KR102312890B1 - Apparatus and method for detecting a small unmanned aerial vehicle(uav) - Google Patents

Abstract

The present invention is to provide an apparatus and method for more effectively detecting a small unmanned aerial vehicle, and a signal receiver for receiving a radar signal including a target signal, and a range for acquiring a range profile from the received target signal a profile obtaining unit; a constant false alarm rate (CFAR) detecting unit detecting, as a range bin, a partial range having an amplitude exceeding a preset first threshold value as a range bin, based on the obtained range profile; a spectrum signal generator for generating a frequency spectrum signal by extracting a signal of the detected range bin; and, based on the frequency spectrum signal, an amplitude exceeding a second threshold determined based on a preset weight and a noise variable is detected and a control unit for detecting the number of detected frequencies and determining whether a target corresponding to the target signal is an unmanned aerial vehicle based on whether the number of detected frequencies exceeds a preset threshold number of signals.

Description

APPARATUS AND METHOD FOR DETECTING A SMALL UNMANNED AERIAL VEHICLE (UAV)}

The present invention relates to a detection device and a detection method for detecting a small unmanned aerial vehicle.

A radar device is widely used to detect the presence or absence of a threat target at a long distance using radio waves and the location of the threat target in advance. As a representative radar detection technique, there are techniques based on a constant false alarm rate (CFAR) detector. The CFAR technique performs detection using a point having a relatively large radar cross section (RCS) of a target, and forms an adaptive threshold in a noise environment, and It is a method that considers a signal exceeding the target as a target.

However, due to their small size, the radar cross section (RCS) is small, and they are used in urban or mountainous environments, and generally fly at low altitudes. Therefore, there is a problem that the conventional detection technique using the false alarm rate has a high probability that the small unmanned aerial vehicle is simply determined to be a clutter. . As a result, there is a problem that the detection based on the CFAR detector considering only the existing noisy environment cannot maintain excellent detection performance for a small UAV.

The present invention is to solve the above problems, and to provide an apparatus and method for more effectively detecting a small unmanned aerial vehicle.

A detection apparatus according to an embodiment of the present invention for achieving the above object includes a signal receiving unit for receiving a radar signal including a target signal, and a range profile obtaining unit for acquiring a range profile from the received target signal. and a CFAR (Constant False Alarm Rate) detector that detects, as a range bin, a partial range having an amplitude exceeding a preset first threshold value as a range bin, based on the obtained range profile; a spectrum signal generator for generating a frequency spectrum signal by extracting a signal of a bin, and based on the frequency spectrum signal, the frequency spectrum for which an amplitude exceeding a second threshold determined based on a preset weight and a noise variable is detected. and a control unit for detecting the number and determining whether a target corresponding to the target signal is an unmanned aerial vehicle based on whether the number of detected frequencies exceeds a preset number of threshold signals.

In an embodiment, the range profile obtaining unit is characterized in that the range profile is obtained by performing noncoherent integration on the received radar signal.

In one embodiment, the spectrum signal generator, from the signal of the range bin, the frequency spectrum signal of the target signal, the frequency spectrum component received from the rigid body of the target, and the frequency spectrum component received from the wing of the target are linearly combined The frequency spectrum component received from the rigid body of the target is a frequency spectrum according to the translational motion of the rigid body of the target, and the frequency spectrum component received from the wing of the target is the rotor of the target. It is characterized in that the component according to the translational motion of the wing and the component according to the fine motion of the wing are a convolutional frequency spectrum.

In one embodiment, the control unit, based on whether the amplitude (amplitude) for each frequency of the frequency spectrum of the target signal exceeds the second threshold, the frequency at which the target signal is received from the rigid body of the target It is determined whether only a spectrum component or a frequency spectrum component received from the wing of the target is further included, and if the number of frequencies having an amplitude exceeding the second threshold value exceeds the threshold signal number, the target It is characterized in that the target corresponding to the signal is determined by the unmanned aerial vehicle.

In an embodiment, the number of threshold signals is determined differently according to the characteristics of the UAV to be detected, and the characteristics of the UAV include the number of wings and the length of the wings of the UAV.

A detection method according to an embodiment of the present invention for achieving the above object includes the steps of: receiving a radar signal including a target signal; acquiring a range profile from the received target signal; Detecting, as a range bin, a partial range having an amplitude exceeding a preset first threshold according to a constant false alarm rate (CFAR) algorithm, as a range bin, based on a range profile; generating a frequency spectrum signal by extracting the signal; determining a second threshold value determined based on a preset weight and noise environment variable; detecting the number of detected frequencies, and determining whether the target corresponding to the target signal is an unmanned aerial vehicle based on whether the number of frequencies in which the amplitude exceeds the second threshold value exceeds a preset threshold signal number It is characterized in that it comprises the step of determining.

In an embodiment, the acquiring the range profile may include performing noncoherent integration on the received radar signal.

In one embodiment, the generating of the frequency spectrum signal comprises, from the range bin signal, the frequency spectrum signal of the target signal, the frequency spectrum component received from the rigid body of the target, and the frequency spectrum received from the wing of the target generating a frequency spectrum signal in which the components are linearly combined, the frequency spectrum component received from the rigid body of the target is a frequency spectrum according to the translational motion of the rigid body of the target, and the frequency spectrum component received from the wing of the target is , It is characterized in that the component according to the translational motion of the rotor of the target and the component according to the fine motion of the wing are a convolutional frequency spectrum.

In an embodiment, the second threshold is the noise environment variable estimated based on data included in a reference cell used for analysis of a noise environment among the input vectors according to the CFAR algorithm. And, characterized in that it is determined by the product of the preset weight.

In an embodiment, the number of threshold signals is determined by the following equation, and is characterized in that it is determined based on a wing length proportional to the number of wings of the unmanned aerial vehicle to be detected.

[Equation]

는 무인기 날개로부터 획득되는 미세성분 주파수 스펙트럼의 주파수 범위 최대값으로,

에 의해 결정되며, 여기서

는

이고, r은 무인기의 날개 길이,

는 탐지 신호의 파장 길이,

는 무인기의 각속도를 의미함. 그리고 쵸핑 주파수(chopping frequency)는

이고, N은 무인기의 날개 개수를 의미함.

is the maximum value of the frequency range of the fine component frequency spectrum obtained from the wing of the drone,

is determined by, where

Is

and r is the wingspan of the drone,

is the wavelength length of the detection signal,

is the angular velocity of the drone. And the chopping frequency is

and N means the number of wings of the UAV.

The effects of the detection apparatus and method according to the present invention will be described as follows.

According to at least one of the embodiments of the present invention, the present invention primarily detects a signal likely to be a small UAV based on a false alarm rate, and based on a frequency spectrum signal generated from the detected signal, the signal is a small UAV By determining whether or not it contains a component corresponding to the fine movement of Therefore, the present invention has the effect of accurately detecting a small UAV even in an environment in which a large number of clutter exists.

1 is a block diagram illustrating a configuration of a detection apparatus according to an embodiment of the present invention.
2 is a conceptual diagram illustrating a geometric structure modeling a signal of a small UAV.
3 is a conceptual diagram illustrating a procedure for performing CFAR detection for range profile-based detection.
4 is a conceptual diagram illustrating an example of setting a threshold value for detecting a target signal from a frequency spectrum signal according to an embodiment of the present invention.
5 is a flowchart illustrating an operation process of determining whether a received radar signal is a small unmanned aerial vehicle in the detection apparatus according to an embodiment of the present invention.
6 is an exemplary diagram illustrating a range profile and a first detection result in the range profile in the detection apparatus according to an embodiment of the present invention.
7 is an exemplary diagram illustrating a frequency spectrum obtained when a target is a small unmanned aerial vehicle, and a secondary detection result for the frequency spectrum.
8 is an exemplary diagram illustrating a frequency spectrum obtained when a target is a clutter and a result of performing secondary detection on the frequency spectrum.

It should be noted that technical terms used herein are used only to describe specific embodiments, and are not intended to limit the present invention. Also, as used herein, the singular expression includes the plural expression unless the context clearly dictates otherwise. As used herein, "consisting of." or "includes." The term such as etc. should not be construed as necessarily including all of the various components or steps described in the specification, and some components or some steps may not be included, or additional components or steps may not be included. It should be construed as being able to include more.

In addition, in describing the technology disclosed in the present specification, if it is determined that a detailed description of a related known technology may obscure the gist of the technology disclosed in this specification, the detailed description thereof will be omitted.

First, FIG. 1 is a block diagram illustrating a configuration of a detection apparatus according to an embodiment of the present invention. 2 is a conceptual diagram illustrating a geometric structure modeling a signal of a small UAV, and FIG. 3 is a conceptual diagram illustrating a procedure for performing CFAR detection for range profile-based detection. 4 is a conceptual diagram illustrating an example in which a threshold value is set according to an embodiment of the present invention.

First, referring to FIG. 1 , the detection apparatus 10 according to an embodiment of the present invention is connected to the control unit 100 and the control unit 100 , and a signal receiving unit 110 that can be controlled by the control unit 100 . , a range profile obtaining unit 120 , a CAFR detecting unit 130 , a spectral signal generating unit 140 , and a memory 150 . Meanwhile, the components illustrated in FIG. 1 are not essential components of the detection apparatus according to an embodiment of the present invention, and thus may include fewer or more components.

First, the signal receiver 110 may include at least one radar or be connected to at least one radar. In addition, the target signal detected by the included or connected at least one radar may be received.

Meanwhile, the received target signal, when the target is an unmanned aerial vehicle having wings rotating through a rotor, the signal of the unmanned aerial vehicle may have a modeled geometry as shown in FIG. 2 .

는

축을 중심으로

의 각속도로 회전할 수 있다. 이때 점산란원

와 원점

사이의 거리는

, 점

의 초기위상은

일 수 있다. 이 때 레이다의 시선방향(line of sight: LOS)과 소형 무인기의 날개 회전 평면 사이의 각도는

이고 레이더와 원점

사이의 거리는

일 수 있다. 그리고 레이더 시선방향의 벡터 성분을

평면에 정사영(projection)한 결과가

축에 존재할 경우, 산란점

와 레이더 사이의 거리는 하기 수학식 1과 같이 나타낼 수 있다.Referring to FIG. 2, the point scattering circle

Is

around the axis

can rotate at an angular velocity of At this time, the point scattering circle

and origin

distance between

, dot

The initial phase of

can be At this time, the angle between the line of sight (LOS) of the radar and the plane of rotation of the wing of the small UAV is

and the radar and origin

distance between

can be And the vector component of the radar line of sight

The result of projection on the plane is

scattering point, if present on the axis

The distance between and radar can be expressed as in Equation 1 below.

는 소형 무인기의 병진운동 성분을,

는 소형 무인기의 미세운동 성분일 수 있다. 즉, 산란점의 위치정보는 소형 무인기의 병진운동과 미세운동 성분의 선형결합으로 표현될 수 있다. 이때 상기

와

는 하기 수학식 2 및 수학식 3과 같이 나타낼 수 있다. in other words,

is the translational motion component of the small UAV,

may be a micro-motion component of a small UAV. In other words, the location information of the scattering point can be expressed as a linear combination of the translational motion and the fine motion component of the small UAV. At this time the

Wow

can be expressed as in Equations 2 and 3 below.

은 레이더로부터 원점

사이의 초기 거리,

는 레이더 가시선(Radar Line of Sight: RLOS) 방향으로의 속도,

는 같은 방향으로의 가속도 값을 의미함. here

is the origin from the radar

the initial distance between

is the speed in the direction of the radar line of sight (RLOS),

is the acceleration value in the same direction.

, 로터의 산란점 개수를

, 그리고 날개의 산란점 개수를

라고 가정하면, 거리 압축(range compression)(또는 펄스 압축)을 통해 특정 시간(레인지 프로파일(range profile) 시간

)에서의

번째 레이더 수신 펄스

로, 하기 수학식 4와 같이 정의될 수 있다. 이를 위해 레인지 프로파일 획득부(120)는 상기 도 1에서 살펴본 기하 구조를 가지는 소형 무인기의 신호에 대하여 펄스 압축(또는 거리 압축)을 수행하여 레인지 프로파일 시간

에서 송출되는 탐지 신호들 중

번째 신호의 레이더 수신 펄스(

)를 획득할 수 있으며, 이를 위해 비간섭 적분(noncoherent integration)을 수행할 수 있다. On the other hand, the signal of the small UAV is the number of scattering points of the rigid body, E in the number of wings connected to the rigid body, rotor, and single rotor of the small UAV.

, the number of scattering points of the rotor

, and the number of scattering points of the wing

Assuming that , a specific time (range profile time) through range compression (or pulse compression)

) in

second radar receive pulse

, it can be defined as in Equation 4 below. To this end, the range profile acquisition unit 120 performs pulse compression (or distance compression) on the signal of the small UAV having the geometric structure shown in FIG. 1 to obtain a range profile time.

Among the detection signals transmitted from

Radar reception pulse of the second signal (

) can be obtained, and for this purpose, noncoherent integration can be performed.

번째 신호의 대역폭(Bandwidth)을 의미함.Here, A _P denotes the intensity of the received signal at the P-th rigid body scattering point, B _l,b denotes the intensity of the received signal at the scattering point of the b-th wing of the l-th rotor, sinc denotes a sinc function, BW is said

It means the bandwidth of the second signal.

When the range profile is obtained through the range profile obtaining unit 120, the control unit 100 first detects a range including a target signal having an amplitude of a certain level or more based on the obtained range profile and a preset threshold value. detection can be performed. Here, the first detection may be performed through a constant false alarm rate (CFAR) detection unit 130 .

3 illustrates a procedure for performing CFAR detection for range profile-based detection according to the CFAR algorithm. 4 is a conceptual diagram illustrating an example of setting a threshold value for detecting a target signal from a frequency spectrum signal according to an embodiment of the present invention.

First, referring to FIG. 3 , the input vector of the CFAR algorithm may be divided into a test cell, a reference cell, and a guard cell. In this case, the test cell may be a cell to determine the presence or absence of the small UAV target, the reference cell may be a cell used for analysis of a noisy environment, and the guard cell may be a cell used to block information leaked from the test cell. As a result, the CFAR detector 130 may determine the presence or absence of a target in each test cell based on a preset first threshold value.

을 이용하여 잡음 환경 변수(

)를 추정할 수 있다. 이후 레퍼런스 셀에 분포하는 신호의 성향에 맞게 가중치

를 설정할 수 있다. 이때

는 사전에 설정한 오경보율에 의해 결정된다. 그러면 주파수 스펙트럼 신호에서 표적 신호를 구분하기 위한 임계값(제2 임계값)은 하기 수학식 5와 같이 추정된

와

를 이용하여 결정될 수 있다.Meanwhile, the order of detecting the target signal from the frequency spectrum signal is as follows. First, the reference cell data from each test cell

using the noise environment variable (

) can be estimated. Thereafter, the weights are weighted according to the propensity of the signal distributed to the reference cell.

can be set. At this time

is determined by the preset false alarm rate. Then, the threshold (second threshold) for discriminating the target signal from the frequency spectrum signal is estimated as shown in Equation 5 below.

Wow

can be determined using

Meanwhile, the CFAR detection unit 130 of the detection apparatus 10 according to an embodiment of the present invention may detect a range having a signal equal to or greater than a preset first threshold value according to the CFAR algorithm. In addition, a partial range having a signal equal to or greater than the first threshold value may be extracted as a range bin.

는 하기 수학식 6과 같이 나타낼 수 있다. 여기서

는 표적이

위치에 있을 때에, 상기 표적으로부터 반사된 탐지 신호가 수신되는 시간(

, 여기서 c는 광속) 동안의 레이더 수신 펄스 일 수 있다. In this case, the extracted range bin may be estimated to have a target, and thus the signal of the range bin in which the target is present.

can be expressed as in Equation 6 below. here

is the target

When in position, the time at which a detection signal reflected from the target is received (

, where c may be the radar reception pulse during the speed of light).

를 획득할 수 있다. To this end, the spectrum signal generator 140 extracts the signal exceeding the threshold, that is, the extracted range bin signals, that is, from the signal of Equation 6, the frequency spectrum signal as shown in Equation 7 below.

can be obtained.

는 소형 무인기의 강체로부터 수신되는 신호로서 강체의 병진운동 신호이고, 상기

신호는 소형 무인기의 날개로부터 수신되는 신호로서 l번째 로터의 병진운동 성분과 날개의 회전에 따른 미세운동 성분이 컨볼루션(convolution)된 값을 의미할 수 있다. On the other hand, in Equation 7,

is a signal received from the rigid body of the small UAV, and is a translational motion signal of the rigid body,

The signal is a signal received from the wing of the small UAV, and may mean a value obtained by convolution of a translational motion component of the l-th rotor and a fine motion component according to the rotation of the wing.

On the other hand, when only the phase information on the micro-motion component is detected in Equation 6, it can be expressed as Equation 8 below.

는

번째 탐지 신호의 파장 길이,

은

번째 레인지 프로파일에서 l번째 로터의 각속도,

는 초기 날개 위치에서 결정되는 위상을 의미함. here

Is

the wavelength length of the second detection signal,

silver

angular velocity of the lth rotor in the th range profile,

means the phase determined from the initial wing position.

Meanwhile, Equation 8 can be expressed in the form of Fourier series expansion as shown in Equation 9 below.

는 k번 째 차수의 제1종 베셀 함수(Bessel function of the first kind)를,

는

를 의미함. 그리고 N은 무인기의 날개 개수를 의미함.here

is the k-th Bessel function of the first kind,

Is

means And N stands for the number of wings of the drone.

And if Equation 9 is Fourier transform, it can be expressed as Equation 10 below.

는 하기 수학식 11과 같이 나타낼 수 있다. On the other hand, the translational motion signal received from the rigid body of the UAV

can be expressed as in Equation 11 below.

는 델타함수를,

는 컨볼루션 연산을 나타내며

는 푸리에 변환을 나타냄.

는

번째 제 1종 베셀함수이고

는

임.here

is the delta function,

represents the convolution operation.

represents the Fourier transform.

Is

The first kind of Bessel function is

Is

Lim.

텀은 주파수 도메인(domain)에서

텀으로 변환되고, 시간 도메인에서 곱은 주파수 도메인에서 컨볼루션인 성질을 이용하면 상기 수학식 7과 같이 스펙트럼 신호 생성부(140)에서 생성된 주파수 스펙트럼 신호

는, 수학식 10과 11로 나타낼 수 있다. on the time axis

Term is in the frequency domain

The frequency spectrum signal generated by the spectrum signal generator 140 as shown in Equation 7 above using the property of being converted to a term and multiplication in the time domain is convolution in the frequency domain.

can be expressed by Equations 10 and 11.

Meanwhile, when the received signal is an unmanned aerial vehicle including a fine movement signal, the control unit 100 may determine the frequency band range of the fine movement signal determined according to the wing length of the unmanned aerial vehicle as shown in Equation 2 below.

는

를, r은 무인기의 날개 길이,

는

번째 탐지 신호의 파장 길이,

는 무인기의 각속도를 의미함.here

Is

where, r is the wingspan of the drone,

Is

the wavelength length of the second detection signal,

is the angular velocity of the drone.

가 1보다 충분히 큰 경우라면 미세운동 신호는 넓은 영역에 에너지가 분포될 수 있다. 또한 미세운동 신호는 넓은 주파수 범위에 특정 주파수 간격으로 하모닉(harmonic)하게 분포될 수 있다. 여기서 하모닉 신호의 기본(fundamental) 주파수인 쵸핑 주파수(chopping frequency)는 하기 수학식 13과 같이 나타낼 수 있다. In this case the above

If is sufficiently larger than 1, the energy of the micro-motion signal can be distributed over a wide area. In addition, the micromotion signal may be harmonically distributed at specific frequency intervals in a wide frequency range. Here, the chopping frequency, which is the fundamental frequency of the harmonic signal, can be expressed as in Equation 13 below.

where N is the number of wings of the UAV.

에 해당할 수 있다. 따라서 만약 레인지 빈에 포함된 신호, 즉 표적 신호가 미세운동 성분을 포함하는 경우 델타 함수의 값이 유효한 값을 가지게 되고, 이는 병진운동 성분의 신호에 선형결합되어 보다 강한 임펄스 신호를 생성할 수 있다. Equation 12 is a frequency range of the fine component signal, and may correspond to f of the delta function shown in Equation 10. Also, the chopping frequency of Equation 13 is the delta function shown in Equation 10.

may correspond to Therefore, if the signal included in the range bin, that is, the target signal, includes a micro-motion component, the delta function has a valid value, which is linearly coupled to the signal of the translation component to generate a stronger impulse signal. .

On the other hand, when the signal included in the range bin, that is, the target signal does not include a micro-motion component, values corresponding to the wing length r of the UAV and the number N of wings of the UAV may not exist. Then, in Equation 12, the frequency range of the fine component frequency may also not exist, and the chopping frequency may also not have a value. Accordingly, the delta function shown in Equation 10 may not be calculated, and the fine component signal may also not exist. That is, if the target signal is not an unmanned aerial vehicle, only the translational motion signal of the rigid body may appear as a frequency spectrum.

)와 잡음 변수(

)에 근거하여 결정(

)될 수 있다. Accordingly, when the target signal includes a micro-motion component, an impulse having a higher value in the frequency spectrum than in the case where the target signal does not include a micro-motion component may occur in a plurality of frequency domains. Accordingly, the control unit 100 can estimate an impulse having a value greater than the threshold value of a preset size as an impulse formed due to the microcomponent, and whether the estimated number of impulses exceeds the preset number It may be finally determined whether the target signal is an unmanned aerial vehicle based on it. Here, the threshold is a preset weight (

) and the noise variable (

) based on (

) can be

Meanwhile, the preset number of impulse signals, that is, the number of threshold signals, may be determined differently according to characteristics of the UAV to be detected. For example, the number of threshold signals may have a larger value as the length of the wing increases in proportion to the number of wings of the UAV. The threshold signal number M may be determined as in Equation 14 below.

는

에 의해 결정되는 미세성분 주파수 범위 최대값으로

는

을, r은 무인기의 날개 길이,

는 탐지 신호의 파장 길이를 의미하며, 쵸핑 주파수(chopping frequency)는

이고, N은 무인기의 날개 개수를,

는 무인기의 각속도임.here

Is

to the maximum value of the fine component frequency range determined by

Is

where , r is the wingspan of the drone,

is the wavelength length of the detection signal, and the chopping frequency is

and N is the number of wings of the drone,

is the angular velocity of the drone.

Meanwhile, the memory 150 may store various data and information supporting the function of the control unit 100 . For example, the memory 150 may store information on a plurality of algorithms for processing related to a received signal, and may store values calculated according to the algorithms.

Meanwhile, FIG. 5 is a flowchart illustrating an operation process of determining whether a received radar signal is a small unmanned aerial vehicle in the detection apparatus 10 according to an embodiment of the present invention.

), 즉 레인지 프로파일(range profile)을 획득할 수 있다(S502). Referring to FIG. 5 , the detection apparatus 10 according to an embodiment of the present invention may first receive a signal from the radar through the signal receiver 110 ( S500 ). Then, the control unit 100 controls the range profile obtaining unit 120, and based on the signal obtained from the signal receiving unit 110, the radar reception pulse (

), that is, a range profile may be obtained (S502).

In this case, the range profile obtaining unit 120 may obtain the range profile from the received signal through a pulse compression (or distance compression) technique. ), the range profile obtaining unit 120 may be controlled.

Meanwhile, when the range profile is obtained in step S502, the controller 100 may control the CFAR detector 130 to primarily detect a target signal based on a preset threshold (S520). Hereinafter, in order to distinguish the preset threshold value from the threshold value of the impulse signal, a threshold value used for detecting the CFAR will be referred to as a first threshold value.

In step S520, the control unit 100 may be a step of detecting, based on the first threshold, a range in which a signal exceeding the first threshold exists as a range in which a target exists. In addition, a range in which the target exists, that is, a range bin, may be detected according to the detection result.

Meanwhile, when a range bin is detected in step S520 , the controller 100 may control the spectrum signal generator 140 to obtain a frequency spectrum signal for the detected range bin signal ( S530 ).

)과 날개에서 검출되는 신호 성분(로터의 병진운동 성분(

)과 날개의 미세운동 성분(

)을 모두 포함할 수 있다. In this case, when the target includes a rotary blade connected to the rotor, a fine component generated by the blade may be included in the frequency spectrum. Accordingly, when the target is an unmanned aerial vehicle, as shown in Equation 7 above, the signal component of the rigid body (the translational component of the rigid body,

) and the signal component (translational component of the rotor (

) and the micro-motion component of the wing (

) can be included.

)만을 포함할 수 있다. 이에 표적이 클러터인 경우, 상기 표적이 무인기인 경우에 발생하는 신호 보다 더 강한 진폭(amplitude)을 가지는 신호, 즉 더 강한 임펄스 신호가 다수의 주파수 도메인에 발생될 수 있다. On the other hand, when the target is a clutter that does not include a rotary blade, a micro-component generated by the blade may not be included in the frequency spectrum. Accordingly, the frequency spectrum of the target signal is the signal component of the rigid body (translational component of the rigid body,

) can be included. Accordingly, when the target is a clutter, a signal having a stronger amplitude than a signal generated when the target is an unmanned aerial vehicle, that is, a stronger impulse signal may be generated in a plurality of frequency domains.

)와 잡음 변수(

)에 근거하여 결정(

)될 수 있다. Accordingly, when the frequency spectrum is generated in step S530, the controller 100 may perform secondary detection to detect the number of frequencies having an amplitude (impulse) equal to or greater than a preset threshold value (S540). Here, the threshold value, that is, the second threshold value is a preset weight (

) and the noise variable (

) based on (

) can be

And when the number of detected frequencies exceeds the preset threshold signal number, the control unit 100 may determine that the target corresponding to the target signal received in step S500 is the unmanned aerial vehicle. On the other hand, when the number of detected frequencies is less than or equal to the threshold signal number, the controller 100 may determine that the target corresponding to the target signal received in step S500 is clutter.

6 to 8 show examples of the range profile detected by the detection apparatus 10 according to the embodiment of the present invention and the frequency spectrum detected when the target is a clutter and an unmanned aerial vehicle. .

Table 1 below shows environmental parameters of a radar that receives a target signal for detection of a frequency spectrum according to the present invention in FIGS. 6 to 8 below.

The following description shows examples of results measured when the unmanned aerial vehicle is allowed to fly in place at a location approximately 50 m away from the radar having the specifications shown in Table 1 in an environment in which a large number of clutter exists.

First, FIG. 6 is an exemplary diagram illustrating a range profile and a first detection result in the range profile in the detection apparatus according to an embodiment of the present invention. In FIG. 6 , a first graph 600 may mean a range profile, and a second graph 610 may mean a threshold value (first threshold value) for CFAR detection. In addition, reference numeral 650 denotes a target signal, and reference numeral 660 denotes a clutter signal.

In addition, the control unit 100 may obtain a frequency spectrum by extracting a signal exceeding a threshold value from the result of FIG. 6 . In addition, a second detection process of detecting frequencies having an amplitude exceeding a second threshold value may be performed from the obtained frequency spectrum.

First, FIG. 7 is an exemplary diagram illustrating a frequency spectrum that can be obtained when a target is an unmanned aerial vehicle, and a result of performing secondary detection on the frequency spectrum.

Referring to FIG. 7 , FIG. 7 shows a frequency spectrum 710 and a second threshold value 700 . As described above, when the target is an unmanned aerial vehicle, since the frequency spectrum further includes a fine motion component of a wing as well as a translation component of a rigid body, the second threshold value ( It can be seen that impulses having an amplitude of 700) or more are formed. In the case of FIG. 7 , 77 frequencies having an amplitude exceeding the second threshold may be detected.

In contrast, FIG. 8 is an exemplary diagram illustrating a frequency spectrum obtained when the target is a clutter and a secondary detection result for the frequency spectrum.

Referring to FIG. 8 , it can be seen that impulses having an amplitude greater than or equal to the second threshold value 700 are formed at a much smaller number of frequencies among the frequency regions of the detected range bin than in FIG. 7 . In the case of FIG. 7, the number of frequencies exceeding the second threshold value is 77, whereas in the case of FIG. 8, the number of frequencies having an amplitude exceeding the second threshold value is 33, which may be much smaller. This is because, when the target is a clutter that does not include a rotary blade, since the frequency spectrum includes only the translational motion component of the rigid body, a smaller change in amplitude for each frequency can be detected unlike FIG. 7 .

Therefore, when the number of threshold signals is determined to be about 40, when the frequency spectrum as in the case of FIG. 7 is obtained, the control unit 100 can determine the target as an unmanned aerial vehicle, and when the frequency spectrum as in the case of FIG. 8 is obtained It can be determined that the target is not an unmanned aerial vehicle. Accordingly, with respect to signals having an amplitude greater than or equal to the threshold value (the first threshold value) of the CFAR detector 130 , it is possible to accurately determine whether the target is a clutter or an unmanned aerial vehicle.

Meanwhile, in the above description of the present invention, specific embodiments have been described, but various modifications may be made without departing from the scope of the present invention.

However, those of ordinary skill in the art to which the present invention pertains will be able to make various modifications and variations without departing from the essential characteristics of the present invention. Therefore, the embodiments disclosed in the present invention are not intended to limit the technical spirit of the present invention, but to explain, and the scope of the technical spirit of the present invention is not limited by these embodiments. The protection scope of the present invention should be construed by the following claims, and all technical ideas within the scope equivalent thereto should be construed as being included in the scope of the present invention.

10: detection device
100: control unit 110: signal receiving unit
120: range profile acquisition unit 130: CFAR detection unit
140: spectrum signal generator 150: memory

Claims (10)

a signal receiver for receiving a radar signal including a target signal;
a range profile obtaining unit which obtains a range profile from the received target signal;
a constant false alarm rate (CFAR) detector configured to detect the target signal having an amplitude exceeding a preset first threshold as a range bin based on the acquired range profile;
a spectrum signal generator configured to extract a signal of the detected range bin to generate a frequency spectrum signal; and,
Based on the frequency spectrum signal, the number of frequencies having an amplitude exceeding a preset second threshold is detected, and the number of frequencies corresponding to the target signal is detected based on whether the number of detected frequencies exceeds the preset threshold signal number. A detection device comprising a control unit for determining whether the target is an unmanned aerial vehicle. According to claim 1, wherein the range profile acquisition unit,
and obtaining the range profile by performing noncoherent integration on the received radar signal. According to claim 1, wherein the spectrum signal generator,
From the signal of the range bin, the frequency spectrum signal of the target signal is generated in the form of a linear combination of a frequency spectrum component received from a rigid body of a target and a frequency spectrum component received from a wing of the target,
The frequency spectrum component received from the rigid body of the target is,
It is a frequency spectrum according to the translational motion of the rigid body of the target,
The frequency spectrum component received from the wing of the target is,
A detection device, characterized in that the component according to the translational motion of the target rotor and the component according to the fine motion of the wing are a convolutional frequency spectrum. According to claim 3, wherein the control unit,
Based on the number of frequencies having an amplitude exceeding the second threshold for each frequency of the frequency spectrum of the target signal, whether the target signal includes only a frequency spectrum component received from the rigid body of the target or the wing of the target Determining whether it further includes a frequency spectrum component received from
When the number of frequencies having an amplitude exceeding the second threshold value exceeds the threshold number of signals, the detection apparatus of claim 1, wherein the target corresponding to the target signal is determined as an unmanned aerial vehicle. 5. The method of claim 4,
The number of threshold signals is
It is determined differently depending on the characteristics of the UAV to be detected,
The characteristics of the unmanned aerial vehicle are:
A detection device comprising the number and wing length of the unmanned aerial vehicle. receiving a radar signal including a target signal;
obtaining a range profile from the received target signal;
detecting, as a range bin, the target signal having an amplitude exceeding a preset first threshold according to a constant false alarm rate (CFAR) algorithm, based on the obtained range profile;
generating a frequency spectrum signal by extracting a signal of the detected range bin;
determining a second threshold value determined based on a preset weight and a noise environment variable;
detecting, from the frequency spectrum signal, a number of frequencies for which an amplitude exceeding the second threshold is detected; and,
and determining whether a target corresponding to the target signal is an unmanned aerial vehicle based on whether the number of frequencies having an amplitude exceeding the second threshold value exceeds a preset threshold signal number. . The method of claim 6, wherein obtaining the range profile comprises:
and performing noncoherent integration on the received radar signal. The method of claim 6, wherein the generating of the frequency spectrum signal comprises:
From the signal of the range bin, the frequency spectrum signal of the target signal, the frequency spectrum component received from the rigid body of the target, and the frequency spectrum component received from the wing of the target are linearly combined to generate a frequency spectrum signal,
The frequency spectrum component received from the rigid body of the target is,
It is a frequency spectrum according to the translational motion of the rigid body of the target,
The frequency spectrum component received from the wing of the target is,
A detection method, characterized in that the component according to the translational motion of the target rotor and the component according to the fine motion of the wing are a convolutional frequency spectrum. The method of claim 6, wherein the second threshold is
Among the input vectors according to the CFAR algorithm, it is determined by the product of the noise environment variable estimated based on data included in a reference cell used for analysis of the noise environment and the preset weight. A detection method characterized. The method of claim 6, wherein the number of threshold signals,
A detection method, which is determined by the following equation, and is determined based on a wing length proportional to the number of wings of the unmanned aerial vehicle to be detected.
[Equation]

is the maximum value of the frequency range of the fine component frequency spectrum obtained from the wing of the drone,

is determined by, where

Is

and r is the wingspan of the drone,

is the wavelength length of the detection signal,

is the angular velocity of the drone. And the chopping frequency is

and N means the number of wings of the UAV.

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