KR101627612B1 - Efficient Method and Apparatus of Generating Inverse Synthetic Aperture Radar Image of Multiple Targets Using Flight Trajectory and Morphological Processing - Google Patents

Abstract

The present invention relates to radar technology, and more particularly, to a method and system for radar technology, and more particularly, to a method and system for accurately segmenting a multi-target inverted synthetic aperture radar image in flight, And more particularly to a method and apparatus for multi-target inverse synthetic aperture radar image formation.

Description

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a method and an apparatus for forming an effective multi-target inverted synthetic radar image using a flight trajectory and a morphological process,

The present invention relates to radar technology, and more particularly, to a method and system for radar technology, and more particularly, to a method and system for accurately segmenting a multi-target inverted synthetic aperture radar image in flight, And more particularly to a method and apparatus for multi-target inverse synthetic aperture radar image formation.

At present, Korea is facing a serious threat due to the development of RCS (Radar Cross Section) fighters and missiles in neighboring countries. Therefore, it is necessary to develop various radar target identification techniques in order to cope with such high-performance weapons.

To this end, various radar target identification techniques are used. Among them, the most widely used technique is a reverse synthesized aperture radar image which shows the scattering point distribution of the target in the two dimensional (distance, Doppler) region.

Currently, most of the inverse synthetic aperture radar imaging techniques are limited to single targets and provide high segmentation performance. However, when multiple targets are present in one radar beam, there is a weak point that the quality of the inverse synthetic aperture radar image is reduced. Particularly, in real world, the target is flight plane, so it is necessary to separate the images of these multiple aircraft.

In case of the general distance alignment technique for dividing the inverse synthetic aperture radar image, serious degradation of the image occurs if the actual trajectory is not similar to the trajectory model.

In addition, in the case of a general segmentation technique, a phenomenon occurs in which the target is cut off according to the size of the segmented window, so that the target can not be segmented appropriately. Therefore, it is necessary to develop a method that can form multi - target inverted synthetic aperture images.

1. Korean Patent Publication No. 10-2013-0081527 2. Korean Patent Publication No. 10-2004-0094439

1. Kwon Kyungil et al., "Radar Target Analysis and Software Development Using RCS / ISAR", Journal of Military Science and Technology, Vol.7 No.17 pp.88-99, 2004.

The present invention has been proposed in order to solve the problems according to the above background art, and it is possible to accurately arrange the distance of the multi-target inverted synthetic aperture radar image in flight in real time, and to use the multi-target ISAR (Inverse Synthetic Aperture Radar) And more particularly, to a method and apparatus for forming a multi-target inverse synthetic aperture radar image which enables successful division of an image.

In order to achieve the above-mentioned object, the present invention is capable of real-time accurate distance alignment of a multi-target inverted synthetic aperture radar image for flight flight, and can be used for successful division of a multi-target ISAR (Inverse Synthetic Aperture Radar) image The present invention provides a method of forming a multi-target, inverse synthesized aperture radar image.

The multi-target reverse synthesized opening surface radar image forming method includes:

a) forming distance side view binarized images for each street side view of multiple targets flying the flight;

b) forming a center of gravity of the distance side view in each binarized image using the distance side view binarized images;

c) performing distance alignment on the distance side view binarized images using the multi-target flight trajectory;

d) performing a phase correction on the binarized images to form a primary initial full image;

e) forming a secondary binarized full image from the initial full image using a constant false alarm rate (CFAR) detector; And

and f) extracting and segmenting the individual binarized images through expansion and erosion of the binarized whole image to generate divided images.

In this case, the step f) includes: performing vertical inverse Fourier transform on the divided image; Performing swing compensation using a one-dimensional entropy and a two-dimensional entropy in a vertically inverse Fourier transformed divided image; And performing a vertical direction Fourier transform on the shake compensated divided image to obtain a final image.

The flight trajectory modeling may be performed using a polynomial and a Gaussian function.

%의 거리성분을 1로, 나머지는 0으로 설정되는 것을 특징으로 할 수 있다.In addition, the binarized image is the largest

% Is set to 1, and the rest is set to zero.

(여기서,

은

번째 이진 거리측면도이고,

은 거리성분의 개수이다)에 의해 산출되는 것을 특징으로 할 수 있다.Also, the center of gravity is a center-of-gravity curve of a binary distance side view in each binarized image, and the center-

(here,

silver

Lt; th > binary distance side view,

Is the number of distance components).

(여기서,

는 거리측면도 번호,

는 다항식 계수,

은 다항식 차수,

는 가우스 함수 계수,

는 가우스 함수 중심,

는 가우스함수 표준편차이다)으로 표현되는 것을 특징으로 할 수 있다.The polynomial and the Gaussian function may be expressed by the following equations

(here,

The street side view number,

Is a polynomial coefficient,

Is a polynomial order,

Is the Gaussian function coefficient,

Centered on the Gaussian function,

Is a Gaussian function standard deviation).

Also, the binarized whole image estimates the mean and variance of the clutter around the point of the initial binarization whole image, and is set to 1 if the predetermined false alarm probability (CFAR) detector is greater than or equal to a predetermined value, Is set.

The step (f) may include forming the binary image as a disc-shaped image; And enlarging or eroding the binary image using the intersection of the binary image and the disk image.

On the other hand, another embodiment of the present invention forms distance side view binarized images for each distance side view of multiple targets that are flying in a flight, and uses the distance side view binarized images to form the center of gravity of the distance side view in each binarized image ; A distance alignment unit for performing distance alignment on the distance side view binarized images using the multi-target flight trajectory; Distance-based distance-side-view binarized images are subjected to phase correction to form a primary initial full image, and a secondary binarization from the initial full image is performed using a constant false alarm rate (CFAR) A binarization image forming unit for forming an image; And a division unit for extracting and segmenting individual binarized images through expansion and erosion of the binarized whole image to generate an effective multi-target reciprocal synthesized aperture image using the trajectory and morphological processing. A radar image forming apparatus is provided.

According to the present invention, it is possible to perform accurate real-time alignment of the multi-target inverted-aperture radar image in real time.

Another advantage of the present invention is that successful division of a multi-target Inverse Synthetic Aperture Radar (ISAR) image using such real-time accurate distance alignment is possible.

In addition, another advantage of the present invention is that it is possible to divide a target within a SAR (Synthetic Aperture Radar) image and / or another optical image by applying a segmentation technique.

1 is a diagram showing a concept of a time-domain signal of a general chirp signal.
2 is a diagram showing a concept of a frequency domain signal of a general chirp signal.
3 is a diagram showing a general matched filtering concept.
4 is a flow chart illustrating a general multi-target inverse synthetic aperture radar spiritual formation process.
5 is a conceptual diagram of a street side view that is not properly aligned in the case of a general method.
6 is a conceptual diagram of a cost function used for PS0 for general distance alignment.
7 is a conceptual diagram for calculating the center of gravity of a binarized image existing within a window of a general target size.
8 is a block diagram of an effective multi-target inverse synthesized opening surface radar image forming apparatus 800 using flight trajectory and morphological processing according to an embodiment of the present invention.
FIG. 9 is a flowchart illustrating a process of forming an effective multi-target inverted synthesized opening radar image using a flight path and morphological processing according to an embodiment of the present invention.
FIG. 10 is a conceptual diagram showing a shape of a disk shape used for image expansion and erosion according to an embodiment of the present invention.
11 is a conceptual diagram showing an image expansion according to an embodiment of the present invention.
12 is a conceptual diagram showing eroding an image according to an embodiment of the present invention.

While the invention is susceptible to various modifications and alternative forms, specific embodiments thereof are shown by way of example in the drawings and will herein be described in detail. It is to be understood, however, that the invention is not to be limited to the specific embodiments, but includes all modifications, equivalents, and alternatives falling within the spirit and scope of the invention.

Like reference numerals are used for similar elements in describing each drawing.

The terms first, second, etc. may be used to describe various components, but the components should not be limited by the terms. The terms are used only for the purpose of distinguishing one component from another.

For example, without departing from the scope of the present invention, the first component may be referred to as a second component, and similarly, the second component may also be referred to as a first component. The term "and / or" includes any combination of a plurality of related listed items or any of a plurality of related listed items.

Unless otherwise defined, all terms used herein, including technical or scientific terms, have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

Terms such as those defined in commonly used dictionaries are to be interpreted as having a meaning consistent with the contextual meaning of the related art and are to be interpreted as either ideal or overly formal in the sense of the present application Should not.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, a method and an apparatus for effectively generating a multi-target inverse synthetic aperture radar image using a flight path and a morphological process according to an embodiment of the present invention will be described in detail with reference to the accompanying drawings.

Before describing the present invention, the concept of the distance side view and the inverse synthesized opening image formation of the stopped target will be described as a basic theory.

The most widely used signal in the formation of a high-resolution street side view is a chirp signal. The chirp signal is a signal that is up-chirped or down-chirped with time. This signal is mainly used for sonar and radar, but it is used in various fields including broadband communication. The chirp signal can be expressed by the following equation.

는 송신된 송신 처프 신호,

는 표적으로부터 반사된 반사 처프 신호,

,

,

는 각각 시작 주파수, 밴드폭, 사각펄스를 나타낸다. 또한,

는 각각 펄스 지속시간, 레이더 시선방향 투영 거리로 인한 시간지연, 송신 처프의 진폭, 수신 처프의 진폭을 나타낸다. here,

Transmitted chirp signal,

A reflected chirp signal reflected from the target,

,

,

Respectively represent a start frequency, a bandwidth, and a square pulse. Also,

Represent the pulse duration, the time delay due to the radial line-of-sight projection distance, the amplitude of the transmission chirp, and the amplitude of the reception chirp, respectively.

1 and 2 show signals in the time domain and the frequency domain of the chirp signal, respectively.

In the case of a target composed of K scattering sources, the radar reflection signal can be modeled as follows.

는

번째 산란원의 크기이고,

는

번째 산란원의 레이더 시선방향으로 투영된 거리로 인한 시간지연을 나타낸다.

대의 표적이 편대비행 할 경우, 위 신호는 다음과 같이 표현될 수 있다.here,

The

The size of the first scattering circle,

The

Lt; RTI ID = 0.0 > scattering circle. ≪ / RTI >

When the target of the target is flying the flight, the above signal can be expressed as:

는

번째 표적의

번째 산란원의 크기이고,

는

번째 표적의

번째 산란원의 레이더 시선방향으로 투영된 거리로 인한 시간지연을 나타낸다. 이러한 처프 신호를 이용하여 고해상도 거리 측면도를 형성하기 위해서는 정합 필터링 과정이 요구된다. 이를 보여주는 도면이 도 3에 도시된다.here,

The

Of the second target

The size of the first scattering circle,

The

Of the second target

Lt; RTI ID = 0.0 > scattering circle. ≪ / RTI > A matched filtering process is required to form a high-resolution distance side view using such a chirp signal. A diagram showing this is shown in Fig.

The matched filtering is a process of convoluting the received signal reflected from the target with the conjugate value of the symmetric signal on the time axis of the transmitted signal. As a result, the position and size of the scatterer in the radar direction are obtained have.

However, since the convolution takes an excessive amount of time, it is converted into the frequency domain as shown in FIG. 3, and then multiplied by each element. The distance resolution of the street side view obtained by matched filtering is as follows.

는 빛의 속도,

는 밴드폭을 나타낸다. 정지된 표적의 경우, 표적을 균일한 각도로 회전하면서 거리 측면도를 얻은 후에 각 거리 성분별로 푸리에 변환을 수행하면 (거리, 도플러 주파수) 영역의 역합성 개구면 레이더 영상을 얻을 수 있다. here

The speed of light,

Represents the bandwidth. In the case of a stationary target, it is possible to obtain an inverse composite aperture radar image of a region (distance, Doppler frequency) by performing Fourier transform for each distance component after obtaining a distance side view while rotating the target at a uniform angle.

However, unlike a stationary target, the target actually performs various types of translational motion. In this case, the same scattering circle is observed at different points, and the inverse composite aperture radar image is severely blurred. Therefore, the translational motion compensation problem is the key to obtaining the inverse synthesized aperture radar image, which consists largely of distance alignment and phase correction.

이라는 두 개의 인접한 거리 측면도를 가정할 경우, 두 거리측면도의 엔트로피는 다음과 같다.In the case of distance alignment, the principle is that the entropy of the sum of the two side views is minimized when the two side views are well aligned.

, The entropy of the two distance side views is as follows.

Using this entropy function, we calculate the function value by moving one of two distance side views. Therefore, the relative movement that minimizes the value of the entropy function H is due to the motion of the target.

However, in actual situations, when the current street side view is aligned with the adjacent street side view, errors accumulate. Therefore, in order to minimize this, an entropy of a distance side view to be aligned with the average of the previously arranged distance side view is used.

The step after the distance alignment is a phase correction step for eliminating the residual phase error. Without this phase compensation, you can not get the focused image.

개의 거리 측면도가 정렬되고,

을 각 원소가 각 거리 측면도의 위상 오차인 위상 오차 벡터라 가정할 경우, 위상보상 후의 거리 측면도는 아래와 같이 나타낼 수 있다.

The distance side views of the dogs are aligned,

Is the phase error vector which is the phase error of each distance side view, the distance side view after phase compensation can be expressed as follows.

은 정렬된 거리 측면도이다.

의 각 성분

별로 푸리에 변환하여 역합성 개구면 영상

을 얻을 경우, 영상이 매우 흐려지게 되므로 위상오차 벡터를 추정하기 위하여 다음과 같이 역합성 개구면 레이더 영상의 2차원 엔트로피를 사용하여 엔트로피가 최소가 되도록 하는 위상 오차 벡터(

)를 얻는다.here,

Is an aligned street side view.

Each component

Fourier transform is applied to each pixel,

, The image becomes very blurred. Therefore, in order to estimate the phase error vector, we use the 2-D entropy of the inverse synthetic aperture radar image to estimate the phase error vector

).

이고, 이는

의 정규화된 역합성 개구면 레이더 영상이다. here,

, Which

Is a normalized inverted composite aperture radar image.

The theoretical basis for shaking compensation and segmentation of multiple targets will be described.

4 is a flow chart illustrating a general multi-target inverse synthetic aperture radar spiritual formation process. That is, FIG. 4 shows a distance-side view obtained by distance-aligning using the binary image and the PSO (steps S401 and S402), and then performs a phase correction + Fourier transform to form the entire image (steps S403 and S404).

Generally, when several targets are present, the size of the scattering circle changes greatly due to the interference of the scattering sources when forming the side view of the distance. A diagram showing this is shown in Fig. 5 is a conceptual diagram of a street side view that is not properly aligned in the case of a general method. Referring to FIG. 5, the values of the third and fifth distance components are very large due to multi-target scattering source interference, and are aligned with the fifth and seventh positions at the time of alignment. As shown in FIG. 5, when a distance is aligned, several large scattering sources greatly affect the one-dimensional entropy value, resulting in a result that large scattering sources are aligned with each other. Therefore, the scattered circles are not seen in the synthesized opening image and only the large scattered circles are visible.

%의 산란원들을 선택한 후, 그 값들을 1로 설정한 이진화 영상을 구성한다. 또한, 비행궤적을 다음과 같은 다항식으로 구성하여 각 거리성분이 동일한 비중을 가지는 정렬을 수행한다.In order to overcome this sorting problem,

% Scatterers, and then construct a binarized image with the values set to 1. Also, the trajectory is composed of the following polynomials, and each distance component has the same weight.

는 거리측면도 번호이고,

은 거리측면도의 개수이다. here,

Is a street side view number,

Is the number of street side views.

The coefficients of the above trajectory are found by applying the Partle Swarm Optimization (PSO) algorithm, which is a cluster algorithm, as the cost function as the sum of the values of the binarization images existing on the locus as shown in FIG.

The cost function of FIG. 6 is a value obtained by adding the values of all binarization images existing on the trajectory while moving the flight trajectory from the leftmost side of the image to the right side. Therefore, if the trajectory is equal to the trajectory of the target, it has a large value, otherwise it has a small value. Since the binarized images are used, each distance component contributes to the alignment at the same rate.

을 구성하여 분할을 수행한다. After the alignment, as shown in FIG. 4, a phase correction and a vertical direction Fourier transform for each distance component are performed to form a whole image. After composing the entire image, a binarization image with a value equal to or greater than the average value of the pixel values of the entire image is set to 1,

And performs the division.

At the time of segmentation, the center of gravity 730 of the binarized image existing within the window 720 of the target size as shown in FIG. 7 is calculated at the first one starting from the upper left pixel of the binarized image. The center of gravity of the target 710 is expressed by the following equation.

,

는 각각

의 행과 열의 개수이다.

는 다음식과 같이

이진화 영상의 2차원 확률분포 함수이다.here,

,

Respectively

≪ / RTI >

Is given by

Dimensional probability distribution function of the binarized image.

Then, another binarization image within the window centered on the calculated center of gravity is calculated and the new center of gravity is calculated. Then, the new center of gravity is sequentially applied until the target is included within the window to divide the image.

이내의 이진화 영상과 기존의 전체 영상을 곱하여 최종적으로 단일표적의 역합성 개구면 레이더 영상을 분할한다(단계 S405).With continued reference to Figure 4,

(Step S405). In step S405, the binarized image is multiplied by the existing overall image to divide the single synthesized opening radar image.

The final step is the quality improvement process of the segmented image. There is an error in the divided images due to interference of other scattering sources in the whole image formation. Accordingly, after the vertical direction inverse Fourier transform is performed for each divided image (steps S410, S411, and S412), shaking compensation is performed using one-dimensional entropy and two-dimensional entropy (step S413). Thereafter, the enhanced image is obtained again by performing the vertical direction Fourier transform (steps S414 and S415). The improved image calculation blocks 410-1 to 410-N comprising the above steps S410 to S415 are performed for each of the N divided images.

In the case of the distance alignment technique for dividing the generalized synthesized opening surface radar image described with reference to Figs. 1 to 7, serious image distortion may occur when the target trajectory of the above-mentioned polynomial shape is different from the actual trajectory.

Also, if the window of the image is not properly set at the time of division, the two targets may exist in one window and the image may not be divided.

In one embodiment of the present invention, such problems as image distortion and image differentiation are solved based on the contents described in Figs. 1 to 7. This will be described with reference to FIGS. 8 to 12, and a detailed description of the overlapping of the contents described in FIG. 1 to FIG. 7 will be omitted.

8 is a block diagram of an effective multi-target inverse synthesized opening surface radar image forming apparatus 800 using flight trajectory and morphological processing according to an embodiment of the present invention. Referring to FIG. 8, a data forming unit 810 forms distance-side-view binarized images for each of the distance side views of multiple targets that are flying in a flight, and forms a center of gravity of distance side view in each binarized image using the distance- A distance arranging unit 820 for performing distance alignment on the distance side view binarized images using the multiple trajectory flight trajectory, a phase aligning unit 820 for distance-aligned distance side view binarization images, A binarization image forming unit 840 for forming a secondary binarization image from the initial full image by using a constant false alarm rate (CFAR) detector, And a division unit 850 for generating divided images by extracting and dividing individual binarized images through erosion, .

The data forming unit 810, the distance arranging unit 820, the binarized image forming unit 840, and the partitioning unit 850 may be implemented by hardware, software, or a combination thereof. (DSP), a programmable logic device (PLD), a field programmable gate array (FPGA), a processor, a controller, a microprocessor, and the like, which are designed to perform the above- , Other electronic units, or a combination thereof. In software implementation, it may be implemented as a module that performs the above-described functions. The software may be stored in a memory unit and executed by a processor. The memory unit or processor may employ various means well known to those skilled in the art.

%의 거리성분을 선택하여 값을 1로, 나머지는 0으로 한 이진화된 거리측면도 이진화 영상을 형성한다. 이후 형성된 이진화 영상을 이용하여 각 이진화 영상내의 거리측면도의 무게중심을 형성한다(단계 S910).FIG. 9 is a flowchart illustrating a process of forming an effective multi-target inverted synthesized opening radar image using a flight path and morphological processing according to an embodiment of the present invention. Referring to FIG. 9,

% Of the distance component is selected and a binarized distance side view binarized image is formed with the value of 1 and the rest of 0. Then, the center of gravity of the distance side view in each binarized image is formed using the formed binarized image (step S910).

The distance trajectory is modeled as a polynomial + Gaussian function for distance alignment for full image formation, distance alignment is performed by applying the cost function shown in FIG. 6, phase correction is performed, (Step S930).

%의 거리성분을 1로, 나머지는 0으로 설정된 이진화 영상을 구성한 후, 거리정렬을 위하여 다음식과 같이 각 이진화 영상내의 이진 거리측면도의 무게중심 곡선을 형성한다.In addition, for the formation of the initial full image, as described above,

After constructing the binarization image with the distance component of 1% and the rest of 0 as 0, the center-of-gravity curve of the binary distance side view in each binarized image is formed as follows.

은

번째 이진 거리측면도이고,

은 거리성분의 개수이다. 이후 무게중심 곡선을 이용한 정렬을 위하여 다음식과 같이 다항식과 가우스 함수로 구성된 비행궤적을 구성한다.here,

silver

Lt; th > binary distance side view,

Is the number of distance components. Then, for the alignment using the center-of-gravity curve, a flight path composed of a polynomial and a Gaussian function is constructed as shown in the following equation.

는 거리측면도 번호,

는 다항식 계수,

은 다항식 차수,

는 가우스 함수 계수,

는 가우스 함수 중심,

는 가우스함수 표준편차이다. here,

The street side view number,

Is a polynomial coefficient,

Is a polynomial order,

Is the Gaussian function coefficient,

Centered on the Gaussian function,

Is the Gaussian standard deviation.

의 오차가 최소가 되는 방법으로 대강 추정 변수를 대강 추정한 후, 도 6의 비용함수 및 PSO를 적용하여 변수들을 세부적으로 추정한다. 이를 이용하여 획득된

을 이용한 정렬 후, 전체 역합성 개구면 레이더 영상 형성한다.To obtain each variable, we used the Gaussian Newton algorithm based on the slope,

The estimation method is roughly estimated, and then the cost function and the PSO of FIG. 6 are applied to estimate the parameters in detail. Using this,

And then, a total reverse synthetic aperture surface radar image is formed.

Referring still to FIG. 9, a secondary binarized binary image is formed using a constant false alarm rate (CFAR) detector for segmentation (step S940).

Thereafter, the individual binarized images connected through the expansion and erosion of the binarized whole image are extracted and divided (steps S950 and S960).

을 형성한다. 이를 위하여 초기 전체 영상(ISAR: Inverse Synthetic Aperture Radar)의 점 주위로 클러터의 평균

및 분산

추정한 후, 다음식과 같이 CFAR 탐지기를 설정한다.In order to divide each image after the formation of the initial full image, a binarized binary image of the entire image to which a constant false alarm rate (CFAR) detector is applied

. For this purpose, the average of the clutter around the point of the initial full image (ISAR: Inverse Synthetic Aperture Radar)

And dispersion

After the estimation, the CFAR detector is set as shown in the following equation.

는 전체영상 중 한 점의 값이다.

가 일정값 이상이면

을 1로, 그렇지 않으면 0으로 설정하여 이진화 전체 영상이 형성된다.here,

Is the value of one point in the whole image.

Is equal to or greater than a predetermined value

Is set to 1, otherwise, a binary full image is formed.

을 형성하고, 이를 이용하여 전체 이진화 영상

을 확장한다. After the whole image is binarized using the CFAR detector, as shown in FIG. 10, a disk-shaped image for binarized image expansion and erosion

And the entire binarization image

.

을 반사시킨 후,

만큼

을 이동하면서 이진화 전체 영상

와의 교집합이 존재하는 부분을 1로 설정하여 확장된 영상을 획득한다. 수식으로 표현하면 다음과 같다. 11,

After reflection,

as much as

The entire binary image

Is set to 1 to acquire an extended image. The expression is as follows.

이고, 이는 영상의 반사이며,

이고 이는 영상의 이동을 나타낸다.

는 영상의 이동 정도를 나타낸다. here,

, Which is the reflection of the image,

Which represents the movement of the image.

Represents the degree of movement of the image.

을

만큼 이동하면서 확장된 영상

내부에 존재하는 부분만 1로 설정하는 침식과정을 거친 후 영상의 내부를 채운다. 수식으로 표현하면 다음과 같다.The expanded image after the image expansion is shown in FIG. 12

of

Extended moving image

And the inside of the image is filled after the erosion process in which only the portion existing inside is set to 1. The expression is as follows.

Finally, the connected parts of the image are searched, and then the whole image is multiplied, and then each image is divided and the divided image is improved.

In other words, after performing the vertical direction inverse Fourier transform on the divided images, the motion compensation is performed using the one-dimensional entropy and the two-dimensional entropy. Thereafter, further vertical direction Fourier transform is performed to obtain an improved final image.

FIG. 10 is a conceptual diagram illustrating a disk shape used for image expansion and erosion according to an embodiment of the present invention.

11 is a conceptual diagram showing an image expansion according to an embodiment of the present invention. Referring to FIG. 11, a binary image is obtained by setting a portion having intersection with A to 1 by using B.

내부에 존재하는 부분만 1로 설정하여 이진화 영상을 확보한다.12 is a conceptual diagram showing eroding an image according to an embodiment of the present invention. Referring to Figure 12, using B

And only the portion existing inside is set to 1 to secure a binarized image.

900: Multi-Target Inverted Composite Opening Radar Imager
910:
920:
940: Binarization image forming unit
450: minute installment

Claims (9)

a) forming distance side view binarized images for each street side view of multiple targets flying the flight;
b) forming a center of gravity of the distance side view in each binarized image using the distance side view binarized images;
c) performing distance alignment on the distance side view binarized images using the multi-target flight trajectory;
d) performing a phase correction on the binarized images to form a primary initial full image;
e) forming a secondary binarized full image from the initial full image using a constant false alarm rate (CFAR) detector; And
f) generating a segmented image by extracting and segmenting individual binarized images through expansion and erosion of the binarized whole image;
The method comprising the steps of: (a) generating an effective multi-target inverse synthetic aperture radar image using a flight path and morphological processing.
The method according to claim 1,
The step (f)
Performing vertical inverse Fourier transform on the divided image;
Performing swing compensation using a one-dimensional entropy and a two-dimensional entropy in a vertically inverse Fourier transformed divided image; And
And performing a vertical direction Fourier transform on the shake compensated divided image to obtain a final image. The method of forming an effective multiple-target inverted-synthesized opening surface radar image using the trajectory and morphological processing.
The method according to claim 1,
Wherein the flight trajectory is represented by a polynomial and a Gaussian function. The method of claim 1, wherein the flight trajectory is represented by a polynomial and a Gaussian function.
The method according to claim 1,
The binarized images are the largest

% Is set to 1, and the rest is set to 0. The method of forming an effective multi-target inverse synthesized opening surface radar image using the trajectory and morphological processing.
The method according to claim 1,
Wherein the center of gravity is a center-of-gravity curve of the binary distance side view in each binarized image,

(here,

silver

Lt; th > binary distance side view,

Wherein the number of the distance components is the number of the distance components.
The method of claim 3,
The polynomial and the Gaussian function are expressed by the following equations

(here,

The street side view number,

Is a polynomial coefficient,

Is a polynomial order,

Is the Gaussian function coefficient,

Centered on the Gaussian function,

Is a Gaussian function standard deviation). The method of claim 1, further comprising:
The method according to claim 1,
The binary full image estimates the average and variance of the clutter around the point of the initial full image and is set to 1 if the predetermined false alarm probability CFAR detector is greater than or equal to a predetermined value and to 0 if the predetermined false alarm probability An effective multi-target inverse synthesized aperture radar image formation method using characteristic trajectory and morphological processing.
The method according to claim 1,
The step (f)
Forming a binary image as a disc-shaped image;
And expanding or eroding the binarized image using the intersection operation of the binarized whole image and the disk-shaped image. The method of forming an effective multi-target inverse synthetic aperture radar image using the trajectory and morphological processing.
A data forming unit for forming distance-side-view binarized images for each of the plurality of target side-by-side distance-side views, and forming a center of gravity of the distance-side viewpoint within each binarized image using the distance-side-view binarized images;
A distance alignment unit for performing distance alignment on the distance side view binarized images using the multi-target flight trajectory;
Distance-based distance-side-view binarized images are subjected to phase correction to form a primary initial full image, and a secondary binarization from the initial full image is performed using a constant false alarm rate (CFAR) A binarization image forming unit for forming an image; And
Extracting and segmenting individual binarized images through expansion and erosion of the binarized whole image to generate divided images;
Wherein the multi-target inverse synthesized opening surface radar image forming apparatus is constructed by using a flight trajectory and morphological processing.

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