 5 (S6: Yes), the process proceeds to step S7, and if not (S6: No), the process returns to step S1.

[0085] The determination unit 23 can also use AI to learn the unknown malfunction degree display image c in step S7. After step S7, the process returns to step S1.

[0086] As described above, according to the flying object monitoring system 10 of this embodiment, the state of the drone D that may be malfunctioning can be visualized and displayed on a map from public information regarding the flight of the drone D. In addition, suspicious drones can also be discovered and displayed on a map.

[0087] In addition, AI can also be used to determine whether a drone is malfunctioning, and the learning effect of the AI ​​can improve the accuracy of determining whether a drone is malfunctioning.

[0088] These features allow users to easily find malfunctioning or suspicious drones. Therefore, by incorporating the airborne object monitoring system 10 of the present embodiment into the system disclosed in Patent Document 3, it becomes possible to quickly discover and capture malfunctioning or suspicious drones.

[0089] Second Embodiment Next, a second embodiment of the present invention will be described.

[0090] The flying object monitoring system of the second embodiment is a modified example of the flying object monitoring system of the first embodiment, and has a configuration as shown in FIG.

[0091] Therefore, only the points that differ from the first embodiment will be described here, and since the other points have already been described in the first embodiment, duplicate explanations will be avoided.

[0092] In addition to the first embodiment, in the second embodiment, the determination unit 23 determines whether the drone D is malfunctioning based on the flight information a, and if it is determined that the drone D is malfunctioning, it further estimates the reason for the malfunction. The reason for determining that the drone is malfunctioning may be, for example, a breakdown in communication with the pilot, but is not limited thereto.

[0093] Flight information a from UTM40 does not include information regarding the health status of drone D. In other words, flight information a from UTM40 does not include information regarding the communication disruption of drone D. Therefore, the status of drone D regarding the communication disruption cannot be determined directly from flight information a.

[0094] When communication is interrupted, drone D is no longer able to receive instructions from the pilot, and so, even before arriving at the pre-designated destination, it simply hovers at the location where communication was interrupted, much like a lost person looking around, and its flight speed drops significantly.

[0095] The UTM 40 grasps the position of the drone D based on the remote ID continuously transmitted from the drone D. Therefore, based on the position information continuously transmitted from the drone D, the UTM 40 calculates the flight speed of the drone D, includes it in the flight information a, and transmits it to the airborne object monitoring system 10A.

[0096] This flight information a is received by the receiving unit 17. Then, if the flight information a indicates that the flight speed significantly decreased before arriving at the destination, the determination unit 23 estimates that a communication breakdown has occurred with this drone D. In this way, the determination unit 23 can indirectly estimate the communication breakdown of the drone D based on the flight information a. The determination unit 23 determines that the drone D that is estimated to have experienced a communication breakdown is a malfunctioning drone.

[0097] As mentioned above, when communication is lost, drone D hovers at the location where communication was lost, and loses momentum in the direction of travel. As a result, drone D may be blown by the wind and deviate from the flight path (airspace) planned in advance.

[0098] The UTM 40 keeps track of the flight path of the drone. In addition, the UTM 40 keeps track of the position of the drone D based on the remote ID transmitted from the drone D. Therefore, based on the position information continuously transmitted from the drone D, if the drone D deviates significantly (for example, by 40 m or more) from the flight path planned in advance, the UTM 40 includes this in the flight information a and transmits it to the airborne object monitoring system 10.

[0099] It is impossible to distinguish whether drone D, which has significantly deviated from the pre-planned flight path, is a malfunctioning drone without any malicious intent, or whether it is a dangerous drone that has intentionally deviated with malicious intent. Therefore, drone D, which has significantly deviated from the pre-planned flight path, must be captured, regardless of whether it has malicious intent or not.

[0100] If the flight information a includes information indicating a significant deviation from the flight path, the judgment unit 23 judges the drone D to be a suspicious drone that should be captured.

[0101] Furthermore, as explained in the first embodiment, a drone D that enters a pre-designated no-entry area R should be captured as a suspicious drone D regardless of whether or not a communication outage has occurred.

[0102] The judgment unit 23 recognizes the position of the drone D from the flight information a, and if the recognized position is within a pre-designated no-entry area R, judges the drone D to be a suspicious drone that should be captured.

[0103] FIG. 8 is a diagram showing an example of a map screen on which an icon of a drone that has been determined to be malfunctioning or suspicious and detailed information are arranged in the second embodiment.

[0104] As described in the first embodiment, the display control unit 24 can arrange the icon of the drone D, on which the malfunction level display image c filled with a darkness according to the vertical acceleration is superimposed, at a position on the map determined based on the flight information a and remote ID of the drone D, and display it on the display screen 16. In this embodiment, the display control unit 24 further displays detailed information E including text information clearly indicating the determination result by the determination unit 23 near the icon of the drone D on the display screen 16.

[0105] The judgment result is, for example, "malfunction" or "suspicious." In addition to the judgment result, the detailed information E can also include specific information indicating the grounds for the judgment result, such as "communication interruption." As already shown in FIG. 5, the detailed information E can also include supplemental information such as the model number, pilot information, speed, and altitude.

[0106] The display control unit 24 can highlight the characters in the detailed information E of the drone D that has been determined to be "malfunctioning" or "suspicious" by the determination unit 23 on the display screen 16, for example, by displaying them in a vivid color such as red, displaying them in bold, displaying them in a font such as italics, etc. Similarly, the display control unit 24 can also highlight the icon of the drone D that has been determined to be "malfunctioning" or "suspicious" by the determination unit 23 on the display screen 16, for example, by displaying them in a vivid color such as red, blinking, etc. Furthermore, the display control unit 24 may display the icon and detailed information of the malfunctioning drone and the icon and detailed information of the suspicious drone in different colors.

[0107] By looking at such map information, the user can easily recognize the occurrence and location of drones that have been determined to be "malfunctioning" or "suspicious." In addition, by further adding specific information indicating the basis for the determination result, such as "communication interruption," to the detailed information E, it becomes possible to grasp more specific information regarding malfunctioning or suspicious drones. In addition, by displaying the icon and detailed information of a malfunctioning drone and the icon and detailed information of a suspicious drone in different colors, it is possible to distinguish between malfunctioning drones and suspicious drones.

[0108] Next, an example of the operation of the flying object monitoring system of the second embodiment configured as above will be described below. Fig. 9 is a flowchart showing an example of the operation of the flying object monitoring system of the second embodiment.

[0109] First, similar to step S1 shown in FIG. 7, data related to drone D, such as flight information a of drone D and a remote ID, is continuously received (S11).

[0110] The judgment unit 23 recognizes the position of the drone D from the flight information a, and if the recognized position is within a pre-designated no-entry area R (S12: Yes), it judges that the drone D is a suspicious drone that should be captured (S16).

[0111] If a drone is determined to be suspicious, the display control unit 24 determines the location based on the flight information a and the remote ID, and an icon representing the drone is placed on a map and displayed on the display screen 16. In addition, the display screen 16 also displays detailed information E about the drone near the drone icon. The detailed information E is highlighted, for example, by displaying in a vivid color such as red, in bold, or in a font such as italics.

[0112] By looking at such map information, users can easily recognize the occurrence and location of "suspicious" drones. In addition, by referring to detailed information E, it is possible to grasp more specific information about the suspicious drone.

[0113] Even if drone D is determined in step S12 to not be within restricted area R (S12: No), if communication is interrupted, it will hover at the location where communication was interrupted, resulting in a significant reduction in flight speed (S13: Yes).

[0114] In this way, when the flight information a indicates that the flight speed has significantly decreased, the judgment unit 23 estimates that a communication outage has occurred with this drone D, and judges it to be a malfunctioning drone (S14).

[0115] The display control unit 24 also determines the position of the drone that has been determined to be malfunctioning based on the flight information a and the remote ID, and an icon representing the drone is placed on a map and displayed on the display screen 16. In addition, the display screen 16 also displays detailed information E about the drone near the drone icon. The detailed information E is highlighted, for example, by displaying in a vivid color such as red, in bold, or in a font such as italics.

[0116] By looking at such map information, the user can easily recognize the occurrence and location of the "malfunctioning" drone. In addition, by referring to the detailed information E, the user can grasp more specific information about the malfunctioning drone.

[0117] As mentioned above, drone D that has experienced a communication outage hovers at the location where communication was lost, and loses propulsion in the direction of travel. As a result, drone D may be blown by the wind and deviate from the flight path (airspace) planned in advance. It is impossible to distinguish whether drone D that has significantly deviated from the flight path (S15: Yes) is a drone that has simply deviated due to a malfunction without any malicious intent, or whether it is a dangerous drone that has deviated intentionally with malicious intent. Therefore, drone D that has significantly deviated from the flight path planned in advance is treated as a suspicious drone that needs to be captured, regardless of whether it has malicious intent or not.

[0118] In other words, if the flight information a includes information indicating a significant deviation from the flight path, the judgment unit 23 judges that the drone D is a suspicious drone (S15: Yes), and the processing of step S16 described above is performed.

[0119] If the flight speed has not decreased significantly in step S13 (S13: No) and if the flight has not deviated from the route in step S15 (S15: No), the process returns to step S11 and the processing from steps S12 to S16 is repeated based on the new flight information a.

[0120] As described above, according to the flying object monitoring system 10 of the present embodiment, it is possible to determine a malfunctioning drone, for example, one that has lost communication. In addition, the location of the malfunctioning drone can be explicitly shown on a map while displaying detailed information about the malfunctioning drone. Furthermore, the location of a suspicious drone that has entered a no-entry area or deviated from its flight path can be explicitly shown on a map while displaying detailed information about the suspicious drone. At this time, it is possible to distinguish between a malfunctioning drone and a suspicious drone and easily and reliably grasp the location.

[0121] Although some embodiments of the present invention have been described, these embodiments are presented as examples and are not intended to limit the scope of the invention. These embodiments can be implemented in various other forms, and various omissions, substitutions, and modifications can be made without departing from the spirit of the invention. These embodiments and their modifications are included in the scope of the invention and its equivalents described in the claims, as well as in the scope and spirit of the invention. [Explanation of symbols]

[0122] 10. Missile Surveillance System 11 Bus 12 CPU 13 External recording media 14 Recording medium reading unit 15 Input section 16 Display screen 17 Receiving section 18 Antenna 20 Memory 21 Calculation section 22 Expression part 23 Judgment section 24 Display control section 25 Simulation Department 26 Writable Data Area 30 Storage device 31 Radar Chart Database a. Flight Information b acceleration c Radar chart image, malfunction display image C Radar chart image for comparison D. Drone E More information M Map R No entry area

Claims

1. A receiving unit for receiving flight information of the flying object; A calculation unit that calculates an acceleration of the flying object for each flight direction from the flight information; a representation unit that represents the calculated acceleration in an acceleration distribution image formed by plotting the calculated acceleration with respect to the flight direction; a determination unit that determines whether the flying object is malfunctioning or not based on the acceleration distribution image; An airborne object surveillance system.

2. 2. The flying object monitoring system according to claim 1, further comprising a database in which an acceleration distribution image showing a malfunctioning flying object and an acceleration distribution image showing a non-malfunctioning flying object are stored.

3. 3. The flying object monitoring system according to claim 2, wherein the determining unit determines whether the flying object is malfunctioning by comparing the represented acceleration distribution image with acceleration distribution images stored in the database.

4. The flying object monitoring system of claim 3, wherein the determination unit determines that the flying object is malfunctioning if the represented acceleration distribution image is similar to an acceleration distribution image indicating the malfunctioning flying object stored in the database.

5. A simulation unit that simulates a flight path of the flying object based on the flight information, The flying object monitoring system of claim 4, wherein the determination unit determines whether or not the flying object is malfunctioning based on a result of a simulation by the simulation unit when the represented acceleration distribution image is not similar to any of the acceleration distribution images stored in the database.

6. The flying object monitoring system of claim 5, wherein the determination unit newly stores the represented acceleration distribution image in the database as an acceleration distribution image indicating the malfunctioning flying object or as an acceleration distribution image indicating the non-malfunctioning flying object depending on the result of the determination made based on the result of the simulation.

7. The flying object monitoring system of any one of claims 1 to 6, further comprising a display control unit that displays an icon representing the flying object superimposed on the represented acceleration distribution image at a position on a map determined based on flight information of the flying object.

8. 7. The flying object monitoring system according to claim 1, wherein the acceleration distribution image is a radar chart image formed by plotting the calculated acceleration with respect to the flight direction.

9. A receiving unit for receiving flight information of the flying object; A determination unit that determines whether the flying object is malfunctioning based on the flight information; An airborne object monitoring system which, when the determination unit determines that the airborne object is malfunctioning, displays an icon representing the airborne object and text information emphasizing and clearly indicating the determination result by the determination unit near a position on a map that is determined based on flight information of the airborne object.

10. The airborne object monitoring system of claim 9, wherein when the flight information indicates that the flight speed of the airborne object has significantly decreased before arriving at a pre-specified destination, the determination unit determines that a communication outage has occurred with the airborne object.

11. The flying object monitoring system of claim 10, wherein when the flight information indicates that the flying object determined to have experienced a communication outage has significantly deviated from a pre-planned flight path, the determination unit determines that the flying object is a suspicious flying object that should be captured.

12. The flying object monitoring system of claim 10, wherein when the flight information indicates that the flying object determined to have experienced a communication outage has entered a pre-designated no-entry area, the determination unit determines that the flying object is a suspicious flying object that should be captured.

13. A receiving unit for receiving flight information of the flying object; a determination unit that determines whether the flying object is a suspicious flying object that should be captured based on the flight information; a display control unit that displays an icon indicating the flying object and text information clearly indicating that the flying object is a suspicious flying object near a position on a map that is determined based on flight information of the flying object; An airborne object surveillance system.

14. The flying object monitoring system according to claim 13, wherein, when the flight information indicates that the flying object has deviated from a pre-designated route, the determination unit determines that the flying object is a suspicious flying object that should be captured.

15. The flying object monitoring system is provided with a determination unit that determines that a flying object that has entered a pre-designated no-entry area is a suspicious flying object that should be captured.

16. An airborne object monitoring method implemented by an airborne object monitoring system, comprising: A processor of the airborne vehicle monitoring system, Receive flight information of the aircraft, Calculating the acceleration of the flying object for each flight direction from the flight information; representing the calculated accelerations in an acceleration distribution image formed by plotting them with respect to the flight direction; A method for monitoring a flying object, the method comprising: determining whether or not the flying object is malfunctioning based on the acceleration distribution image.

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