Unmanned aerial vehicle (UAV) countermeasure system, central unit, and risk assessment method

Abstract

To provide an unmanned aerial vehicle (UAV) countermeasure system capable of analyzing the degree of danger posed by an aircraft based on information acquired by radio wave sensors. [Solution] The unmanned aerial vehicle (UAV) response system comprises a radio wave sensor that obtains sensing data including signal strength and direction of arrival of radio waves from an aircraft, a database that registers the specifications of radio waves emitted from each UAV, which have been acquired in advance for each type of UAV, and a central unit. The central unit comprises a data acquisition unit, a data analysis unit, an identification unit, and a risk analysis unit. The data acquisition unit acquires sensing data from the radio wave sensor. The data analysis unit analyzes the sensing data and calculates the specifications of the captured radio waves. The identification unit accesses the database and identifies the UAV corresponding to the calculated specifications. The risk analysis unit analyzes the risk level of the identified UAV based on the signal strength and direction of arrival.

Description

Technical Field

[0001] Embodiments of the present invention relate to an unmanned aircraft countermeasure system, a central device, and a risk analysis method.

Background Art

[0002] An unmanned aerial vehicle (UAV) is an object capable of moving in the air by wireless control or autonomously, and so-called drones also fall within this category. Hereinafter, this type of moving body is collectively referred to as an aircraft. In recent years, drone technology has developed remarkably and has come to be used in a wide range of fields such as disaster situation grasping and infrastructure inspection. On the other hand, malicious use such as incidents where runways are closed for a long time due to intrusion into airports, disruption of operations due to intrusion into power plants, or terrorist acts targeting important persons has become a major problem. In addition, drones that have become uncontrollable due to malfunctions, etc., and drones that behave unintentionally by inexperienced operators are also problems. Therefore, there is increasing interest in technology (counter-drone technology) for early detection and countermeasures against harmful drones.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Technologies for early detection of approaching drones are attracting attention. There are multiple methods for detecting drones from a distance, including active radar, passive radar (radio wave sensors), image sensors, and acoustic sensors. By organically combining the information acquired by each sensor, it is possible to plan and implement countermeasures to minimize damage to our side and eliminate the danger posed by drones.

[0005] Connecting multiple sensors to obtain useful information is just as important as enhancing the capabilities of individual sensors. At the same time, it is also necessary to be able to accurately analyze the hazard level of an aircraft and devise effective countermeasures, even if information can only be obtained from a single sensor.

[0006] Therefore, the objective is to provide an unmanned aerial vehicle (UAV) countermeasure system, a central unit, and a risk analysis method that can analyze the degree of danger posed by an aircraft based on information acquired by radio wave sensors. [Means for solving the problem]

[0007] According to the embodiment, the unmanned aerial vehicle (UAV) countermeasure system comprises a radio wave sensor that captures radio waves emitted from an aircraft and obtains sensing data including at least the signal strength and direction of arrival of the radio waves; a database that registers the specifications of radio waves emitted from each UAV, which have been acquired in advance for each type of UAV; and a central unit. The central unit comprises a data acquisition unit, a data analysis unit, an identification unit, and a risk analysis unit. The data acquisition unit acquires sensing data from the radio wave sensor. The data analysis unit analyzes the sensing data and calculates the specifications of the captured radio waves. The identification unit accesses the database and identifies the UAV corresponding to the calculated specifications. The risk analysis unit analyzes the risk level of the identified UAV based on the signal strength and direction of arrival. [Brief explanation of the drawing]

[0008] [Figure 1] Figure 1 shows an example of the application of the unmanned aerial vehicle (UAV) countermeasure system according to the embodiment. [Figure 2] Figure 2 is a block diagram showing an example of an unmanned aerial vehicle (UAV) countermeasure system according to the embodiment. [Figure 3] Figure 3 is a functional block diagram showing an example of the central server 2 shown in Figure 2. [Figure 4] Figure 4 shows an example of the information registered in database 6. [Figure 5] Figure 5 shows an example of a target direction shown on a two-dimensional map. [Figure 6] Figure 6 is a flowchart showing an example of the processing procedure for the central server 2. [Figure 7] Figure 7 shows the relationships between the various quantities involved in calculating the distance of the target drone. [Figure 8] Figure 8 is a graph showing an example of how signal strength changes with respect to distance from the radio wave sensor. [Figure 9] Figure 9 is a graph showing an example of the expected signal strength of radio waves from a drone approaching a radio wave sensor. [Figure 10] Figure 10 shows an example of an interval for taking the average value of the signal strength. [Modes for carrying out the invention]

[0009] Figure 1 shows an example of the application of an unmanned aerial vehicle (UAV) response system according to the embodiment. In this embodiment, a so-called drone is assumed as the UAV, and the response to a target drone arriving in the monitored area is described. The basic configuration, operation, and effects are the same for targets such as manned aircraft and ships.

[0010] The system shown in Figure 1 is deployed, for example, on land close to the coastline and forms a monitoring area toward the horizon to capture targets flying in the air and approaching from across the sea. This system includes a center 1, a radio wave sensor 12 that captures radio waves coming from the target and acquires sensing data, and a group of assets (jammer 3, capture drone 4, launcher 5, etc.) as means of dealing with unmanned aerial vehicles. The radio wave sensor 12 and the group of assets are connected to the center 1 in a communicative manner via a network 10 which serves as a data link. The network 10 may be wired or wireless.

[0011] Jammer 3, capture drone 4, and launcher 5 each possess the following characteristics and are classified as different types of assets.

[0012] <Jammer> Jammer 3 is equipped with the ability to emit jamming radio waves at specific frequencies, disrupting the target drone 30 with radio waves. For example, it has a jamming range that extends 500m in radius from the antenna, forcing the target drone 30 to retreat or to make a soft landing through jamming. If the target drone 30 uses radio waves in the 2.4GHz and 5GHz bands, Jammer 3 will also emit radio waves in the 2.4GHz and 5GHz bands accordingly. Incidentally, most commercially available drones use radio waves in these bands.

[0013] <Capture Drone> The capture drone 4 is equipped with a capture net 41 and other components, and captures the target drone 30 and transports it to our side. The capture drone 4 autonomously flies toward the approaching target drone 30 and captures the target drone 30 at the encounter point (rendezvous point) or when it enters a certain area. If the target drone 30 is flying at an average speed of 60 km / h, it is preferable that the capture drone 4 has a flight speed of about 90 km / h.

[0014] <Firearm (Rifle)> The launcher 5 fires projectiles such as bullets and flying objects towards the target drone 30 and physically destroys it. If it is a continuous firing device, multiple firings are possible within the range. It is desirable to ensure a range of about 1 km. In the following description, for the sake of simplicity, the flight time until the bullet reaches is not considered.

[0015] Figure 2 is a block diagram showing an example of the unmanned aircraft countermeasure system according to the embodiment. This system is constructed with the central server 2 as the core. The central server 2 is installed, for example, in the center 1 (Figure 1).

[0016] The central server 2 is communicably connected to the database (DB) 6, the radio wave sensor 12, and the asset group (jammer 3, capture drone 4, launcher 5) via the network 10.

[0017] The database (DB) 6 stores sensing data acquired by the radio wave sensor 12, information calculated by the central server 2, or information such as existing data used for calculations. In particular, in the database 6, the specifications of the radio waves radiated from the unmanned aircraft, which are acquired in advance for each type of unmanned aircraft, are registered in advance.

[0018] Here, the specifications are various quantities that characterize the radio wave source of the radio wave, such as frequency, frequency band, modulation pattern, pulse pattern, or signal level. By analyzing the specifications and comparing them with the existing database, it is possible to identify the unmanned aircraft as the radio wave source.

[0019] The radio wave sensor 12 is, for example, a passive radar, captures the radio waves radiated from the target, and obtains sensing data. The sensing data includes at least the signal intensity and the arrival direction of the signal (horizontal direction and vertical direction). The sensing data also includes data such as the frequency of the signal, the bandwidth, the communication method, and the acquisition time. The sensing data is received and analyzed to obtain target information such as the arrival direction of the target.

[0020] Figure 3 is a functional block diagram showing an example of the central server 2 shown in Figure 2. The central server 2 is a computer equipped with a processor 21, memory 20, and storage 22. In other words, the functions of the central server 2 are realized by the processor 21 executing a program 22a (process 20a) loaded from the storage 22 into the memory 20.

[0021] The central server 2 further includes a communication unit 23, a media reading unit 25 for reading optical media 24, an interface (I / F) unit 26, a display device 27, and an operation unit 28. These are connected to the internal bus 29 along with the processor 21, memory 20, and storage 22.

[0022] The communication unit 23 is connected to the database 6, radio wave sensor 12, and asset group (jammer 3, capture drone 4, launcher 5) via the network 10, enabling communication. The interface unit 26 is connected to the display device 27 and the operation unit 28. The operation unit 28 is equipped with a mouse, keyboard, etc., and accepts input from the operator.

[0023] The storage device 22 is a non-volatile storage device such as an HDD (Hard Disk Drive) or SSD (Solid State Drive), and stores the program 22a, sensing data 22b from the sensor group, and a priority table 22c. Here, the program 22a can be recorded on, for example, an optical media 24 and distributed, and installed on the central server 2 via the media reading unit 25.

[0024] In Figure 3, the processor 21 includes a data acquisition unit 21a, a data analysis unit 21b, a identification unit 21c, a risk analysis unit 21d, and a presentation unit 21e, which are novel functions related to this embodiment. These functions are realized by the processor 21 executing a program 22a (process 20a) loaded into the memory 20.

[0025] The data acquisition unit 21a communicates with the radio wave sensor 12 and acquires sensing data obtained by the radio wave sensor 12 via the network 10.

[0026] The data analysis unit 21b analyzes the acquired sensing data and calculates the parameters of the radio waves captured by the radio wave sensor 12.

[0027] The identification unit 21c accesses the database 6 using the above specifications as a key and identifies the unmanned aerial vehicle corresponding to these specifications.

[0028] The risk analysis unit 21d analyzes the risk level of the unmanned aerial vehicle identified by the identification unit 21c based on the signal strength included in the sensing data acquired from the radio wave sensor 12 and the direction of the incoming radio waves.

[0029] In particular, the risk analysis unit 21d calculates the movement trend of the identified unmanned aerial vehicle (target drone) from changes in radio signal strength. Based on this trend, it calculates the risk level of the target drone. Here, the movement trend of the unmanned aerial vehicle includes, for example, approaching or retreating toward the friendly side, and the speed of movement.

[0030] The risk analysis unit 21d averages the signal strength of radio waves included in the sensing data over multiple sampling periods and calculates the risk level of the target drone based on the trend of changes in the average value.

[0031] Furthermore, the risk analysis unit 21d compares the radio wave parameters calculated by the data analysis unit 21b with the current signal strength of these radio waves to calculate the geographical area where the target drone may be located. Based on these results, the risk analysis unit 21d then analyzes the risk level of the target drone.

[0032] In other words, by referring to the specifications of the identified target drone, the radio transmission power level can be determined. If the received signal strength and the transmission power level are close, it means the target drone is nearby, and the risk level is higher.

[0033] The presentation unit 21e, based on the risk level calculated by the risk analysis unit 21d, presents, for example, guidance on the display device 27, which of the asset group (jammer 3, capture drone 4, launcher 5, etc.) should be used as an option to deal with the situation.

[0034] Figure 4 shows an example of information registered in database 6. Database 6 stores signal specification table 6a, sensing data 6b, and possible range 6c.

[0035] Signal Specifications Table 6a is a table that registers known unmanned aerial vehicles, associating their type with the previously acquired specifications of the emitted radio waves. Sensing data 6b is sensing data acquired from the radio wave sensor 12. The possible location range 6c is data calculated by the risk analysis unit 21d, indicating the geographical area where the target drone may be located.

[0036] Figure 5 shows an example of a target direction shown on a two-dimensional map. The display device 27 displays a digital map with contour lines showing the Earth's surface (including land and sea) viewed from vertically above. The digital map shown in Figure 5 includes coastlines, which are the boundaries between land and sea, and contour lines indicating the height of the land.

[0037] Once the target direction is obtained, the position of our side (black circle symbol 50) and a line segment 60 extending from symbol 50 to the target direction are drawn on the digital map of the display device 27. The length of the line segment 60 indicates the radio wave strength; in other words, the line segment 60 is the signal strength vector from the target. With existing technology, it was difficult to accurately determine the location of the radio wave source using only sensor data acquired from a single radio wave sensor. The following describes a technology that can overcome this difficulty.

[0038] Figure 6 is a flowchart showing an example of the processing procedure of the central server 2. In Figure 6, the central server 2 receives sensing data from the radio wave sensor 12 (step S1), analyzes the movement characteristics (step S2), and analyzes the communication method (step S3).

[0039] In analyzing the movement characteristics, the central server 2 stores the signal strength included in the sensing data in the database 6 (step S21), and also calculates the average value of the stored signal strength data and stores this in the database as well (step S22). From the trend of the average signal strength, the central server 2 can determine whether the target drone is approaching or retreating (step S23).

[0040] Meanwhile, based on the results of the communication method analysis, the central server 2 estimates the type or model of the target drone (step S4), and based on that result, it refers to the database 6 to derive the output strength of the captured radio waves at the location of the radio source (step S5).

[0041] Then, the central server 2 estimates the position of the target drone from the movement characteristics of the target drone and the derived radio wave output strength (step S6), calculates the possible range (area) in which it can exist (step S7), and registers it in the database.

[0042] Figure 7 shows the relationships between various quantities involved in calculating the distance to the target drone 30. As shown in Figure 7, the radio waves (signals) emitted from the target drone to be detected reach the radio wave sensor 12 and are detected as the sum of direct and indirect waves. Because there is a difference in the distance that the direct and indirect waves travel to reach the radio wave sensor 12, their phases are shifted upon arrival. In addition, the relative positions of the radio wave sensor 12 and the target drone 30, as well as the terrain shape, also have an influence, making the sum of the signal strengths unstable.

[0043] In Figure 7, the altitude of the target drone 30 is denoted as hd, and the height of the radio wave sensor 12 is denoted as ha. The distance the direct wave reaches the radio wave sensor 12 is denoted as Rt, and the distance the indirect wave reaches is denoted as R2 + R1. The horizontal distance between the target drone 30 and the radio wave sensor 12 is denoted as L, and the horizontal distance between the reflection point of the indirect wave on the ground and the radio wave sensor 12 is denoted as L1, so L2 is defined as L = L1 + L2.

[0044] The distance between the radio wave sensor 12 and the target drone 30 is estimated based on the reception sensitivity of the radio wave sensor 12, which is represented by a combination of direct and indirect waves from the target drone 30, and the signal strength from the radio wave source.

[0045] If the transmission power of the target drone is Pt and the transmission gain of the drone is Gt, the power density at a distance R from the drone can be expressed by equation (1).

number

[0046] Since the radio wave sensor 12 is unidirectional, the value in equation (1) is multiplied by the effective area Ar of the radio wave sensor 12, and the signal strength S of the received power is calculated using equation (2).

number

[0047] Here, the receiving gain Gr of the radio wave sensor can be expressed by equation (3), so equation (4) holds. Therefore, the signal strength S can be transformed into equation (5).

number

[0048] From the above, if we know S for the direct wave only, and Pt and Gt, we can calculate the distance R using equation (6).

[0049]

number

[0050] [Table 1]

[0051] Figure 8 is a graph showing an example of the change in signal strength with respect to distance from the radio wave sensor. The origin of the graph indicates the installation position of the radio wave sensor. In Figure 8, curve (B) first shows the change in signal strength with only the direct wave. Curve (A), which is at a higher level, shows the possible changes in signal strength when the indirect wave is added. (C) shows the possible changes when the signal is canceled out by the indirect wave.

[0052] The signal strength of radio waves captured by radio wave sensors is constantly affected by indirect waves with a phase shift. Therefore, it is difficult to determine changes in the signal strength of the direct wave alone from a single data point.

[0053] Figure 9 is a graph showing an example of the expected signal strength of radio waves from a drone approaching a radio wave sensor. The signal strength received by the radio wave sensor 12 changes non-linearly. Therefore, by taking the average value over a certain period and comparing the changes before and after, it is possible to understand the relative change in signal strength.

[0054] Figure 10 shows an example of an interval for taking the average value of signal strength. In Figures 9 and 10, the black dots indicate observed signal strength values. However, the horizontal axis in Figure 9 is distance, while the horizontal axis in Figure 10 is time. In Figure 10, a sampling period of a certain length is set, and each is designated as interval (A), interval (B), ..., interval (F) according to the passage of time. The average of the observed signal strength values ​​in each interval is shown by the horizontal dotted line. As shown in Figure 10, by taking the average value over a certain period and tracking the change in the average value over time, it is possible to grasp the changes in signal strength that fluctuate non-linearly. In this embodiment, for example, the average value of the signal strength obtained over a continuous 10 seconds (parameter) is considered as the signal strength of the direct wave.

[0055] As described above, in this embodiment, the signal strength of radio waves acquired by the radio wave sensor 12 is accumulated over a certain period, and the changes in strength are averaged over multiple sampling periods. Based on the changes in this average value, the approach and retreat of the target drone are determined. In addition, the target drone is estimated from signal parameters such as frequency information, and the geographical range in which it may exist (possible range of existence) is calculated in reverse from the radio wave signal strength that can be determined from known specifications. This allows the approximate position, speed, and direction to be determined, and the degree of danger posed by the target drone is analyzed based on this information. Furthermore, if the degree of danger is high, appropriate countermeasures based on the signal parameters are selected and presented.

[0056] Existing response systems using radio wave sensors could only detect the direction of arrival of radio waves coming from the target. Furthermore, since the target's location was estimated by crossing the directions of arrival of the captured radio waves on a map, it was necessary to use multiple radio wave sensors.

[0057] In contrast, according to this embodiment, even under conditions where only a single radio wave sensor is functioning, it becomes possible to detect an approaching drone, understand its situation, and take action. Therefore, according to this embodiment, it is possible to provide an unmanned aerial vehicle response system, a central device, and a risk analysis method that can analyze the degree of danger of an aircraft from information acquired by radio wave sensors.

[0058] However, this invention is not limited to the embodiments described above. For example, in Figure 2, an active radar that emits radar pulses into space and receives reflected echoes from a target, or an image sensor that acquires image data as sensing data, may be connected to the network 10. Furthermore, an acoustic sensor that captures sounds emitted from the target (such as propeller noise) using a microphone and acquires target information such as the direction of arrival, aircraft type, and type identification result of the target using methods such as spectral analysis may also be connected to the network 10. Conversely, a situation in which these sensor groups are compromised and only the radio wave sensor 12 remains operational can be considered as the situation in Figure 2. In addition, while the configuration shown involves the central server 2 accessing the database 6 via the network 10, the central server 2 can also have the database 6 built-in.

[0059] While embodiments have been described, these embodiments are presented as examples only and are not intended to limit the scope of the invention. These novel embodiments can be implemented in various other forms, and various omissions, substitutions, and modifications are permitted without departing from the spirit of the invention. These embodiments and their variations are included within the scope and spirit of the invention, as well as within the scope of the claims and their equivalents. [Explanation of symbols]

[0060] 1...Center, 2...Central Server, 3...Jammer, 4...Capture Drone, 5...Launch Device, 6...Database, 6a...Signal Specifications Table, 6b...Sensing Data, 6c...Possible Existence Range, 10...Network, 12...Radio Wave Sensor, 20...Memory, 21...Processor, 21a...Data Acquisition Unit, 21b...Data Analysis Unit, 21c...Identification Unit, 21d...Risk Analysis Unit, 21e...Presentation Unit, 22...Storage, 22a...Program, 22b...Sensing Data, 22c...Priority Table, 23...Communication Unit, 24...Optical Media, 25...Media Reading Unit, 26...Interface Unit, 27...Display Device, 28...Operation Unit, 29...Internal Bus, 30...Target Drone, 41...Capture Net, 50...Symbol, 60...Line Segment.

Claims

1. A radio wave sensor that captures radio waves emitted from an aircraft and obtains sensing data including at least the signal strength and direction of arrival of the radio waves, A database containing pre-acquired data on the radio wave parameters emitted from each type of unmanned aerial vehicle, It is equipped with a central device, The aforementioned central device is A data acquisition unit that acquires the sensing data from the radio wave sensor, A data analysis unit analyzes the sensing data and calculates the parameters of the captured radio waves, A special unit that accesses the aforementioned database and identifies an unmanned aerial vehicle corresponding to the calculated specifications, An unmanned aircraft response system comprising: a risk analysis unit that analyzes the degree of risk of the identified unmanned aircraft based on the signal strength and the direction of arrival.

2. The unmanned aircraft countermeasure system according to claim 1, wherein the risk analysis unit calculates the trend of movement of the identified unmanned aircraft from the change in signal intensity, and calculates the risk level based on this trend.

3. The unmanned aerial vehicle (UAV) countermeasure system according to claim 2, wherein the tendency of movement includes the approach or retreat of the identified UAV and the speed of movement.

4. The unmanned aerial vehicle countermeasure system according to claim 2, wherein the risk analysis unit averages the signal intensity over a plurality of sampling periods and calculates the risk based on the trend of change of the average value.

5. The unmanned aircraft countermeasure system according to claim 1, wherein the risk analysis unit compares the calculated parameters with the signal strength to calculate the geographical area in which the identified unmanned aircraft may be located, and analyzes the risk level based on the result.

6. Each is further equipped with multiple countermeasures capable of dealing with unmanned aerial vehicles, The aforementioned central device is The unmanned aerial vehicle countermeasure system according to any one of claims 1 to 5, further comprising a display unit that presents a selection of the multiple countermeasures according to the degree of risk.

7. A central device that can be connected via a network to a radio wave sensor that captures radio waves emitted from an aircraft and obtains sensing data including at least the signal strength and direction of arrival of the radio waves, and to a database that has been pre-acquired for each type of unmanned aerial vehicle and registers the specifications of the radio waves emitted from the said unmanned aerial vehicle, A data acquisition unit that acquires the sensing data from the radio wave sensor, A data analysis unit analyzes the sensing data and calculates the parameters of the captured radio waves, A special unit that accesses the aforementioned database and identifies an unmanned aerial vehicle corresponding to the calculated specifications, A central device comprising: a risk analysis unit that analyzes the risk level of the identified unmanned aerial vehicle based on the signal strength and the direction of arrival.

8. A risk analysis method using a computer that can connect to a radio wave sensor that captures radio waves emitted from an aircraft and obtains sensing data including at least the signal strength and direction of arrival of the radio waves, and a database that has been pre-acquired for each type of unmanned aerial vehicle and registers the specifications of the radio waves emitted from the unmanned aerial vehicle, The process by which the computer acquires the sensing data from the radio wave sensor, The process by which the computer analyzes the sensing data and calculates the parameters of the captured radio waves, The process by which the computer accesses the database and identifies the unmanned aerial vehicle corresponding to the calculated parameters, A risk analysis method comprising the steps of a computer analyzing the risk level of the identified unmanned aerial vehicle based on the signal strength and the direction of arrival.

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