KR20180122966A - Method and apparatus for identifying flight vehicle - Google Patents

Abstract

The present invention provides a method and a device for identifying a flight vehicle, which automatically estimate a length of a wing of a flight vehicle and rotation per second (RPS) by using a micro-Doppler signature and estimate the maximum Doppler frequency and a Doppler frequency in a received radar signal, thereby identifying the flight vehicle. The method for identifying a flight vehicle comprises: a step (a) of receiving the radar signal for the flight vehicle; a step (b) of converting and filtering the radar signal; a step (c) of calculating an intermediate frequency from the filtered signal, or composing the filtered signal as a spectrum image; and a step (d) of calculating the length and a speed of the wing of the flight vehicle based on the intermediate frequency or the spectrum image.

Description

TECHNICAL FIELD [0001] The present invention relates to a method and apparatus for identifying an aircraft,

The present invention relates to a flying object identification technology, and more particularly, to a flying object identification technology which is capable of detecting the shape, size, number, rotation speed, and the like of a propeller or a wing of a flying body, a rotating body such as a dron, a helicopter, And more particularly, to a method and apparatus for identifying a flying object.

As the commercialization of the drones spread rapidly, safety of the drones becomes a key issue. As a result, the size, number and rotation speed of the wing helicopter, drones, wind turbines, etc. having microwave, millimeter wave radar, Is required to be automatically calculated from radar measurement data (i.e., micro Doppler signals) to automatically determine the size, weight, and shape of the drones.

Micro-motion, such as vibration or rotation of the body of the target, may introduce frequency modulation for radar received signals, known as micro-Doppler frequency modulation. Here, the micro Doppler frequency can vary around the Doppler frequency due to the major body portion. For example, in the case of humans, the main-Doppler frequency is due to the motion of the torso. However, the micro Doppler frequency corresponds to the movement of the hands and legs. In the case of drone, the micro Doppler frequency may be due to the rotation of each blade or drones.

Recently, much effort has been devoted to analyzing micro Doppler for target recognition and classification for various applications such as helicopters, wind turbines, and the like. However, many researchers have focused only on simulation-based analysis to provide reasonable results for real case scenarios due to the presence of noise, interference by neighboring blades, complexity of blade radar cross sections, and non-ideal behavior of the blades There is a problem that can not be done.

Korean Patent No. 10-1739966

The object of the present invention is to automatically measure the size, number, and rotation speed of a wing of a measurement object using a W-band millimeter wave radar to automatically determine the size, weight, and shape of the dron, And to provide a method and apparatus for identifying a shape of a measurement object and imaging the 3D object.

An object of the present invention is to provide a flight object identification method for automatically estimating a flight wing length and a rotation speed per second (RPS) using a micro Doppler signature, estimating a maximum Doppler frequency and a Doppler frequency in the received radar signal, And apparatus.

According to a first aspect of the present invention, there is provided a method of identifying a flying object, comprising the steps of: (a) receiving a radar signal for a flying object; (b) converting and filtering the radar signal; (c) calculating an intermediate frequency from the filtered signal or analyzing a spectral image of the filtered signal; And (d) calculating the speed and length of the wing of the air vehicle based on the intermediate frequency or spectral image.

Preferably, the step (b) includes the steps of: converting the radar signal into a digital signal; Filtering the converted digital signal with a band-pass filter for a specific band; And calculating a spectrum by time-frequency-transforming the filtered signal.

Preferably, the step (c) includes the steps of: (c-1) calculating a maximum Doppler frequency from the filtered signal; (c-2) calculating a time-frequency spectral function from the filtered signal; (c-3) calculating an intermediate frequency using the maximum Doppler frequency and the time-frequency spectrum function; (c-4) calculating a peak frequency using the intermediate frequency; And (c-5) calculating an average time difference between the calculated peak frequencies.

Advantageously, the step (c-1) includes the steps of performing FFT on the filtered signal; Calculating a peak envelope from the fast Fourier transformed signal; Differentiating the peak envelope; And calculating the maximum Doppler frequency using the finely divided peak envelope.

Preferably, the step (c-2) includes the steps of: performing a short-time Fourier transform on the filtered signal; And calculating a time-frequency spectral function representing the short-time Fourier transformed spectrogram.

Preferably, the step (c-3) includes the steps of: calculating a power spectral density from the time-frequency spectral function; And calculating an intermediate frequency having a specific frequency width using the power spectral density and the maximum Doppler frequency.

Preferably, the step (d) includes: calculating the number of revolutions per second of the wing based on an average time difference between the peak frequencies; Calculating a speed of the wing based on the number of rotations per second; And calculating the length of the wing based on the speed of the wing.

Preferably, the step (c) includes the steps of: (c-1) short-time Fourier transforming the filtered signal to calculate a time-frequency spectrum function; (c-2) constructing a spectral image based on the time-frequency spectral function; (c-3) calculating a maximum Doppler frequency using the spectral image; (c-4) filtering and flattening the signal based on the maximum Doppler frequency; And (c-5) calculating a main frequency by FFT-transforming the flattened signal.

Preferably, the step (c-3) includes the steps of: calculating a time-frequency edge from the spectral image; Calculating a peak frequency from an edge value having a maximum frequency value among the calculated edges; And calculating the maximum Doppler frequency by summing all the edge values having the maximum frequency value among the calculated edges.

Preferably, the step (d) includes: calculating the number of revolutions per second of the wing based on the main frequency and the number of wings of the air vehicle; And calculating the length of the vane based on the number of revolutions per second and the maximum Doppler frequency.

According to a second aspect of the present invention, there is provided a radar system including: a radar signal receiving unit for receiving a radar signal for a vehicle; A radar signal converting unit for converting and filtering the radar signal; A signal processing unit for calculating an intermediate frequency from the filtered signal or analyzing a spectral image of the filtered signal; And a flight identification unit for calculating a speed and a length of the wing of the airplane based on the intermediate frequency or spectral image.

Preferably, the signal processing unit further comprises an intermediate frequency calculating module, wherein the intermediate frequency calculating module calculates a maximum Doppler frequency from the filtered signal, calculates a time-frequency spectral function from the filtered signal, The intermediate frequency may be calculated using the maximum Doppler frequency and the time-frequency spectrum function, the peak frequency may be calculated using the intermediate frequency, and the average time difference between the calculated peak frequencies may be calculated.

Preferably, the airplane identification unit may calculate the number of revolutions per second of the wing based on the average time difference between the peak frequencies, calculate the speed of the wing based on the number of revolutions per second, Can be calculated.

Preferably, the signal processing unit further includes a spectral image analysis module, wherein the spectral image analysis module calculates a time-frequency spectrum function by performing a short-time Fourier transform on the filtered signal, The maximum Doppler frequency is calculated using the spectrum image, the signal is filtered and planarized on the basis of the maximum Doppler frequency, and the main frequency can be calculated by fast Fourier transforming the flattened signal have.

Preferably, the flying object identification unit calculates the number of revolutions per second of the wing based on the main frequency and the number of wings of the air vehicle, and calculates the length of the wing based on the number of revolutions per second and the maximum Doppler frequency .

As described above, according to the present invention, it can be utilized as a safe operation monitoring system for an unmanned variable flying vehicle, a drone, and the like. When the rotational speed of a rotary vane can not be directly measured, such as a wind turbine or a turbine, And it can be used as a fault diagnosis system for normal operation or not, and can be used as a monitoring and diagnosis device for all other rotating blade objects.

1 and 2 are diagrams for explaining the relationship between the Doppler frequency and the rotation of the wing.
3 is a block diagram illustrating an aircraft identification apparatus according to a preferred embodiment of the present invention.
4 is a flowchart illustrating a method of identifying a flying object according to a preferred embodiment of the present invention.
5 is a flowchart illustrating an air vehicle identification method according to an embodiment.
FIGS. 6 to 10 are diagrams for explaining the air vehicle identification method shown in FIG. 5. FIG.
11 is a flowchart illustrating a method of identifying a flying object according to another embodiment.
12 and 13 are diagrams for explaining the air vehicle identification method shown in FIG.

BRIEF DESCRIPTION OF THE DRAWINGS The advantages and features of the present invention and the manner of achieving them will be more apparent from the following detailed description taken in conjunction with the accompanying drawings. The present invention may, however, be embodied in many different forms and should not be construed as being limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art. Is provided to fully convey the scope of the invention to those skilled in the art, and the invention is only defined by the scope of the claims. Like reference numerals refer to like elements throughout the specification. &Quot; and / or " include each and every combination of one or more of the mentioned items.

In each step, the identification code (e.g., a, b, c, etc.) is used for convenience of explanation, the identification code does not describe the order of each step, Unless otherwise stated, it may occur differently from the stated order. That is, each step may occur in the same order as described, may be performed substantially concurrently, or may be performed in reverse order.

The terminology used herein is for the purpose of illustrating embodiments and is not intended to be limiting of the present invention. In the present specification, the singular form includes plural forms unless otherwise specified in the specification. It is noted that the terms "comprises" and / or "comprising" used in the specification are intended to be inclusive in a manner similar to the components, steps, operations, and / Or additions.

Unless defined otherwise, all terms (including technical and scientific terms) used herein may be used in a sense commonly understood by one of ordinary skill in the art to which this invention belongs. Also, commonly used predefined terms are not ideally or excessively interpreted unless explicitly defined otherwise.

In the following description of the present invention, detailed description of known functions and configurations incorporated herein will be omitted when it may make the subject matter of the present invention rather unclear. The following terms are defined in consideration of the functions in the embodiments of the present invention, which may vary depending on the intention of the user, the intention or the custom of the operator. Therefore, the definition should be based on the contents throughout this specification.

For a Doppler frequency, the conversion or shift of the frequency due to the relative radial velocity is given by Equation 1 below.

[Formula 1]

는 도플러 주파수이고,

는 송신된 신호의 파장이고,

는 타겟의 상대속도이고,

는 신호의 입사각이다. From here,

Is the Doppler frequency,

Is the wavelength of the transmitted signal,

Is the relative velocity of the target,

Is the incident angle of the signal.

The Doppler effect causes periodic modulation of the received radar signal as shown in FIG. The positions of the wings Pi (i = 1, 2, ..., 5) may be associated with corresponding Doppler frequencies of the received signal. For example, simulation results for the microdoppler signatures of two rotor rotors and three rotor rotors are as shown in FIG. From Figure 2 it can be seen that the speed of the rotor tip is directly related to blade length and revolutions per second (RPS). The information about the rotating wings can be obtained by analyzing the micro Doppler signatures and the position of the maximum Doppler frequency and peak from the micro Doppler signature of the received radar signal, i.e., a spectrogram, The number of revolutions per second of the wing and the wing can be estimated. Hereinafter, a method and apparatus for identifying a flying object according to the present invention will be described.

3 is a block diagram illustrating an aircraft identification apparatus according to a preferred embodiment of the present invention.

3, the object identifying apparatus 100 includes a radar signal receiving unit 110, a radar signal converting unit 120, a signal processing unit 130, a flying object identifying unit 140, and a controller 150. The control unit 150 controls the operations of the radar signal receiving unit 110, the radar signal converting unit 120, the signal processing unit 130, and the flying object identifying unit 140 and the flow of data. Hereinafter, referring to FIG. 4, a method of identifying a flying object performed by the flying object identifying apparatus 100 will be described in detail.

Referring to FIG. 4, the radar signal receiving unit 110 receives a radar signal for the air vehicle (step S410). Preferably, the received radar signal corresponds to a baseband signal.

The radar signal converting unit 120 converts and filters the radar signal (step S420). Preferably, the radar signal converter 120 converts the radar signal into a digital signal by an analog-digital converter and outputs the radar signal to a frequency higher than the maximum Doppler frequency to remove the DC signal and the high- Filtering is performed using a band-pass filter.

The radar signal converting unit 120 time-frequency-converts the filtered signal to calculate a spectrum. Preferably, the time-frequency-converted spectrum after being converted into the digital signal can be expressed by the following [Equation 2], and the signal filtered by the band-pass filter can be expressed as [Equation 3] below.

[Formula 2]

은 레이다 신호, n은 시간 인덱스, N은 FFT 크기, k는 주파수 인덱스,

는 윈도우(window) 함수에 해당한다.From here,

N is a time index, N is an FFT size, k is a frequency index,

Corresponds to a window function.

[Formula 3]

Here, N corresponds to a length of data sample.

The signal processing unit 130 calculates an intermediate frequency from the filtered signal or configures the filtered signal into a spectral image (step S430). The flying object identification unit 140 calculates the velocity and length of the wing of the air vehicle on the basis of the intermediate frequency or spectral image (step S440).

Preferably, the signal processing unit 130 may further include an intermediate frequency calculation module and a spectrum image analysis module. In one embodiment, the intermediate frequency calculation module calculates the intermediate frequency from the filtered signal, and calculates the speed and length of the wing of the airplane based on the intermediate frequency calculated from the airplane identification unit 140, Will be described with reference to FIG. In another embodiment, the spectral image analysis module constructs a spectral image of the filtered signal so that the velocity and length of the wing of the air vehicle are calculated based on the spectral image from the air body identification unit 140, .

First, a method of identifying a vehicle based on the intermediate frequency will be described.

Referring to FIG. 5, the intermediate frequency calculation module calculates a maximum Doppler frequency from the filtered signal (step S431-1). More specifically, the intermediate frequency calculation module calculates a frequency-power spectrum by performing Fast Fourier Transform (FFT) on the filtered signal, and calculates a peak envelope in a fast Fourier transformed signal, that is, a spectrum signal . Here, the peak envelope corresponds to the envelope of the peak value corresponding to the peak in the spectral signal. For example, referring to FIG. 6, as a result of spectral and peak envelope calculation using actual radar measurement data, the graph shown in blue in FIG. 6 shows the spectrum and the graph in yellow dotted line shows the peak envelope.

The intermediate frequency calculation module then differentiates the peak envelope. The derivative of the envelope is used to estimate the frequency location where there is a large difference in the power level of the reflected signal and the noise floor and the minima of the differential coefficient are used to determine the most steep negative slope of the envelope Location. Since the accuracy of the calculated maximum Doppler frequency is dependent on the signal-to-noise ratio (SNR) of the received radar signal and the spikes of the spectrum, the present invention uses a median filter to remove spikes while determining the envelope of the spectrum will be.

)를 산출하고, 미분(

)하면, 아래의 [식 4]와 같이 나타낼 수 있다.Preferably, the intermediate frequency calculation module derives a peak envelope (eq.

), And the differential (

), It can be expressed as [Equation 4] below.

[Formula 4]

)는 [식 4]로부터 아래의 [식 5]와 같이 산출될 수 있다.The intermediate frequency calculation module can calculate the maximum Doppler frequency using the differentiated peak envelope and the maximum Doppler frequency calculated by differentiating the peak envelope based on the actual radar measurement data, for example, as shown in FIG. 7 . Preferably, the maximum Doppler frequency (

) Can be calculated from [Equation 4] as Equation 5 below.

[Formula 5]

The intermediate frequency calculation module calculates a time-frequency spectrum function from the filtered signal in step S410 (step S432-1). More specifically, the intermediate frequency calculation module performs a short-time Fourier transform (STFT) on the filtered signal in step S410 and calculates a time-frequency spectrum function representing a short-time Fourier transformed spectrogram. The maximum Doppler shift corresponds to the length of the wing since the tip of the wing that rotates when the incident light is perpendicular to the blade, i.e. the wing, has a maximum velocity. Since the wings rotating at high speed have a Doppler shift that is larger than the main body (for example, frame) of the flying body, the Doppler shift due to the movement of the flying body can be ignored, That is, a spectrogram. For example, the spectrogram calculated on the basis of the actual radar measurement data can be represented as shown in FIG.

)는 단시간 푸리에 변환을 이용하여 시간(n)-주파수(k)로 아래의 [식 6]과 같이 표현될 수 있다.Preferably, the radar signal received in step < RTI ID = 0.0 > S410 &

) Can be expressed by the following equation (6) at time (n) -frequency (k) using the short-time Fourier transform.

[Formula 6]

은 길이 M의 윈도우(window) 함수, K는 샘플링 포인트의 수를 나타낸다.From here,

Is a window function of length M, and K is the number of sampling points.

The intermediate frequency calculation module calculates the intermediate frequency using the maximum Doppler frequency and the time-frequency spectrum function (step S433-1). More specifically, the intermediate frequency calculation module calculates the power spectral density (PSD) from the time-frequency spectrum function. Preferably, the power spectral density can be calculated from Equation (6) as Equation (7) below.

[Equation 7]

는 샘플링 주파수, N은 전체 샘플 개수를 나타낸다.From here,

Is the sampling frequency, and N is the total number of samples.

)를 산출하며, 최대 도플러 주파수(

)를 중심으로

부터

구간에서 주파수폭은

로 아래의 [식 8]을 만족하도록 중간 주파수가 산출된다. 여기에서, 중간 주파수는 시간 함수이며, 다수개의 첨두(peak)를 가지는 마이크로 도플러 주파수 신호를 포함하고 있다.The intermediate frequency calculation module calculates an intermediate frequency having a specific frequency width, using the power spectral density and the maximum Doppler frequency. More specifically, the intermediate frequency calculation module calculates the intermediate frequency from the spectrum shape to the intermediate frequency (media frequency)

), And calculates the maximum Doppler frequency (

Centering on

from

In the interval,

The intermediate frequency is calculated so as to satisfy [Expression 8] below. Here, the intermediate frequency is a time function and includes a micro Doppler frequency signal having a plurality of peaks.

[Equation 8]

)는 도플러 주파수의 주기와 연관되고, 바람직하게, 아래의 [식 9]와 같이 산출될 수 있다.The intermediate frequency calculation module calculates the peak frequency using the intermediate frequency (step S434-1). Here, the peaks frequency corresponds to the local maximum value of the periodic micro Doppler frequencies present in the micro Doppler signature of the radar signal,

) Is related to the period of the Doppler frequency, and can be calculated as shown in the following Equation (9).

[Equation 9]

은 첨두 신호 추출을 위한 윈도우(window) 함수이다.From here,

Is a window function for peak signal extraction.

)는 아래의 [식 10]과 같이 산출될 수 있고, 시간차 주기에 대한 평균 시간차(

)는 아래의 [식 11]과 같이 산출될 수 있다.The intermediate frequency calculation module calculates an average time difference between the calculated peak frequencies (step S435-1). Since the peak frequencies are not equal to the maximum Doppler frequency and each window has only one peak frequency, the total number of peak frequencies is equal to the number of windows M. The window size depends on the type of aircraft (e.g., two wings or three wings) and the rotational speed of the rotor, and the time corresponding to each peak frequency provides information about the rotation of the wing, The time difference period between the two peak frequencies generated when the flight wing rotates

) Can be calculated as shown in the following [Equation 10], and the average time difference (

) Can be calculated as the following Equation (11).

[Equation 10]

[Equation 11]

The flying object identifying unit 140 calculates the number of revolutions per second of the wing based on the average time difference between the peak frequencies (step S441-1). For example, referring to FIG. 9, calculation results of an intermediate frequency, a peak frequency, a peak frequency period, and a peak frequency average period are shown as spectrocograms calculated using actual radar measurement data. In FIG. 9, the solid line indicates the intermediate frequency, the rhombus indicates the peak frequency, and T _diff indicates the peak frequency period.

)는 아래의 [식 12]와 같이 산출될 수 있다.Preferably, the rotation speed per second (RPS) of the wing (

) Can be calculated as shown in [Equation 12] below.

[Equation 12]

)은 아래의 [식 13]과 같이 산출될 수 있고, [식 13]은 최대 도플러 주파수(

)에 대하여 [식 14]와 같이 표현될 수 있다.The flying object identifying unit 140 calculates the speed of the wing based on the number of rotations per second (step S442-1). Preferably, the tip velocity (

) Can be calculated as [Expression 13], and [Expression 13] can be calculated as the maximum Doppler frequency

) Can be expressed as [Equation 14].

[Formula 13]

[Equation 14]

는 송신 신호의 파장을 나타낸다.From here,

Represents the wavelength of the transmission signal.

The flying object identification unit 140 calculates the length of the wing based on the speed of the wing (step S443-1). Preferably, the length of the wing can be calculated by the following equation (15) using [Expression 13] and [Expression 14].

[Formula 15]

For example, referring to FIG. 10, there are shown measured results according to the present invention, wherein the error rate (Error) between the measured length and the actual length of the drone's wings and the measured rotation per second Calculated RPS. Referring to FIG. 10, it can be seen that the accuracy of the length of the blades measured according to the present invention is 96% or more.

Next, a method of identifying the air vehicle based on the spectral image will be described.

)의 단시간 푸리에 변환된 시간(n)-주파수(k) 스펙트럼 함수는 아래의 [식 16]과 같이 표현될 수 있다.Referring to FIG. 11, the spectral image analysis module calculates a time-frequency spectrum function by short-time Fourier transforming the filtered signal (step S431-2). Preferably, the radar signal (

(N) -frequency (k) spectrum function of the short-time Fourier transformed time (n) can be expressed as [Equation 16] below.

[Formula 16]

은 길이 M의 윈도우(window) 함수, K는 샘플링 개수에 해당한다.From here,

Is a window function of length M, and K is the number of samples.

)을 스펙트럼 영상으로 출력할 수 있고, 여기에서, 스펙트럼 영상은 시간-주파수의 영상으로서, 예를 들어, 도 12에 도시된 Gray scale image와 같이 출력될 수 있다.The spectral image analysis module constructs a spectral image based on the time-frequency spectral function (step S432-2). Preferably, the spectral image analysis module comprises a spectrogram (expressed as < RTI ID = 0.0 >

) As a spectral image, wherein the spectral image can be output as a time-frequency image, for example, a gray scale image shown in FIG.

The spectral image analysis module calculates the maximum Doppler frequency using the spectral image (step S433-2). More specifically, the image analysis module calculates a time-frequency edge from the spectral image, and an edge can be calculated, for example, as shown in Edge detection in FIG.

The image analysis module calculates the peak frequency from the edge value having the maximum frequency value among the calculated edges, and calculates the maximum Doppler frequency by summing all the edge values having the maximum frequency value as shown in [Equation 17] below.

[Formula 17]

는 주파수 계열(frequency series),

는 최대 주파수 포인트(maximum frequency points),

는 시간축에서 픽셀(pixel)의 개수,

는 주파수 축에서 픽셀의 개수에 해당한다. 예를 들어, 최대 주파수 포인트는 도 12의 Maximum frequency points에 도시된 바와 같다.From here,

Is a frequency series,

Is the maximum frequency points,

The number of pixels in the time axis,

Corresponds to the number of pixels in the frequency axis. For example, the maximum frequency point is as shown in the Maximum frequency points in FIG.

The spectral image analysis module filters and flattens the signal based on the maximum Doppler frequency (step S434-2) and may be filtered, for example, as shown in the frequency index of FIG.

)의 산출 결과는 도 12의 Dominant frequency에 도시된 바와 같다.The spectral image analysis module performs a fast Fourier transform on the flattened signal to calculate a main frequency (step S435-2), for example, a dominant frequency (

) Are as shown in the dominant frequency in Fig.

로 하여 날개의 초당 회전수가 산출될 수 있다.The flying object identification unit 140 calculates the number of revolutions per second of the wing based on the number of wings of the main frequency and the flying object (step S441-2). Preferably, the number of revolutions per second of the wing can be calculated as in the following [Eq. 18]. For example, when the air vehicle has two wings as shown in Fig. 13,

The number of revolutions per second of the wing can be calculated.

[Formula 18]

Here, BN corresponds to the blade number of the blade.

The flying object identifying unit 140 calculates the length of the wing based on the rotation speed per second and the maximum Doppler frequency (step S442-2). Preferably, the length of the wing can be calculated as: " (19) "

19]

는 송신 주파수 파장에 해당한다.From here,

Corresponds to the transmission frequency wavelength.

Meanwhile, the air vehicle identification method according to an embodiment of the present invention can also be implemented as a computer-readable code on a computer-readable recording medium. A computer-readable recording medium includes all kinds of recording apparatuses in which data that can be read by a computer system is stored.

For example, the computer-readable recording medium includes a ROM, a RAM, a CD-ROM, a magnetic tape, a hard disk, a floppy disk, a removable storage device, a nonvolatile memory, , And optical data storage devices.

In addition, the computer readable recording medium may be distributed and executed in a computer system connected to a computer communication network, and may be stored and executed as a code readable in a distributed manner.

Although the preferred embodiments of the method and apparatus for identifying a flying object according to the present invention have been described above, the present invention is not limited thereto, and various modifications may be made within the scope of the claims, And this also belongs to the present invention.

100: Flight identification device
110: Radar signal receiver
120: Radar signal conversion unit
130: Signal processor
140:
150:

Claims (16)

(a) receiving a radar signal for a vehicle;
(b) converting and filtering the radar signal;
(c) calculating an intermediate frequency from the filtered signal or analyzing a spectral image of the filtered signal; And
(d) calculating the speed and length of the wing of the airplane based on the intermediate frequency or spectral image.
The method of claim 1, wherein the step (b)
Converting the radar signal into a digital signal;
Filtering the converted digital signal with a band-pass filter for a specific band; And
And calculating a spectrum by time-frequency-transforming the filtered signal.
The method of claim 1, wherein the step (c)
(c-1) calculating a maximum Doppler frequency from the filtered signal;
(c-2) calculating a time-frequency spectral function from the filtered signal;
(c-3) calculating an intermediate frequency using the maximum Doppler frequency and the time-frequency spectrum function;
(c-4) calculating a peak frequency using the intermediate frequency; And
(c-5) calculating an average time difference between the calculated peak frequencies.
4. The method of claim 3, wherein the step (c-1)
Performing fast Fourier transform on the filtered signal;
Calculating a peak envelope from the fast Fourier transformed signal;
Differentiating the peak envelope; And
And calculating the maximum Doppler frequency using the differentiated peak envelope.
4. The method of claim 3, wherein the step (c-2)
Short-time Fourier transforming the filtered signal; And
Calculating a time-frequency spectral function representing the short-time Fourier transformed spectrogram.
4. The method of claim 3, wherein the step (c-3)
Calculating a power spectral density from the time-frequency spectral function; And
And calculating an intermediate frequency having a specific frequency width using the power spectral density and the maximum Doppler frequency.
4. The method of claim 3, wherein step (d)
Calculating a number of revolutions per second of the wing based on an average time difference between the peak frequencies;
Calculating a speed of the wing based on the number of rotations per second; And
And calculating the length of the wing based on the speed of the wing.
The method of claim 1, wherein the step (c)
(c-1) calculating a time-frequency spectrum function by short-time Fourier transforming the filtered signal;
(c-2) constructing a spectral image based on the time-frequency spectral function;
(c-3) calculating a maximum Doppler frequency using the spectral image;
(c-4) filtering and flattening the signal based on the maximum Doppler frequency; And
(c-5) calculating a main frequency by performing a fast Fourier transform on the flattened signal.
9. The method of claim 8, wherein the step (c-3)
Calculating an edge of time-frequency from the spectral image;
Calculating a peak frequency from an edge value having a maximum frequency value among the calculated edges; And
And calculating the maximum Doppler frequency by summing all the edge values having the maximum frequency value among the calculated edges.
9. The method of claim 8, wherein step (d)
Calculating a number of revolutions per second of the wing based on the main frequency and the number of wings of the air vehicle; And
And calculating the length of the wing based on the number of rotations per second and the maximum Doppler frequency.
A radar signal receiving unit for receiving a radar signal for the air vehicle;
A radar signal converting unit for converting and filtering the radar signal;
A signal processing unit for calculating an intermediate frequency from the filtered signal or analyzing a spectral image of the filtered signal; And
And a flying object identification unit for calculating a velocity and a length of the wing of the object based on the intermediate frequency or the spectral image.
The signal processing apparatus according to claim 11,
Further comprising an intermediate frequency calculation module,
The intermediate frequency calculation module calculates a maximum Doppler frequency from the filtered signal, calculates a time-frequency spectral function from the filtered signal, and calculates an intermediate frequency using the maximum Doppler frequency and the time- Calculates a peak frequency using the intermediate frequency, and calculates an average time difference between the calculated peak frequencies.
13. The airbag device according to claim 12,
Calculating the number of revolutions per second of the wing based on the average time difference between the peak frequencies, calculating the speed of the wing based on the number of revolutions per second, and calculating the length of the wing based on the speed. Aircraft identification device.
The signal processing apparatus according to claim 11,
Further comprising a spectral image analysis module,
The spectral image analysis module calculates a time-frequency spectrum function by short-time Fourier transforming the filtered signal, forms a spectrum image based on the time-frequency spectrum function, and calculates a maximum Doppler frequency using the spectrum image Wherein the signal is filtered and planarized on the basis of the maximum Doppler frequency, and the main frequency is calculated by performing a fast Fourier transform on the flattened signal.
15. The airbag device according to claim 14,
Calculates the number of revolutions per second of the wing based on the main frequency and the number of wings of the airplane, and calculates the length of the wing based on the number of revolutions per second and the maximum Doppler frequency.
A computer-readable recording medium having recorded thereon a computer program for executing the method according to any one of claims 1 to 10.

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