KR102290702B1 - Method for recognition about drone position - Google Patents

Abstract

A drone location recognition method according to an embodiment of the present invention comprises the steps of: (a) receiving a Wi-Fi signal from a plurality of Wi-Fi routers; (b) calculating a distance between the Wi-Fi router and the drone based on the signal strength of the Wi-Fi signal received in step (a); (c) determining a direction in which the Wi-Fi signal is incident on the drone; and (d) recognizing the location of the drone through the distance information calculated in step (b) and the incident direction information determined in step (c).

Description

How to recognize the location of a drone

The present invention relates to a drone location recognition method.

Recently, unmanned aerial vehicles (UAVs), also known as drones, have been widely used in military, scientific and exploration missions. Many technologies and algorithms are being developed to realize fully autonomous driving of drones.

Currently, a global positioning system (GPS) is widely used to measure the location of a drone. However, in the case of GPS, it is often difficult to determine the exact location of a drone due to instability or distortion of the GPS signal in a terrain where there are many buildings, tunnels, or mountains. And to solve this problem, auxiliary sensors such as vision sensors, laser scanners, accelerometers and gyroscopes are being applied.

However, the attachment of such additional auxiliary sensors causes additional problems such as an increase in the loading capacity of the drone and an increase in energy consumption of the drone's built-in battery. Accordingly, there is a continuous increase in demand for technology development capable of improving the accuracy of location measurement of a drone without an increase in loading capacity or increase in energy consumption.

An embodiment of the present invention aims to provide a drone location recognition method for recognizing a location of a drone through a distance between a Wi-Fi device and a drone measured through Wi-Fi signal strength and a direction in which a Wi-Fi signal is incident on the drone.

On the other hand, the technical problems to be achieved in the present invention are not limited to the technical problems mentioned above, and other technical problems not mentioned are clearly to those of ordinary skill in the art to which the present invention belongs from the description below. can be understood

A drone location recognition method according to an embodiment of the present invention comprises the steps of: (a) receiving a Wi-Fi signal from a plurality of Wi-Fi routers; (b) calculating a distance between the Wi-Fi router and the drone based on the signal strength of the Wi-Fi signal received in step (a); (c) determining a direction in which the Wi-Fi signal is incident on the drone; and (d) recognizing the location of the drone through the distance information calculated in step (b) and the incident direction information determined in step (c).

The step (b) includes: (b-1) correcting the signal strength of the Wi-Fi signal by applying the urban distance attenuation model to the Wi-Fi signal received through the step (a); (b-2) A Wi-Fi that selects a preset number of Wi-Fi signals in the order of increasing signal strength among the Wi-Fi signals corrected in step (b-1), sets them as adjacent Wi-Fi signals, and transmits the adjacent Wi-Fi signals setting the router as an adjacent Wi-Fi router; and (b-3) calculating a distance between the adjacent Wi-Fi router and the drone by applying a Received Signal Strength Indicator (RSSI) to the signal strength of the adjacent Wi-Fi signal.

The downtown distance attenuation model is,

, where h is the height of a building in a nearby area, d is the distance between the drone and the Wi-Fi router, and γ may be a scale parameter describing a Rayleigh variance random variable for each building.

The step (c) is: setting a Wi-Fi router that transmits a first signal having the highest signal strength among the neighboring Wi-Fi signals as the first router, and the first signal has a virtual height equal to the height of the drone. Calculating a first angle that is an angle formed with a plane of , and setting a Wi-Fi router that transmits a second signal that is a Wi-Fi signal incident to the drone at the same angle as the first angle as a second router, the first router and The distance between the drones from the second router may be calculated.

In step (c-1): A Wi-Fi router capable of transmitting the strongest Wi-Fi signal among the adjacent Wi-Fi routers may be selected using a Markov Chain Monte Carlo (MCMC) method.

The step (d) includes: (d-1) recognizing the location of the drone through the distance information calculated in step (b); and (d-2) when the distance difference between the position of the drone recognized in step (d-1) and the position of the drone recognized before the preset time is equal to or greater than a preset value, the incident direction information determined in step (c) In further consideration, the method may include re-recognizing the location of the drone.

The drone location recognition method according to an embodiment of the present invention may recognize the location of the drone through the distance between the Wi-Fi equipment and the drone measured through the Wi-Fi signal strength and the direction in which the Wi-Fi signal is incident on the drone.

On the other hand, the effects obtainable in the present invention are not limited to the above-mentioned effects, and other effects not mentioned will be clearly understood by those of ordinary skill in the art to which the present invention belongs from the following description. will be able

1 is a diagram schematically illustrating a state of recognizing a location of a drone through a Wi-Fi router.
2 is a flowchart illustrating a drone location recognition method according to an embodiment of the present invention.
3 is a diagram schematically illustrating a state in which a distance between each Wi-Fi router and a drone is calculated based on the strength of a Wi-Fi signal and the location of the drone is recognized through this.
4 is a flowchart illustrating a step of calculating a distance between a Wi-Fi router and a drone based on the signal strength of a received Wi-Fi signal.
5 is a flowchart illustrating a step of determining a direction in which a Wi-Fi signal is incident on a drone.
6 is a diagram schematically illustrating a state of a radio wave through which a Wi-Fi signal is propagated from a Wi-Fi router.
7 is a diagram schematically illustrating a state in which a location of a drone is recognized through a direction in which a Wi-Fi signal is incident on a drone and a distance between a Wi-Fi router and the drone.

Other advantages and features of the present invention, and a method of achieving them will become apparent with reference to the embodiments described below in detail in conjunction with the accompanying drawings. However, the present invention is not limited to the embodiments disclosed below, but may be implemented in various different forms, and only this embodiment serves to complete the disclosure of the present invention, and to obtain common knowledge in the technical field to which the present invention pertains. It is provided to fully inform the possessor of the scope of the invention, and the present invention is only defined by the scope of the claims.

Even if not defined, all terms (including technical or scientific terms) used herein have the same meaning as commonly accepted by common technology in the prior art to which this invention belongs. Terms defined by general dictionaries may be construed as having the same meaning as they have in the related description and/or in the text of the present application, and are conceptualized even if not expressly defined herein.

nor will it be construed as overly formal.

The terminology used herein is for the purpose of describing the embodiments and is not intended to limit the present invention. As used herein, the singular also includes the plural unless specifically stated otherwise in the phrase. As used herein, 'comprise' and/or various conjugations of this verb, eg, 'comprise', 'comprising', 'comprising', 'comprising', etc., refer to the referenced composition, ingredient, component, A step, operation and/or element does not exclude the presence or addition of one or more other compositions, components, components, steps, operations and/or elements. As used herein, the term 'and/or' refers to each of the listed components or various combinations thereof.

Meanwhile, terms such as '~ unit', '~ group', '~ block', and '~ module' used throughout this specification may mean a unit that processes at least one function or operation. For example, it can mean software, hardware components such as FPGAs or ASICs. However, '~ part', '~ group', '~ block', and '~ module' are not meant to be limited to software or hardware. '~ unit', '~ group', '~ block', and '~ module' may be configured to reside in an addressable storage medium or to regenerate one or more processors.

Accordingly, as an example, '~ part', '~ group', '~ block', and '~ module' are components such as software components, object-oriented software components, class components, and task components. fields, processes, functions, properties, procedures, subroutines, segments of program code, drivers, firmware, microcode, circuitry, data, databases, data structures, tables, arrays and include variables. The functions provided within the components and '~bu', '~gi', '~block', and '~module' are smaller than the number of components and '~bu', '~gi', '~block' ', '~modules' combined or additional configuration

It can be further divided into elements and '~part', '~gi', '~block', and '~module'.

Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings of the present specification.

1 is a diagram schematically showing a state of recognizing the location of a drone 10 through a Wi-Fi router.

Referring to FIG. 1 , the drone 10 receives a position movement command from an external device and moves the position according to the corresponding command. In this case, in order to move the position of the drone 10, it is necessary to preemptively recognize the position of the drone.

In general, location measurement of a drone is performed through a GPS device. However, unlike open land, in the city center, the GPS signal is distorted due to reflections from different building heights and glass windows attached to the wall of the building, and the accuracy of GPS location measurement is lowered due to numerous radio waves.

In order to compensate for this, in the present invention, by using a Wi-Fi signal, an error in location measurement through a GPS signal is reduced and the accuracy of drone location recognition is improved.

A large number of Wi-Fi access points (hereinafter referred to as Wi-Fi routers) are installed inside buildings and on roads. Wi-Fi routers include Wi-Fi routers installed by the government and local governments, and Wi-Fi routers installed by each telecommunication company and available to anyone who subscribes to the communication.

The drone location recognition method ( S10 ) of the present invention recognizes the location of the drone 10 using a Wi-Fi signal received from such a Wi-Fi router.

Each Wi-Fi router has its own MAC address, and location information of each Wi-Fi router can be obtained from a Wi-Fi Positioning System (WPS) through the MAC address.

The existing method for recognizing the location of a drone consists of measuring the distance between the drone 10 and the Wi-Fi router by directly applying a Received Signal Strength Indicator (RSSI) to the signal strength of the received Wi-Fi signal. However, this method not only has low accuracy, but also has a problem in that it is difficult to apply in places where Wi-Fi signal distortion is severe, such as in downtown areas, or there are many shaded areas where Wi-Fi signals do not reach.

Hereinafter, a method for correcting an error of location recognition through GPS and overcoming a disadvantage of location recognition that occurs when only the RSSI method is used alone is proposed.

2 is a flowchart illustrating a drone location recognition method (S10) according to an embodiment of the present invention.

Referring to FIG. 2 , the drone location recognition method ( S10 ) according to an embodiment of the present invention includes the steps of receiving Wi-Fi signals from a plurality of Wi-Fi routers ( S100 ), the signal strength of the Wi-Fi signals received through the steps S100 . Calculating the distance between the Wi-Fi router and the drone through the step (S200), determining the direction in which the Wi-Fi signal is incident on the drone (S300), and the distance information calculated through the step S200 and the incident direction determined through the step S300 and recognizing the location of the drone through information (S400).

3 is a diagram schematically illustrating a state in which a distance between each Wi-Fi router and the drone 10 is calculated based on the strength of the Wi-Fi signal and the location of the drone is recognized through this.

Referring to FIG. 3 , the drone 10 of the present invention includes a Wi-Fi receiver in order to receive Wi-Fi signals from a plurality of Wi-Fi routers ( S100 ). The Wi-Fi receiver receives the Wi-Fi signal transmitted from the Wi-Fi router.

Calculating the distance between the Wi-Fi router and the drone through the signal strength of the Wi-Fi signal (S200) is a step of correcting the signal strength of the Wi-Fi signal by applying the urban distance attenuation model to the Wi-Fi signal received through the step S100 (S210) ), selects a preset number of Wi-Fi signals in the order of increasing signal strength among the Wi-Fi signals corrected through step S210, sets them as adjacent Wi-Fi signals, and sets the Wi-Fi router that transmits the adjacent Wi-Fi signal as the adjacent Wi-Fi router and calculating a distance between the adjacent Wi-Fi router and the drone by applying a Received Signal Strength Indicator (RSSI) to the signal strength of the adjacent Wi-Fi signal (S230).

4 is a flowchart illustrating the step (S200) of calculating the distance between the Wi-Fi router and the drone through the signal strength of the received Wi-Fi signal.

Referring to FIG. 4 , in step S200, the signal strength of the Wi-Fi signal is corrected by applying the urban distance attenuation model to the Wi-Fi signal received through step S100 (S210), and the signal strength of the Wi-Fi signal corrected through steps S210. The step of selecting a preset number of Wi-Fi signals in the order of increasing, setting them as adjacent Wi-Fi signals, and setting a Wi-Fi router that transmits the adjacent Wi-Fi signal as a neighboring Wi-Fi router (S220) and RSSI in the signal strength of the adjacent Wi-Fi signal and calculating the distance between the adjacent Wi-Fi router and the drone by applying (Received Signal Strength Indicator) (S230).

First, the signal strength of the Wi-Fi signal is corrected by applying the urban distance attenuation model to the Wi-Fi signal received through step S100 (S210).

When the drone 10 receives a Wi-Fi signal through the Wi-Fi receiver and applies a Received Signal Strength Indicator (RSSI) to the signal strength, the distance between each Wi-Fi router that has transmitted the corresponding Wi-Fi signal and the drone can be calculated.

However, the Wi-Fi signal in the city center is reduced due to interference between neighboring buildings or other signals. Therefore, in order to compensate for such Wi-Fi signal reduction, the Wi-Fi signal strength must be corrected by applying a distance reduction model.

A general distance reduction model is defined as Equation (1) below under probability conditions.

...... 식 (1)

...... Equation (1)

In this case, fc is the Wi-Fi signal frequency (carrier frequency), Rc is the data transmission rate (transmission data rate), d is the distance between the drone and the Wi-Fi router, P(Loss) is the probability of line-of-sight (LoS) )), P(NLoS) is the probability of non-line-of-sight, η_Loss is additional losses in the case of LoS, and η_NLoS is additional losses in the NLoS case in the case of NLoS).

In the case of the present invention, this general distance reduction model was modified into a downtown distance reduction model suitable for application to the city center.

Equation (2) below is an equation showing the downtown distance reduction model of the present invention.

...... 식 (2)

...... Equation (2)

In this case, h is the height of the building in the neighboring area, d is the distance between the drone and the Wi-Fi router, and γ is a scale parameter describing the Rayleigh distributed random variable for each building, and in the present invention, γ is 1.5.

Next, a preset number of Wi-Fi signals are selected in order of increasing signal strength among the Wi-Fi signals corrected through step S210, set as adjacent Wi-Fi signals, and a Wi-Fi router that transmits the adjacent Wi-Fi signal is set as an adjacent Wi-Fi router do (S220).

The process of selecting a preset number of Wi-Fi signals in order of increasing signal strength from among the corrected Wi-Fi signals is performed through the processes of Equations (3) to (5) below.

...... 식(3)

...... Equation (3)

...... 식(4)

...... Equation (4)

...... 식(5)

...... Equation (5)

In cities where there are many Wi-Fi routers, multiple signals are mixed, so it is necessary to determine whether the signal coming into the drone is a good signal and select the signal. To this end, equations (3) to (5) are considered, which relates to the constraint when finding a Wi-Fi router located at a distance with the least loss of radio waves, that is, a Wi-Fi router with good signal strength. The constraint changes according to the distance information included in the distance expression below.

...... 식(6)

...... Equation (6)

The drone moves its position according to time, and the signal strength received by the position movement always changes. Therefore, a signal loss less than or equal to a predetermined threshold of radio waves that the drone can receive is set as the threshold. The threshold value is selected as a signal that can be received at a specific bit error rate (BER) per transmission. Based on the loss in one urban area, the loss can be calculated at the two locations i and j through Equation (7) below.

...... 식(7)

...... Equation (7)

Through Equation (7), it is determined that the signal-to-interference-plus-noise ratio (SINR) is good as a value obtained by taking the reciprocal of the propagation loss factor obtained at two locations. The radio wave may follow the radio wave loss rate in a general city center, or the radio wave may be received at a high transmission rate exceeding the loss rate.

According to an embodiment, after considering the above conditions, four Wi-Fi signals are selected in the order of Wi-Fi signal strength and set as adjacent Wi-Fi signals.

After the adjacent Wi-Fi signal is set, the distance between each of the adjacent Wi-Fi routers 20a, 20b, 20c and 20d and the drone is calculated by applying RSSI to the signal strength of the adjacent Wi-Fi signal (S230).

At this time, since the signal strength of the Wi-Fi signal used for RSSI application was corrected while going through the downtown distance reduction model in step S210, the accuracy of distance calculation compared to the case of directly applying RSSI without correction or applying a general distance reduction model can increase

If the location information of the drone 10 is normally recognized through the distance between the adjacent Wi-Fi router 20 and the drone 10 calculated through the above process and the location information of each adjacent Wi-Fi router 20, the following process can be omitted. there is.

However, in order to more accurately recognize the location of the drone, the step of determining the direction in which the Wi-Fi signal is incident on the drone ( S300 ) may be additionally performed.

For example, if the distance difference between the current position of the drone recognized through step S200 and the position of the drone recognized before the preset time is greater than a preset value, the following step S300 may be additionally performed.

5 is a flowchart showing the step (S300) of determining the direction in which the Wi-Fi signal is incident on the drone,

6 is a diagram schematically showing the state of the radio wave through which the Wi-Fi signal is propagated from the Wi-Fi router,

7 is a diagram schematically illustrating a state in which a location of a drone is recognized through a direction in which a Wi-Fi signal is incident on the drone 10 and a distance between the Wi-Fi router and the drone.

5 to 7 , in step S300, a Wi-Fi signal having the largest signal strength among the adjacent Wi-Fi signals set in step S220 is set as the first signal, and the Wi-Fi router that transmits the first signal is set to the first router 30 ), setting the first vector 31 with the drone as the starting point of the vector and the first router as the vector ending point (S310), by analyzing the signal waveform of the first signal received by the drone 10 Determining the direction in which the first signal is incident on the drone (S320), setting a virtual plane 50 having the same height as the altitude of the drone 10, and setting the virtual plane 50 and the first vector 31 ) calculating a first angle 32 that is an angle formed by (S330), searching for a Wi-Fi signal incident on the drone at the same angle as the first angle 32, setting it as a second signal, and transmitting the second signal and setting the Wi-Fi router as the second router 40 (S340) and calculating the distance between the drone from the first router and the second router (S350).

Referring to FIG. 6 , the strength of a Wi-Fi signal may be different from each other due to various factors including radio wave interference caused by a received building, etc., even if they are separated from each other at the same distance (R).

Therefore, in the present invention, in order to perform step S300, a Wi-Fi signal having the largest signal strength received from the drone 10 among the adjacent Wi-Fi routers 20 selected in step S220 is set as the first signal, and the first signal A Wi-Fi router that transmits is set as the first router 30 . Thereafter, the first vector 31 is set with the drone 10 as the starting point of the vector and the first router 30 as the vector ending point.

At this time, in order to identify the Wi-Fi router having the largest Wi-Fi signal strength among the adjacent Wi-Fi sharing devices 20 selected in step S220, the present invention uses a Markov Chain Monte Carlo (MCMC) method.

The method is expressed by Equation (8) below.

...... 식(8)

...... Equation (8)

When a plurality of adjacent Wi-Fi routers 20 exist through the application of Equation (8), a Wi-Fi router having the highest Wi-Fi signal strength among them may be selected.

Referring to FIG. 7 , after selecting the first signal and the first router 30 through Equation (8), the signal waveform of the first signal is analyzed to determine the direction to be incident on the drone 10 .

The Wi-Fi signal takes the form of a wave and is incident on the drone 10 , and by analyzing the form in which the wave is incident at this time, it is possible to determine the direction in which the Wi-Fi signal is incident on the drone 10 .

According to the IEEE 802.11 wireless standard, the Wi-Fi router announces its existence by broadcasting the SSID to the devices it can connect to to announce its existence. SSID (Service Set Identifier) is 0~4 bytes in size and is the service provider name (ID) of a single jurisdiction (ESS, extended service set) that groups several Wi-Fi routers. The SSID packet is an ID that identifies Wi-Fi routers. This is a required field corresponding to the body of the beacon frame transmitted through the LAN. It is a 32-bit unique identifier attached to the required header of the packet.

Clients attempt to connect to the Wi-Fi router through the SSID displayed on the device and request a session connection through an authentication process.

Beacons allow nearby clients to easily discover Wi-Fi routers. A beacon is a broadcast frame that a Wi-Fi router regularly notifies of the existence of a wireless network (BSS) under its jurisdiction. It is one of the wireless network management frame types of IEEE 802.11. Their main role is to announce the existence of a wireless network through broadcasting, and to find 802.11, which is a wireless network, to be connected to a wireless network. Finally, the SSID signal is displayed on the client's mobile device, and the client tries to connect to the Wi-Fi router through the searched SSID signal and the authentication process begins.

In order to determine the distance between the drone and the Wi-Fi router, it is necessary to obtain the angle between the Wi-Fi antenna and the drone through the Wi-Fi signal. For this purpose, a method called AoA (Angle of Arrival) is used to know the angle of the incoming radio wave between the drone and the Wi-Fi router.

The method consists of three steps: collecting radio wave phase information (collecting the phase difference of the radio wave arriving at each antenna through the MIMO antenna built into the drone), calculating the radio wave phase difference between the received antennas, and using the MIMO antenna and figuring out the radio wave transmission location of the corresponding AP through the angle difference of the arriving radio waves.

, λ를 신호의 파장(주파수는 파장의 역수)이라고 한다면 해당 전파가 들어오는 각도 θ는 다음과 같이 표현된다.If the distance between the MIMO antennas is d, and the phase difference between the radio waves arriving from each antenna (the difference value in which the waveform changes with time, AoA) is calculated

If , λ is the wavelength of the signal (frequency is the reciprocal of the wavelength), the angle θ at which the radio wave enters is expressed as follows.

...... 식(9)

...... Equation (9)

After analyzing the signal waveform of the first signal received by the drone 10 through the above process and determining the direction in which the first signal is incident on the drone 10 ( S320 ), thereafter, the same height as the altitude of the drone A virtual plane 50 with , is set, and a first angle 32 that is an angle between the virtual plane 50 and the first vector 31 is calculated (S330).

Next, the Wi-Fi signal incident on the drone 10 at the same angle as the first angle 32 is searched and set as the second signal, and the Wi-Fi router that transmits the second signal is set as the second router 40 and (S340), and then calculates the distance between the drone 10 from the first router 30 and the second router 4 (S350).

That is, the Wi-Fi signals transmitted by the first router 30 and the second router 40 are incident on the drone 10 at the same angle, and communicate with the first router 30 and the second router 40 through RSSI. Since the distance between the drones 10 can be known, a more precise location recognition is possible than when recognizing the location of the drone only with distance information between the Wi-Fi router and the drone in step S200.

In step S400, the location of the drone is recognized through the distance information calculated in step S200 and incident direction information of the Wi-Fi signal calculated in step S300.

Step S400 is a step of recognizing the location of the drone 10 through the distance information calculated in step S200 (S410) and the distance difference between the location of the drone recognized in step S410 and the location of the drone recognized before a preset time is preset. When the value is greater than or equal to the value, the method further includes the step of re-recognizing the position of the drone in consideration of the incident direction information determined in step S300 ( S420 ).

That is, basically, the location recognition of the drone 10 recognizes the location of the drone through only the distance information between the Wi-Fi router and the drone calculated in step S200.

However, if the distance difference between the position of the drone 10 recognized in step S410 and the position of the drone recognized before the preset time is equal to or greater than a preset value, for example, the position of the drone recognized 1 second ago and the currently recognized drone If the distance difference between the positions of the drones exceeds the distance difference when the drone moves at the maximum speed, the position of the drone is re-recognized in consideration of the incident direction information determined in step S300.

In other words, in a situation where the location recognition of the drone 10 is normally performed, the location of the drone is recognized only through step S200, and when it is determined that there is a problem in the location recognition of the drone, step S300 is additionally performed to recognize the location of the drone Data throughput for recognition and power consumption for data processing can be reduced.

Although the present invention has been described by way of examples above, the above examples are merely for explaining the spirit of the present invention and are not limited thereto. Those skilled in the art will understand that various modifications may be made to the above-described embodiments. The scope of the present invention is determined only through interpretation of the appended claims.

10: Drone 20: Adjacent Wi-Fi router
30: first router 31: first vector
32: first angle 40: second router

Claims (6)

(a) receiving a Wi-Fi signal from a plurality of Wi-Fi routers;
(b) calculating a distance between the Wi-Fi router and the drone based on the signal strength of the Wi-Fi signal received in step (a);
(c) determining a direction in which the Wi-Fi signal is incident on the drone; and
(d) recognizing the position of the drone through the distance information calculated in step (b) and the incident direction information determined in step (c),
Step (b) is:
(b-1) applying the urban distance attenuation model to the Wi-Fi signal received through step (a) to correct the signal strength of the Wi-Fi signal,
The downtown distance attenuation model is,

ego,
In this case, h is the height of the building in the neighboring area, d is the distance between the drone and the Wi-Fi router, and γ is the scale parameter that describes the Rayleigh variance random variable for each building.
How to recognize the location of a drone.
The method of claim 1,
Step (b) is:
(b-2) Wi-Fi for selecting a preset number of Wi-Fi signals in the order of increasing signal strength among the Wi-Fi signals corrected in step (b-1), setting them as adjacent Wi-Fi signals, and transmitting the adjacent Wi-Fi signals setting the router as an adjacent Wi-Fi router; and
(b-3) applying a Received Signal Strength Indicator (RSSI) to the signal strength of the adjacent Wi-Fi signal, further comprising calculating a distance between the adjacent Wi-Fi router and the drone
How to recognize the location of a drone.
delete 3. The method of claim 2,
Step (c) is:
(c-1) setting a Wi-Fi router that transmits a first signal having the highest signal strength among the adjacent Wi-Fi signals as the first router, using the drone as the starting point of the vector and the first router as the vector ending point setting a first vector;
(c-2) analyzing a signal waveform of the first signal received by the drone to determine a direction in which the first signal is incident on the drone;
(c-3) setting a virtual plane having the same height as the altitude of the drone, and calculating a first angle that is an angle between the virtual plane and the first vector;
(c-4) setting a Wi-Fi router that transmits a second signal, which is a Wi-Fi signal incident to the drone at the same angle as the first angle, as a second router; and
(c-5) calculating a distance between the drone from the first router and the second router
How to recognize the location of a drone.
5. The method of claim 4,
The step (c-1) is:
Using the Markov Chain Monte Carlo (MCMC) method, a Wi-Fi router that can transmit the strongest Wi-Fi signal among the adjacent Wi-Fi routers is selected.
How to recognize the location of a drone.
6. The method of claim 5,
Step (d) is:
(d-1) recognizing the location of the drone through the distance information calculated in step (b); and
(d-2) If the distance difference between the position of the drone recognized in step (d-1) and the position of the drone recognized before the preset time is equal to or greater than a preset value, the incident direction information determined in step (c) is further added Including the step of re-recognizing the location of the drone in consideration
How to recognize the location of a drone

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