Missile monitoring system, method, and program

Abstract

To provide a flying object monitoring system with high versatility.SOLUTION: According to an embodiment, a flying object monitoring system comprises: a reception unit which receives flight information of a flying object from a sensor which detects the flying object; a first determination unit which determines whether or not the flying object flies on a preset aerial corridor from the flight information; and a classification unit which classifies a flying object determined to fly on the aerial corridor by the first determination unit as a normal flying object, and classifies a flying object which is not determined to fly on the aerial corridor by the first determination unit as an abnormal flying object.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] Drones are convenient and available to anyone. However, they have only recently become widespread on the market, and there has been an increase in drone crashes and other accidents. There has also been an increase in incidents involving drone misuse.

[0004] Drones can easily approach and infiltrate areas where access is restricted or prohibited, such as government agencies, private property, military facilities, and airports, from the air.

[0005] For this reason, drones can be problematic because, if operated by malicious individuals, they can be misused for purposes such as taking unauthorized photographs, transporting explosives, and preventing aircraft from taking off or landing.

[0006] As a means to counter this type of problem, Patent Document 1 discloses a flying object monitoring system. [Prior art documents] [Patent documents]

[0007] [Patent Document 1] Japanese Patent Application Laid-Open No. 2017-167870 [Non-patent literature]

[0008] [Non-Patent Document 1] https: / / drone-journal.impress.co.jp / docs / event / 1182711.html (searched on March 9, 2021) Summary of the Invention [Problem to be solved by the invention]

[0009] However, because the conventional system described above is composed of a surveillance radar, an imaging unit, and a central device, it can adequately monitor and control specific drones that have been anticipated in advance, i.e., existing drones, but there is no guarantee that it will be able to adequately monitor unexpected drones, such as new types of drones that will be developed in the future or secret drones, and it has the problem of lacking versatility.

[0010] The problem to be solved by the present invention is to provide a highly versatile airborne object monitoring system, airborne object monitoring method, and program. [Means for solving the problem]

[0011] The embodiment of the airborne object monitoring system includes a receiving unit that receives flight information of the airborne object from a sensor that detects the airborne object, a first determination unit that determines from the flight information whether the airborne object is flying through a predetermined airborne corridor, and a classification unit that classifies an airborne object that is determined by the first determination unit to be flying through an airborne corridor as a normal airborne object, and an airborne object that is not determined to be flying through an airborne corridor as an abnormal airborne object. [Brief explanation of the drawings]

[0012] [Figure 1] FIG. 1 is an example of a diagram showing the functional relationships of an airborne object monitoring system to which an airborne object monitoring method according to an embodiment of the present invention is applied. [Figure 2] FIG. 2 is a block diagram showing an example of the electronic circuit configuration of the flying object monitoring system. [Figure 3A] FIG. 3A is a diagram for explaining a case where the behavior pattern of the flying object is a charge. [Figure 3B] FIG. 3B is a diagram for explaining a case where the behavior pattern of the flying object is border crossing. [Figure 3C] FIG. 3C is a diagram for explaining a case where the behavior pattern of the flying object is wandering. [Figure 4] FIG. 4 is a diagram showing an example of a risk quantification flow according to the risk assessment program. [Figure 5] FIG. 5 is a diagram for explaining an example of calculating the risk level. [Figure 6] FIG. 6 is a diagram for explaining an example of calculating the risk level. [Figure 7] FIG. 7 is a simplified flow diagram showing an example of a countermeasure decision process using the countermeasure suggestion program. [Figure 8] FIG. 8 is a detailed flow chart showing another example of a countermeasure decision process by the countermeasure suggestion program. [Figure 9] FIG. 9 is a diagram illustrating an example of monitoring control of an assault drone by an airborne object monitoring system. DETAILED DESCRIPTION OF THE INVENTION

[0013] Hereinafter, an embodiment of the present invention will be described with reference to the drawings.

[0014] FIG. 1 is an example of a diagram showing the functional relationships of an airborne object monitoring system to which an airborne object monitoring method according to an embodiment of the present invention is applied.

[0015] The airborne object monitoring system 10 is a system for monitoring airborne objects such as drones (flight determination a2, airborne object classification a3, model identification a4, destination prediction, purpose estimation, and risk determination a5) based on inputs IN such as detection information from a radar 40 or an RF sensor 42, image data from an imaging unit 44 such as a camera, sound information from a sound source sensor 46 such as a microphone, sighting information MJ from witnesses, and the airborne object's remote ID obtained from, for example, a UTM (Unmanned Traffic Management) system.

[0016] Specifically, the system acquires and shares (a1) detection information from radar 40 and RF sensor 42, image data from imaging unit 44 such as a camera, sound information from sound source sensor 46 such as a microphone, and sighting information MJ from witnesses, and performs a flight determination (a2) of the flying object based on this information. Furthermore, by taking into account the remote ID in addition to this information, the system performs flying object classification (a3), model identification (a4), destination prediction, purpose estimation, and danger level determination (a5). Furthermore, if necessary, the system presents (a6) countermeasures for controlling the flying object based on the destination prediction, purpose estimation, and danger level determination (a5), further taking into account the radar 40, RF sensor 42, imaging unit 44, sound source sensor 46, and the status (a8) of witnesses. The system then outputs (OUT) countermeasure instructions (a7) determined in response to the above to appropriate equipment (e.g., noise radio wave transmitter 50, net capture gun 52, capture drone dispatch device 54, security guard KB, etc.).

[0017] FIG. 2 is a block diagram showing an example of the electronic circuit configuration of the flying object monitoring system.

[0018] In order to realize the functions described using Figure 1, the electronic circuit of the airborne object monitoring system 10 includes a CPU 12, a recording medium reading unit 14, a communication unit 15, a memory 20, and a storage device 30, which are connected to each other by a bus 11, as illustrated in Figure 2.

[0019] The memory 20 stores a flight judgment program 21 for making a flight judgment a2, a flying object classification program 22 for making a flying object classification a3, a model identification program 23 for making a model identification a4, a destination prediction program 24 for making a destination prediction, purpose estimation, and risk judgment a5, a purpose estimation program 25, a risk judgment program 26, a countermeasure presentation program 27 for presenting a countermeasure proposal a6, and a countermeasure instruction program 28 for issuing a countermeasure instruction a7.

[0020] These programs 21 to 28 may be stored in advance in the memory 20, or may be read and stored in the memory 20 from an external recording medium 13 such as a memory card via the recording medium reading unit 14. These programs 21 to 28 are designed not to be rewritable.

[0021] In addition to this non-user-rewritable area, the memory 20 also has a writable data area 29 reserved as an area for storing rewritable data.

[0022] The CPU 12 is a computer that controls the operation of each part of the circuit in accordance with each program 21 to 28 stored in the memory 20.

[0023] The storage device 30 is composed of, for example, an SSD (Solid State Drive) or an HDD (Hard Disk Drive), and stores an air corridor information database 31, a performance data database 32, an airborne object image data database 33, a risk assessment criteria data database 34, and a countermeasure related database 35.

[0024] The air corridor information database 31 is a database that stores air corridor information b, which will be described later.

[0025] The performance data database 32 is a database that stores performance data c indicating, for each model (manufacturer and product number) of the flying object DR, external information (shape, size, weight, number of propellers and location), payload information (camera, battery, etc.), flight performance (maximum ascent speed, maximum descent speed, maximum flight speed, maximum flight time, operating environment temperature, GPS mode, hovering accuracy, maximum wind resistance, cruising distance, etc.), and radio wave characteristics. The performance data c indicating radio wave characteristics includes a classification table c2 indicating the model of the flying object DR for each communication method, and an evaluation table c3 indicating the risk evaluation value for each model.

[0026] The flying object image data database 33 is a database that stores images of flying objects DR for each model (manufacturer and product number) of the flying object DR. Even if only images provided by the manufacturer are stored initially, it is possible to later store image data captured by the imaging unit 44.

[0027] The performance data c can be acquired, for example, from a drone manufacturer via an external recording medium 13 such as a memory card or a communication network (not shown). When the performance data c is acquired from the external recording medium 13, it is written and stored in the performance data database 32 via the recording medium reading unit 14. On the other hand, when the performance data c is acquired via a communication network (not shown), it is received by the communication unit 15 and written and stored in the performance data database 32.

[0028] The risk determination criteria data database 34 stores a table d1 showing an evaluation value for the distance from the current position and a table d2 showing an evaluation value for the remaining time, which are used when determining the risk in the risk determination program 26.

[0029] The countermeasure related database 35 stores in advance countermeasures T1 (for example, transmitting noise radio waves, firing a net capture gun, deploying a capture drone, etc.) that can be taken depending on the degree of danger.

[0030] A radar 40, an RF sensor 42, an imaging unit 44 such as a camera, a sound source sensor 46 such as a microphone, etc. are provided outside the airborne object monitoring system 10. Note that the airborne object monitoring system 10 may incorporate the radar 40, the RF sensor 42, the imaging unit 44, the sound source sensor 46, etc.

[0031] The radar 40 can detect a flying object DR such as a drone. Furthermore, when the radar 40 detects a flying object DR, it acquires flight information e of the flying object DR (including, for example, coordinate information e1, altitude e2, vector information (azimuth, speed) e3, track information e4, direction e5, acceleration e6, flight distance e7, time information e8, etc. of the flying object DR) and outputs it to the communication unit 15.

[0032] The RF sensor 42 emits radio waves toward the flying object DR such as a drone, acquires radio wave parameters f of the reflected wave, such as the center frequency f1, bandwidth f2, power f3, arrival direction f4, and time information f5, and outputs the acquired data to the communication unit 15. The RF sensor 42 can only acquire the radio wave parameters f of a flying object DR that is relatively close, but the radar 40 can also acquire flight information e of a flying object DR that is farther away than the RF sensor 42. Therefore, it is desirable to use the radar 40 when acquiring flight information e of a flying object DR that is far away, and the RF sensor 42 when acquiring flight information e of a flying object DR that is close by, for example.

[0033] The imaging unit 44 is, for example, a camera, captures an image of a flying object DR such as a drone, and outputs to the communication unit 15 image data g to which time information g1 indicating the time at which the image was captured is attached.

[0034] The sound source sensor 46 is, for example, a microphone, which collects sounds including propeller sounds and engine sounds of an air vehicle DR such as a drone, and outputs the sound data h to the communication unit 15, accompanied by time information h1 indicating the time the sound was collected.

[0035] The communication unit 15 receives flight information e, radio wave specifications f, image data g, and sound data h, and outputs them to the memory 20.

[0036] The flight determination program 21 determines whether the flying object DR is flying in a predetermined air corridor from the flight information e. The predetermined air corridor may be determined by, for example, an Unmanned Traffic Management (UTM) system to safely manage drone flights in low-altitude airspace.

[0037] An air corridor is a limited air route that is set up so that the flying object DR can enter and exit without being obstructed. Therefore, it can be understood as a virtual tunnel set up in the air. The location of the entrance and exit of this tunnel and its size (length) can be set arbitrarily, for example, depending on the type of flying object DR, or variably set depending on the date and time. There may also be air corridors with only an entrance and exit set up. For such air corridors, the flight determination program 21 determines whether the flying object DR has passed the entrance and exit of the air corridor in this order.

[0038] Furthermore, there may be air corridors that have checkpoints set along the way in addition to entrances and exits. For such air corridors, the flight determination program 21 determines whether the projectile DR has passed through the entrance, checkpoint (if there are multiple checkpoints, checkpoints 1, 2, 3, etc.), and exit of the air corridor in that order.

[0039] Furthermore, the number of preset air corridors is not limited to one, and there may be multiple air corridors. In this case, the flight determination program 21 determines whether or not all of the preset air corridors have been passed through. Therefore, the airborne object monitoring system 10 acquires information about the air corridor (hereinafter referred to as "air corridor information b") from the UTM system using, for example, a dedicated sensor (not shown), and stores the acquired air corridor information b in the air corridor information database 31.

[0040] For the relationship between the UTM system and air corridors, please refer to Non-Patent Document 1.

[0041] The air corridor information b can be acquired from an external recording medium 13 such as a memory card via the recording medium reading unit 14, or can be acquired by the communication unit 15 receiving it from the UTM system via a communication network (not shown). The acquired air corridor information b is written and stored in the air corridor information database 31.

[0042] The flight determination program 21 determines whether the flying object DR is flying in an air corridor by comparing the flight information e with the air corridor information b stored in the air corridor information database 31. In addition, the program 21 outputs coordinate information e1 included in the flight information e and radio wave specifications f from the RF sensor 42.

[0043] The flying object classification program 22 classifies the flying object DR into a normal flying object (hereinafter referred to as a "normal flying object") DRn or an abnormal flying object (hereinafter referred to as an "abnormal flying object") DRi based on the determination result by the flight determination program 21 and the remote ID from the UTM. Specifically, the flying object DR determined to be flying in the air corridor specified for the remote ID is classified as a normal flying object DRn. On the other hand, the flying object DR not determined to be flying in the air corridor specified for the remote ID is classified as an abnormal flying object DRi. The classification result i1 is then output together with vector information e3 of the flying object DR.

[0044] When the result i1 from the flying object classification program 22 indicates an abnormal flying object DRi, the model identification program 23 identifies the model of the abnormal flying object DRi based on at least one of the flight information e, radio wave specifications f, image data g, sound data h, and remote ID, and outputs the identification result i2. The model identification program 23 identifies the model by, for example, comparing at least one of the track information e4 and maneuvering performance (speed, acceleration, flight distance, etc.) included in the flight information e acquired about the abnormal flying object DRi, the characteristics of the mounting base material obtained from the radio wave specifications f, the surface shape (number and appearance of propellers) and surface features (written characters, illustrations, coloring) shown in the image data g, and the propeller sound included in the sound data h with the performance data c, and outputs the result i2 and the track information e4 of the abnormal flying object DRi.

[0045] In addition, even if the result i1 from the missile classification program 22 is a normal missile DRn, the missile model identification program 23 can similarly identify the missile model and output the result i2 and flight path information e4.

[0046] The destination prediction program 24 predicts the destination n of the abnormal flying object DRi based on the flight information e. Specifically, the destination prediction program 24 predicts the destination n of the abnormal flying object DRi from the vector information (direction, speed) e3 and the track information e4 included in the flight information e.

[0047] The destination prediction program 24 can predict the destination n of the normal flying object DRn from the vector information (direction, speed) e3 and the flight track information e4 included in the flight information e.

[0048] The purpose estimation program 25 estimates the purpose p of the abnormal flying object DRi based on at least the destination n, and preferably also on the image data g. Examples of the purpose p include destroying the destination n, photographing the destination n, and transporting cargo to the destination n. For this estimation, the purpose estimation program 25 refers to the image data g. If the image data g includes an image of the abnormal flying object DRi carrying explosives, the purpose estimation program 25 estimates that the purpose p is the destruction of the destination n. Furthermore, if the image data g includes an image of the abnormal flying object DRi equipped with a camera, the purpose estimation program 25 estimates that the purpose p is the photography of the destination n. Furthermore, if the image data g includes an image of the abnormal flying object DRi carrying cargo, the purpose estimation program 25 estimates that the purpose p is the transportation of cargo to the destination n. These estimation results are not limited to a single result, but can also be any combination of results. For example, if the image data g includes an image of the abnormal flying object DRi equipped with a camera and carrying explosives, the purpose estimation program 25 estimates that the purpose p is the destruction and photography of the destination n.

[0049] The purpose estimation program 25 can also estimate the purpose p of a normal flying object DRn based on at least the destination n, and preferably also on image data g.

[0050] The danger level determination program 26 determines the danger level of the abnormal flying object DRi. Specifically, the danger level determination program 26 first analyzes the behavior pattern of the abnormal flying object DRi based on at least one of the type of aircraft identified by the aircraft type identification program 23, the payload (type, size) of the abnormal flying object DRi obtained from the image data g, the protection strength of the destination n (robustness, bodyguard system, etc.), the distance of the abnormal flying object DRi to a no-entry area (also called a "keep out range") including the destination n (for example, if the destination n is an airport, the no-entry area is vast), the number of abnormal flying objects DRi (for example, whether it is a single aircraft or multiple aircraft (for example, a swarm)), and the current weather and meteorological conditions, and then quantifies the danger level, which is the level of danger of the abnormal flying object DRi.

[0051] Specifically, the risk assessment program 26 refers to the flight information e of the abnormal flying object DRi, and analyzes the behavioral pattern of the abnormal flying object DRi based on the vector information (direction, speed) e3, flight track information e4, and flight distance e7 included in the flight information e. The behavioral patterns are classified into, for example, charge, crossing the border, and wandering.

[0052] FIG. 3A is a diagram for explaining a case where the behavior pattern of the flying object is a charge.

[0053] FIG. 3B is a diagram for explaining a case where the behavior pattern of the flying object is border crossing.

[0054] FIG. 3C is a diagram for explaining a case where the behavior pattern of the flying object is wandering.

[0055] As illustrated in FIG. 3A, when an abnormal flying object DRi is flying in a straight line toward a destination n, the behavior pattern of this abnormal flying object DRi is determined to be a charge.

[0056] Also, as illustrated in Figure 3B, if an abnormal flying object DRi is attempting to fly across a boundary line BD such as a national border or an airport area boundary, the behavior pattern of this abnormal flying object DRi is determined to be border crossing.

[0057] Furthermore, as illustrated in FIG. 3C, if an abnormal flying object DRi flies while repeatedly entering an area ER and retreating from the area ER, the behavior pattern of this abnormal flying object DRi is determined to be wandering.

[0058] In addition, if the abnormal flying object DRi crosses the boundary line BD and flies in a straight line toward the destination n, the behavior pattern of this abnormal flying object DRi is determined to be crossing the boundary line and charging, and if the abnormal flying object DRi flies while crossing and returning to the boundary line BD, the behavior pattern of this abnormal flying object DRi is determined to be crossing the boundary line and wandering.The behavior pattern of this abnormal flying object DRi can also be determined as any combination of charging, crossing the boundary line, and wandering.

[0059] Next, the risk assessment program 26 quantifies the risk, which is the level of risk.

[0060] FIG. 4 is a diagram showing an example of a risk quantification flow according to the risk assessment program.

[0061] In the example shown in Figure 4, the risk assessment program 26 individually evaluates the risk using the radio wave parameters f from the RF sensor 42, the flight information e from the radar 40, and the image data g from the imaging unit 44, and calculates the risk by averaging these evaluation values.

[0062] First, an example of risk assessment using the radio wave parameters f obtained from the RF sensor 42 will be described.

[0063] In the example shown in FIG. 4, the risk assessment program 26 obtains a power intensity distribution f1 with respect to frequency based on the radio wave specifications f, and estimates the communication method (e.g., VHF, UHF, etc.) of the abnormal flying object DRi. The performance data database 32 also stores in advance a classification table c2 indicating the type of flying object DR corresponding to each communication method, and an evaluation table c3 indicating evaluation information for each model. In the example shown in FIG. 4, the evaluation table c3 indicates that the flying object DR of the model "RO2" has the lowest evaluation value of "1," and the flying object DR of the model "DO3" has the highest evaluation value of "5." The risk assessment program 26 determines the evaluation value by referring to the evaluation table c3 from the model identified by the model identification program 23.

[0064] Next, an example of risk assessment using flight information e obtained from the radar 40 will be described.

[0065] 4, the risk assessment program 26 obtains trajectory information e4 for the current position GI of the abnormal flying object DRi based on the flight information e. The risk assessment program 26 further calculates the distance and remaining time until the abnormal flying object DRi reaches the current position GI from the vector-resolved relative velocity based on the flight information e. Then, the risk assessment program 26 determines an evaluation value for the calculated distance from table d1 stored in the risk assessment criteria data database 34. The risk assessment program 26 also determines an evaluation value for the calculated remaining time from table d2 stored in the risk assessment criteria data database 34.

[0066] In the example shown in Fig. 4, table d1 specifies that the evaluation value is the lowest "1" when the distance is 5 km, and increases by "1" for every 1 km decrease in distance, reaching the highest evaluation value of "5" when the distance is 1 km. Table d2 specifies that the evaluation value is the lowest "1" when the remaining time is 5 minutes, and increases by "1" for every minute the remaining time decreases, reaching the highest evaluation value of "5" when the remaining time is 1 minute. Therefore, the risk level determination program 26 determines the evaluation value according to the distance to the current position GI and the remaining time.

[0067] Next, an example of risk assessment using image data g obtained from the imaging unit 44 will be described.

[0068] 4, the risk determination program 26 uses the above-mentioned evaluation table c3 to determine an evaluation value according to the model identified by the model identification program 23. In this way, the risk determination program 26 determines an evaluation value according to the model of the abnormal flying object DRi based on the image data g.

[0069] In this way, the danger level judgment program 26 can determine the evaluation value based on the radio wave specifications f, flight information e, and image data g of the abnormal flying object DRi.

[0070] The risk assessment program 26 calculates the risk by averaging these evaluation values.

[0071] 5 and 6 are diagrams for explaining examples of calculation of the risk level.

[0072] 5 shows the risk level calculated when the model is determined to be "DO2," the distance to the current position GI is 2 km, and the remaining time is 1 minute. When the distance to the current position GI is 2 km, the evaluation value is "4" according to table d1, when the remaining time is 1 minute, the evaluation value is "5" according to table d2, and when the model is "DO2," the evaluation value is "4" according to evaluation table c3, so the risk level is "4.33," which is the average of these three evaluation values.

[0073] On the other hand, the example shown in Fig. 6 shows the risk level calculated when the model is determined to be "RO3," the distance to the current position GI is 2 km, and the remaining time is 5 minutes. When the distance to the current position GI is 2 km, the evaluation value is "4" according to table d1, when the remaining time is 5 minutes, the evaluation value is "1" according to table d2, and when the model is "RO3," the evaluation value is "2" according to evaluation table c3. Therefore, the risk level is "2.33" by averaging these three evaluation values, which is lower than the case shown in Fig. 5.

[0074] The countermeasure presentation program 27 selects countermeasures (hereinafter referred to as "candidate countermeasures") TS that can be taken according to the risk level from among a plurality of countermeasures TS that are prepared in advance and stored in the countermeasure related database 35. can The user can then decide and present the proposed candidate countermeasures TS can The measures to be actually applied final It is possible to decide on the measures to be actually applied. final The number may be singular or plural. A specific example will be described with reference to FIG.

[0075] FIG. 7 is a simplified flow diagram showing an example of a countermeasure decision process using the countermeasure suggestion program.

[0076] The countermeasure presentation program 27, for example, as shown in FIG. 7, selects candidate countermeasure TSs from among a plurality of countermeasure TSs prepared in advance and stored in a countermeasure related database 35, based on the degree of risk. can Determine the candidate countermeasures TS can Furthermore, based on the remaining time NJ to the destination n and the size and number of the abnormal projectiles DRi, these multiple candidate countermeasures TS can From the list, the acceptable candidate countermeasures TS allow Furthermore, based on the contents of the payload (e.g., explosives, simple luggage, or a camera) and the surrounding environment SK of the destination n (e.g., urban area, non-urban area (wilderness), power plant, infrastructure facility, sea, etc.), the acceptable candidate countermeasures TS allow Available workarounds from TS avail Finally, taking into consideration meteorological information KJ such as weather (sunny, rainy, weak wind, strong wind, fog, snow), etc., countermeasure TS avail Countermeasures TS final Decide and present the solution. final is not necessarily singular, but may be plural.

[0077] The countermeasure presentation program 27 presents the decided countermeasure TS final If there are multiple TSs, multiple countermeasures will be applied. final The countermeasure suggestion program 27 determines the priority order for each of the countermeasures in descending order of preference, and presents a plurality of countermeasures in descending order of priority. Based on this presentation, the operator can easily decide which countermeasure to take, and can quickly instruct the countermeasure suggestion program 28 on which countermeasure to take. Note that the flow shown in Fig. 7 is an example, and the countermeasure suggestion program 27 can also determine the countermeasure according to a different flow.

[0078] FIG. 8 is a detailed flow chart showing another example of a countermeasure decision process by the countermeasure suggestion program.

[0079] The countermeasure related database 35 stores a table T1 that lists countermeasures TS that can be taken, such as transmitting noise radio waves, deploying a ramming drone, and using a rifle.

[0080] In the example shown in FIG. 8, the countermeasure suggestion program 27 first selects candidate countermeasures TS from the countermeasures TS shown in the table T1 by taking into account the current weather information KJ. can is selected (S1).

[0081] Table T2 contains the candidate countermeasures TS selected in step S1. can In Table T1, noise radio wave transmission performed by Group A, ramming drone sorties performed by Groups B, C, and D, and the use of rifles performed by Groups E and F were available, but after taking into account the current weather information KJ, it was determined that the use of rifles by Group G was not possible, so Table T2 excludes the use of rifles by Group G compared to Table T1.

[0082] After step S1, the countermeasure suggestion program 27 presents the selected candidate countermeasures TS can Considering the drone information DJ, the selected candidate countermeasures TS can From the accepted countermeasures TS allow (S2) The drone information DJ is information that combines the scale, quantity, and payload shown in Figure 7.

[0083] In this example, it is assumed that, after taking into consideration the drone information DJ, the model of the abnormal flying object DRi is "DO3."

[0084] The countermeasure related database 35 stores a table T3 that lists countermeasures TS that can be taken for each model. The countermeasure presentation program 27 refers to table T3, and since the countermeasures TS applicable to model "DO3" are noise radio wave transmission and the use of a rifle, as shown in table T4, as a result of the processing of step S2, "ramming drone" is excluded from table T2, and as shown in table T4, noise radio waves by group A and rifles by groups E and F are listed as available countermeasures TS. avail It remains as (S2).

[0085] The countermeasure suggestion program 27 further considers the surrounding environment SK, and if it is possible to shoot down the target (S3: Yes), it proposes a final countermeasure TS final The use of rifles is presented (S4). The example shown in Table T5 indicates that rifles can be used by Group F. This means that although Table T4 indicated that rifles could be used not only by Group F but also by Group E, the use of rifles by Group E was excluded after taking into account the surrounding environment SK.

[0086] On the other hand, if it is not possible to shoot down the enemy in step S3 (S3: No), the use of rifles is excluded from the candidate countermeasures shown in table T4, and the final countermeasure TS final As a result, noise radio wave transmission by group A is presented (S5).

[0087] The countermeasure instruction program 28 outputs the final countermeasure TS thus determined. final The corresponding parts of the noise radio wave transmitter 50, the net capture gun 52, the capture drone dispatch device 54, and the security guard KB are instructed to implement the above. final will be implemented.

[0088] Next, an example of the operation of the flying object monitoring system to which the flying object monitoring method according to the embodiment of the present invention configured as described above is applied will be described with reference to FIG.

[0089] FIG. 9 is a diagram illustrating an example of monitoring control of an assault drone by an airborne object monitoring system.

[0090] The graph shown at the top of Figure 9 has the horizontal axis representing time t (minutes) and the vertical axis representing the distance r (km) to destination n. The straight line L shown in the figure indicates the distance r (km) to destination n of the approaching drone (assault drone) DR at a constant speed of 1 km per minute versus time t (minutes).

[0091] This assault-type drone DR is first detected by the radar 40 at a location r1 that can reach the destination n in 6 minutes, and within 10 seconds after that point (t1), the radar 40 acquires flight information e, which is then compared with the air corridor information b by the flight determination program 21. Here, it is assumed that the comparison result does not determine that the drone is flying in an air corridor. As a result, this assault-type drone DR is classified as an abnormal air corridor DRi by the air corridor classification program 22 (ST1).

[0092] The radar 40 continues tracking the abnormal flying object DRi (ST2). Meanwhile, the flying object monitoring system 10 further performs the following operations: model identification program 23, destination prediction program 24, purpose estimation program 25, risk assessment program 26, and countermeasure presentation program 27 identify the model of the abnormal flying object DRi, predict its destination, estimate its purpose, assess its risk, and present proposed countermeasures. The user then decides which of the presented countermeasures to implement (ST3). The processing of this step ST3 is performed, for example, within 35 seconds. In FIG. 9, the model identification program 23, destination prediction program 24, purpose estimation program 25, risk assessment program 26, and countermeasure presentation program 27 are collectively referred to as the drone countermeasure program.

[0093] In step ST3, the final countermeasure TS finalWhen it is determined that a ramming drone will be carried out as a countermeasure, the countermeasure instruction program 28 immediately sends that instruction to the capture drone sortie device 54. As a result, the capture drone sortie device 54 dispatches the ramming drone as soon as it is ready (ST4). In one example, in step ST4, preparations for the ramming drone sortie are made over a 35-second period, and at time t2, the ramming drone is dispatched so as to come closest to the abnormal flying object DRi 150 seconds later (ST5). In response to this, the drone countermeasure program is also displayed and updated (ST6).

[0094] During this time, the abnormal flying object DRi continues to fly toward the destination n at a constant speed of 1 km per minute, and at time t3, it is detected by the RF sensor 42, which transmits radio wave specifications f of the abnormal flying object DRi. The drone countermeasure program performs waveform analysis (see f1 in FIG. 4), identification, risk assessment, etc. based on the radio wave specifications f from the RF sensor 42, and outputs the results (ST7). In one example, these processes in step ST7 are performed for 60 seconds.

[0095] While ST7 is being performed, the scheduled time t4 for the ramming drone to come closest to the anomalous flying object DRi arrives. When the scheduled time t4 arrives, the ramming drone releases a capture net and attempts to capture the anomalous flying object DRi.

[0096] When the abnormal flying object DRi is captured in this way, it is momentarily not detected by the radar 40. Therefore, by observing the absence of flight information e from the radar 40, it is possible to confirm the effect of the ramming drone, i.e., the successful capture of the abnormal flying object DRi. On the other hand, if the capture by the ramming drone fails, the flight information e from the radar 40 continues to be observed, and therefore, if there is no absence of flight information e from the radar 40, it can be recognized that the capture by the ramming drone has failed (ST8).

[0097] In step ST8, regardless of whether the ramming drone succeeds or fails to capture the abnormal flying object DRi, the radar 40 tracks the abnormal flying object DRi. Continue (ST9).

[0098] In the following, the explanation will be continued assuming that capture by the ramming drone failed in step ST8.

[0099] Data detected by the radar 40, RF sensor 42, imaging unit 44, and sound source sensor 46, as well as sighting information MJ, etc. are constantly sent to the airborne object monitoring system 10. In response to this, as described above, the programs 21 to 26 operate, and the countermeasure suggestion program 27 presents the most appropriate countermeasure TS at that time in accordance with the situation, which changes from moment to moment. final is presented in real time.

[0100] Therefore, the operator can immediately decide on the next course of action depending on the change in the situation.

[0101] In step ST8, if the abnormal flying object DRi is successfully captured, the response is completed. However, if it is not successful, the countermeasure suggestion program 27 accordingly suggests an appropriate countermeasure TS at that time. final Here, the countermeasure TS final Assume that the options of sending out a second ramming drone (second wave) and transmitting noise radio waves are presented, and in response, the operator instructs the countermeasure instruction program 28 to send out both a second ramming drone (second wave) and transmit noise radio waves.

[0102] In this specification, this second ramming drone is also referred to as the "second wave," and correspondingly, the first ramming drone dispatched in step ST5 is also referred to as the "first wave."

[0103] In response to the command to dispatch the second wave, the capture drone dispatch device 54 prepares for the dispatch of the second wave during the 30 seconds after successful capture is not confirmed in step ST8 (ST10), and dispatches the second wave at time t5 so that it will come closest to the abnormal flying object DRi at time t6, 60 seconds later (ST11).

[0104] Furthermore, during this time, in response to the noise radio wave transmission instruction, the noise radio wave device 50 prepares to transmit noise radio waves (ST12), and at time t6, transmits noise radio waves for 30 seconds (ST13). Time t6 is also the scheduled time when the second wave of ramming drones will come closest to the abnormal flying object DRi. When the scheduled time of closest approach arrives, the second wave of ramming drones will also release a capture net and attempt to capture the abnormal flying object DRi.

[0105] When the anomalous flying object DRi is captured in this way, it is momentarily not detected by the radar 40 and the RF sensor 42. Therefore, it is possible to confirm whether or not the anomalous flying object DRi has been successfully captured by observing the outputs from the radar 40 and the RF sensor 42. In this example, it takes 10 seconds to make this confirmation by observing the output from the radar 40 (ST14), and it takes 30 seconds to make this confirmation by observing the output from the RF sensor 42 (ST15).

[0106] If the effect of the noise radio waves can affect the flight of the abnormal flying object DRi, the abnormal flying object DRi will momentarily go undetected by the radar 40. Therefore, after step ST14, the effect of the noise radio waves transmitted in step ST13 can be confirmed by observing the output from the radar 40 (ST16). In this example, this confirmation by observing the output from the radar 40 takes 20 seconds.

[0107] In this way, according to the airborne object monitoring system to which the airborne object monitoring method of an embodiment of the present invention is applied, abnormal airborne objects DRi such as drones operated maliciously, drones that have become uncontrollable due to a malfunction, or drones behaving unintentionally by an inexperienced operator can be judged, their model identified, their destination n predicted, their purpose estimated, and their level of danger judged in accordance with the ever-changing situation, and appropriate countermeasures can be presented in real time at all times depending on the results.Therefore, the operator can always take the most appropriate countermeasures to monitor and control the abnormal airborne object DRi, thereby reducing the risk posed by the abnormal airborne object DRi as much as possible.

[0108] In addition, since it is possible to store image data g of the flying object DR captured by the imaging unit 44 in the flying object image data database 33, the learning effect regarding model identification can be improved by storing image data g of flying objects DR for which images are not provided by the manufacturer or flying objects DR that have been manufactured in secret.

[0109] In the above example, the radar 40, RF sensor 42, imaging unit 44, sound source sensor 46, sighting information MJ, and remote ID were described as examples of input IN for acquiring data related to the flying object DR, but an flying object monitoring system to which the flying object monitoring method of the embodiment of the present invention is applied can be realized even if only one of these is provided as the input IN. Furthermore, an flying object monitoring system to which the flying object monitoring method of the embodiment of the present invention is applied can be realized by adding any other sensing means in addition to these input IN and using them in a free combination.

[0110] Similarly, in the above example, the noise radio wave transmitter 50, the net capture gun 52, the capture drone dispatch device 54, etc. were described as examples of the output OUT for controlling the abnormal flying object DRi, but an flying object monitoring system to which the flying object monitoring method of the embodiment of the present invention is applied can be realized even if only one of these is provided as the output OUT. Furthermore, an flying object monitoring system to which the flying object monitoring method of the embodiment of the present invention is applied can be realized by adding any other control means in addition to these outputs OUT and using them in free combination.

[0111] As described above, according to this embodiment, it is possible to provide a highly versatile airborne object monitoring system, airborne object monitoring method, and program.

[0112] Although several 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 within the scope and spirit of the invention, as well as within the scope of the invention described in the claims and their equivalents. [Explanation of symbols]

[0113] 10. Airborne object monitoring system, 11. Bus, 12. CPU, 13. External recording medium, 14. Recording medium reading unit, 15. Communication unit, 20. Memory, 21. Flight judgment program, 22. Airborne object classification program, 23. Model identification program, 24. Destination prediction program, 25. Purpose estimation program, 26. Risk judgment program, 27. Countermeasure presentation program, 28. Countermeasure instruction program, 29. Writable data area, 30. Storage device, 31. Air corridor information database, 32. Performance data database, 33. Airborne object image data database, 34. Risk judgment criteria data database, 35. Countermeasure related database, 40. Radar, 42. RF sensor, 44. Imaging unit, 46. Sound source sensor, 50. Noise radio wave transmitter, 52. Net capture gun, 54. Capture drone sortie device

Claims

1. a receiving unit that receives flight information of the flying object from a radar that detects the flying object, receives radio wave specifications of the reflected waves of the radio wave from an RF sensor that radiates radio waves toward the flying object, receives image data of the flying object captured by an imaging unit that captures an image of the flying object, and receives sound data including the propeller sound of the flying object from a sound source sensor that collects sounds including the propeller sound; a first determination unit that determines whether the flying object is flying a predetermined limited air route by comparing the flight information with information on the limited air route that has been prepared in advance; a classification unit that classifies a flying object that is determined by the first determination unit to be flying along the restricted air route as a normal flying object, and a flying object that is not determined to be flying along the restricted air route as an abnormal flying object; a performance database that stores performance data indicating external shape information, payload information, flight performance, and radio wave characteristics for each model of the flying object; an identification unit that identifies the model of the flying object by comparing at least one of the flight track information and maneuvering performance included in the flight information, the characteristics of the mounting base material obtained from the radio wave specifications, the surface shape and surface characteristics of the flying object obtained from the image data, and the propeller sound with the performance data stored in the performance database; and a prediction unit that predicts the destination of the flying object from the vector information and trajectory information included in the flight information.

2. The airborne object monitoring system according to claim 1 , wherein the vector information includes a direction and a speed of the airborne object.

3. 3. The airborne object monitoring system according to claim 1, further comprising an estimation unit that estimates a purpose of the airborne object based on the destination and cargo or payload of the airborne object obtained from the image data.

4. 4. The flying object monitoring system according to claim 3, wherein the purpose is at least one of destroying the destination, photographing the destination, and transporting cargo to the destination.

5. The flying object monitoring system according to claim 1 , further comprising a second determination unit that determines a degree of danger of the abnormal flying object.

6. 6. The airborne object monitoring system of claim 5, wherein the second determination unit analyzes the behavioral pattern of the airborne object based on at least one of the model identified by the identification unit, the number of the airborne objects obtained from the image data, the payload, the protection strength of the destination, the distance to a no-entry area including the destination, and the current weather and meteorological conditions, and then performs a risk assessment using the radio wave specifications, the flight information, and the image data individually, and calculates the risk by averaging each of these assessment values.

7. The airborne object monitoring system of claim 6, wherein the second determination unit further refers to the flight information and analyzes the behavior pattern of the airborne object based on vector information, trajectory information, and flight distance contained in the flight information.

8. 8. The flying object monitoring system according to claim 6, further comprising a presentation unit that determines and presents a countermeasure that can be taken according to the degree of danger from among a plurality of countermeasures prepared in advance.

9. The airborne object monitoring system according to claim 1 , wherein the airborne object is a drone.

10. For each type of aircraft, performance data indicating external shape information, payload information, flight performance, and radio wave characteristics is stored in a performance database; A radar detects a flying object and acquires flight information of the detected flying object; an RF sensor that emits radio waves toward the flying object and acquires radio wave parameters of the reflected waves of the radio waves; an imaging unit that images the flying object; a sound source sensor collecting sounds including the propeller sound of the flying object; The processor: determining whether the detected flying object is flying along a predetermined restricted air route by comparing the flight information with information on the restricted air route that has been prepared in advance; classifying a flying object determined to be flying along the restricted air route as a normal flying object and a flying object not determined to be flying along the restricted air route as an abnormal flying object; Identifying the model of the detected flying object by comparing at least one of the flight path information and maneuvering performance included in the flight information, the characteristics of the mounting base material obtained from the radio wave specifications, the surface shape and surface characteristics obtained from image data of the flying object, and the propeller sound with the performance data stored in the performance database; A method for monitoring flying objects, which predicts the destination of the detected flying object from vector information and trajectory information included in the flight information.

11. Computer, a receiving unit that receives flight information of the flying object from a radar that detected the flying object, receives radio wave specifications of the reflected waves of the radio wave from an RF sensor that emitted a radio wave toward the flying object, receives image data of the flying object captured by an imaging unit that captured an image of the flying object, and receives sound data including the propeller sound of the flying object from a sound source sensor that collected a sound including the propeller sound; a first determination unit that determines whether the flying object is flying a predetermined limited air route by comparing the flight information with information on the limited air route that has been prepared in advance; a classification unit that classifies a flying object determined by the first determination unit to be flying through the limited air route as a normal flying object and a flying object not determined to be flying through the limited air route as an abnormal flying object; an identification unit that identifies the model of the flying object by comparing at least one of the flight track information and maneuvering performance included in the flight information, the characteristics of the mounting base material obtained from the radio wave specifications, the surface shape and surface characteristics of the flying object obtained from the image data, and the propeller sound with performance data for each model of the flying object stored in a performance database in advance; a prediction unit that predicts a destination of the flying object based on vector information and track information included in the flight information; A program to function as a

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