KR20220084793A - Method and apparatus for obtaining radatr video - Google Patents

Abstract

The present specification is a multiple input multiple output (Multiple Input Multiple Output; MIMO) orthogonal frequency division (Orthogonal Frequency Division Multiplexing; OFDM) obtaining a radar signal; converting the radar reception signal based on image patch based modeling; and compressing and sensing the converted radar reception signal using Fast Fourier Transform (FFT). and acquiring a radar image from the compressed sensed radar reception signal.

Description

Radar image acquisition method and apparatus {METHOD AND APPARATUS FOR OBTAINING RADATR VIDEO}

The present invention relates to a method and apparatus for acquiring a radar image, and more particularly, to a method and apparatus for acquiring a radar image in a Multiple Input Multiple Output (MIMO) Orthogonal Frequency Division Multiplexing (OFDM) radar system, and It's about the device.

In order to estimate the exact location of a target in vehicle radar and military radar, it is necessary to acquire a high-resolution radar image that can determine the distance and azimuth to the target. In particular, in radar applications such as autonomous driving, it is necessary to acquire radar images at a real-time processing speed. However, the current radar system has a problem in that it cannot acquire a high-resolution radar image due to the complexity of calculation in estimating the distance to the target, the azimuth, and the speed.

In addition, in a radar system in which an information communication band and a radar operating band are adjacent to or coexist with each other, there is a problem in that an accurate radar image cannot be obtained due to interference between waveforms.

An object of the present invention to solve the above problems is to obtain a high-resolution radar image by accurately estimating the distance, azimuth, and speed to a target in front of the radar.

Another object of the present invention to solve the above problems is to provide a method for converting a radar reception signal into a signal suitable for applying compression sensing.

In addition, another object of the present invention to solve the above problems is to estimate the distance to the target, the azimuth and the speed through compression sensing for radar imaging based on the converted received signal.

A radar image acquisition method according to an embodiment of the present invention for achieving the above object includes: acquiring a multiple input multiple output (MIMO) orthogonal frequency division multiplexing (OFDM) radar reception signal; converting the radar reception signal based on image patch based modeling; and compressing and sensing the converted radar reception signal using Fast Fourier Transform (FFT). and obtaining a radar image from the compressed sensed radar reception signal.

According to an embodiment of the present invention, it is possible to reduce the complexity of calculations when estimating a distance to a target, an azimuth, and a velocity by using a Fast Fourier Transform (FFT).

1 is a view for explaining a radar image acquisition method of the present invention.
2 is a diagram for explaining a radar reception signal.
3 is a diagram illustrating a radar reception signal as a data cube.
4 is a diagram for explaining image patch modeling.
5 is a diagram for explaining a fast Fourier transform.
6A is a first diagram for comparing radar image acquisition results.
6B is a second diagram for comparing radar image acquisition results.
6C is a third diagram for comparing the radar image acquisition results.
6D is a fourth diagram for comparing the radar image acquisition results.
6E is a fifth diagram for comparing the radar image acquisition results.
7 is an operation flowchart of a method for acquiring a radar image according to an embodiment of the present invention.
8 is a block diagram of an apparatus for acquiring a radar image according to another embodiment of the present invention.

Since the present invention can have various changes and can have various embodiments, specific embodiments are illustrated in the drawings and described in detail in the detailed description. However, this is not intended to limit the present invention to specific embodiments, and it should be understood to include all modifications, equivalents and substitutes included in the spirit and scope of the present invention. In describing each figure, like reference numerals have been used for like elements.

Terms such as first, second, A, and B may be used to describe various elements, but the elements should not be limited by the terms. The above terms are used only for the purpose of distinguishing one component from another. For example, without departing from the scope of the present invention, a first component may be referred to as a second component, and similarly, a second component may also be referred to as a first component. The term “and/or” includes a combination of a plurality of related listed items or any of a plurality of related listed items.

When a component is referred to as being “connected” or “connected” to another component, it may be directly connected or connected to the other component, but it is understood that other components may exist in between. it should be On the other hand, when it is said that a certain element is "directly connected" or "directly connected" to another element, it should be understood that the other element does not exist in the middle.

The terms used in the present application are only used to describe specific embodiments, and are not intended to limit the present invention. The singular expression includes the plural expression unless the context clearly dictates otherwise. In the present application, terms such as “comprise” or “have” are intended to designate that a feature, number, step, operation, component, part, or combination thereof described in the specification exists, but one or more other features It should be understood that this does not preclude the existence or addition of numbers, steps, operations, components, parts, or combinations thereof.

Unless defined otherwise, 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 should be interpreted as having a meaning consistent with the meaning in the context of the related art, and should not be interpreted in an ideal or excessively formal meaning unless explicitly defined in the present application. does not

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

1 is a view for explaining a radar image acquisition method of the present invention.

1, the radar image acquisition method according to an embodiment of the present invention is a radar image based on an Orthogonal Frequency Division Multiplexing (OFDM) waveform through multiple antennas (Multiple Input Multiple Output; MIMO). can be obtained

FIG. 2 is a diagram for explaining a radar reception signal, and FIG. 3 is a diagram expressing the radar reception signal as a data cube.

When subcarriers are orthogonally assigned to each transmit antenna (ie, do not overlap), a signal transmitted from the transmit antenna can be expressed as in Equation (1).

개의 송신 안테나 및

개의 수신 안테나를 포함하는 MIMO OFDM 시스템에서, 송신 안테나가 전송하는 신호에 대한 수식일 수 있다. 여기서,

는

번 째 안테나가 할당 받은 부반송파 인덱스 집합을 의미할 수 있고,

은 OFDM 심볼의 개수를 의미할 수 있다. 또한, 수학식 1에 따르면 전방의 목표물들의 속도를 추정하기 위해

개의 OFDM 심볼을 전송할 수 있다. 이 때,

은 1일 수 있다. 한편,

(

)은 부반송파의 주파수를 의미할 수 있고,

은 CP를 포함한 OFDM 심볼 시간 길이를 의미할 수 있고,

는 심볼 반복 인터벌(symbol repetition interval)을 의미할 수 있다. 또한, 시간(t)는

로 표현될 수 있다. 따라서, 상기 송신 안테나가 전송하는 신호가 전방으로 방사되어, K개의 목표물에 반사되어 수신되는 신호를

로 샘플링하여 이산 신호로 변환할 수 있다. 이 때, 이산 시간 수신 신호는 수학식2와 같다.Equation 1 is

two transmit antennas and

In a MIMO OFDM system including Rx antennas, it may be an equation for a signal transmitted by a transmit antenna. here,

Is

It may mean a set of subcarrier indexes allocated to the first antenna,

may mean the number of OFDM symbols. In addition, according to Equation 1, in order to estimate the speed of the targets in front

OFDM symbols can be transmitted. At this time,

may be 1. Meanwhile,

(

) may mean the frequency of the subcarrier,

may mean the OFDM symbol time length including CP,

may mean a symbol repetition interval. Also, the time (t) is

can be expressed as Therefore, the signal transmitted by the transmitting antenna is radiated forward, and the signal received by being reflected by K targets

can be sampled and converted to a discrete signal. In this case, the discrete-time reception signal is expressed by Equation (2).

는 송신 신호가 전방의 k번 째 목표물에 반사되어 수신 안테나가 이를 수신하는데 소요된 지연시간을 의미할 수 있다. 한편,

는 수학식 3과 같이 표현될 수 있다.here,

may mean the delay time it takes for the transmission signal to be reflected by the k-th target in front and the reception antenna to receive it. Meanwhile,

can be expressed as in Equation (3).

는 기준 안테나로부터 k 번째 목표물까지의 거리를 의미할 수 있고,

는 k번째 목표물의 방위각을 의미할 수 있다. 즉, 지연시간에 두 파라미터가 커플링(coupling)될 수 있다. 한편, 수학식 2와 수학식 3을 정리하면 수학식 4와 같다.here,

may mean the distance from the reference antenna to the k-th target,

may mean the azimuth of the k-th target. That is, the two parameters may be coupled to the delay time. On the other hand, if Equations 2 and 3 are arranged, Equation 4 is obtained.

According to Equation 4, the distance from the antenna to the target may be estimated, the azimuth may be estimated, and the Doppler frequency may be estimated. In this case, Equation 6 that simply expresses Equation 4 may be derived through Equation 4 and Equation 5.

In addition, when the received signal in the OFDM symbol of Equation 6 is arranged in a vector form, Equation 7 is obtained.

)를 활용하면 수학식 8과 같이 정리할 수 있다.A matrix that performs Fast Fourier Transform (FFT) to transform Equation 7 into the frequency domain (

), it can be arranged as in Equation 8.

Referring to Equation (8), since subcarriers are not overlapped for each transmit antenna, a signal received from each transmit antenna can be separated at the receive antenna. Accordingly, the effect of the virtual array antenna can be obtained. In this case, the virtual antenna may be disposed according to a uniform linear array antenna shape. On the other hand, the signal received from the m-th virtual antenna using the virtual antenna index (m) is expressed in Equation (9).

In addition, if all virtual antenna indices (m) are arranged in Equation 9, Equation 10 is obtained.

개의 OFDM 심볼을 송신하여 수신한 신호를 도 3과 같은 데이터 큐브(data cube) 형태로 표현할 수 있다.Schematic of Equation 10 is the same as in FIG. 2, and based on the radar reception signal model of FIG.

A signal received by transmitting OFDM symbols may be expressed in the form of a data cube as shown in FIG. 3 .

4 is a diagram for explaining image patch modeling.

A signal received from the m th virtual antenna may be expressed as in Equation 11.

로 표현할 수 있다. 여기서,

는 (r, p)번 째 이미지 패치에 해당하는 픽셀에 목표물이 있으면 그 목표물의 반사율(reflectivity)을 의미할 수 있고, 목표물이 없으면 0의 값을 가질 수 있다. 한편, 지연시간은 수학식 12와 같이 이미지 패치의 거리, 방위각 및 송수신 안테나의 인덱스(즉, 가상 배열 안테나의 인덱스)에 의해 결정될 수 있다.When Equation 11 is described with reference to FIG. 4, after dividing the radar image into several pixels by R in the distance direction and P in the azimuth direction, the distance and azimuth corresponding to the (r, p)th image patch are calculated.

can be expressed as here,

may mean the reflectivity of the target if there is a target in the pixel corresponding to the (r, p)th image patch, and may have a value of 0 if there is no target. Meanwhile, the delay time may be determined by the distance of the image patch, the azimuth, and the index of the transmit/receive antenna (ie, the index of the virtual array antenna) as shown in Equation 12.

Meanwhile, Equation 10 can be rearranged as Equation 13.

Here, the matrix A and the matrix x may be expressed as Equations 14 and 15, respectively.

The matrix x may include forward image information and generally has a sparse property because there are not many scatterers (ie, it contains many elements of 0).

Here, each element of the matrix A is expressed by Equation (16).

Therefore, according to Equation 17, the radar reception signal may be converted through image patch modeling.

5 is a diagram for explaining a fast Fourier transform.

In order to acquire a MIMO OFDM radar image, various types of compression sensing techniques may be applied. For example, Bayesian Matching Pursuit (BMP), which has been proposed to acquire the existing FMCW MIMO radar image, may be applied.

번 째 수신 신호를 바탕으로 획득한 레이더 영상 정보를

라고 정의할 때, 각각의 수신 신호를 통해 추정된 값의 평균을 내어 잡음에 강인한(robust) 개선된 영상을 획득할 수 있고, 이는 수학식 18과 같다.On the other hand, the gun

Radar image information acquired based on the second received signal

, it is possible to obtain an improved image robust to noise by averaging the values estimated through each received signal, as shown in Equation (18).

의 원소가 0이면 해당 픽셀에 목표물이 존재하지 않으며, 원소가 0보다 큰 값을 가질 경우 목표물이 존재할 수 있고 이 때 해당 목표물의 도플러 주파수 추정을 통해 목표물의 속도를 추정할 수 있다. 따라서, 목표물이 있는 픽셀의 집합을

라고 하면, 데이터 큐브(data cube)로부터 목표물이 있는 픽셀에 대해 행렬 A의 열벡터를 활용하여 수학식 19와 같은 신호를 획득할 수 있다.According to Equation 18,

If the element of is 0, the target does not exist in the corresponding pixel, and if the element has a value greater than 0, the target may exist. In this case, the speed of the target can be estimated through estimation of the Doppler frequency of the target. Thus, the set of pixels with the target

, a signal such as in Equation 19 can be obtained from a data cube by using a column vector of a matrix A for a pixel having a target.

개의 수신 파형에 대해 거리 및 방위각 정보를 이용하여 매치드 필터(matched filter)를 통과하기 때문에, 다른 목표물에 대한 도플러 정보는 제거되고 해당 목표물의 도플러 정보에 기반한 위상 변화만이 존재할 수 있다. 이 때, 수학식 20과 같은 고속 푸리에 변환(FFT)를 통해 해당 도플러 주파수를 추정할 수 있다.That is, referring to Equation 19,

Since the received waveforms are passed through a matched filter using distance and azimuth information, Doppler information for other targets is removed and only a phase change based on Doppler information of the corresponding target may exist. In this case, a corresponding Doppler frequency may be estimated through a fast Fourier transform (FFT) as in Equation (20).

Meanwhile, when a radar image is acquired through compression sensing of a radar reception signal, calculation complexity increases to acquire a high-resolution radar image. That is, in order to acquire a high-resolution radar image, the number of pixels for the image in front is increased, so computational complexity increases.

Therefore, according to the present invention, the approximate target distance can be roughly and quickly estimated through fast Fourier transform by utilizing the characteristics of MIMO OFDM in order to reduce computational complexity. That is, according to the present invention, compressed sensing can be applied by reducing the dimension of the model of Equation 13 by roughly estimating the Doppler frequency through the fast Fourier transform.

Meanwhile, Equation 21 may be derived from Equation 9 indicating a received signal for each virtual antenna.

로서 표현하고 있다. 여기서,

는 수학식 15와 유사하게 표현할 수 있고,

는 수학식 15의 행렬 A에 비해 행의 개수가

의 비율로 감소될 수 있고, 열의 개수가

의 비율로 감소된 행렬의 크기를 가질 수 있다. 이 때, 수학식 21로 범위(range) 추정을 위해

에 고속 푸레이 변환을 적용하면 목표물 거리에 따른 딜레이 위치에 임펄스(impulse)가 나타날 수 있다. 이는 수학식 22와 같이 표현할 수 있다.Equation 13 expresses the two-dimensional radar image in front using the azimuth and distance, while Equation 21 shows the one-dimensional direction of the distance axis in a state where the azimuth is fixed.

is expressed as here,

can be expressed similarly to Equation 15,

is the number of rows compared to matrix A in Equation 15

can be reduced at the rate of , and the number of columns

It is possible to have the size of the matrix reduced by the ratio of . At this time, in order to estimate the range by Equation 21

If fast Fouray transform is applied to , an impulse may appear at the delay position according to the target distance. This can be expressed as Equation 22.

에 인버스 고속 푸리에 변환(Inverse FFT)를 적용하면 목표물이 있는 거리에 해당하는 지연 시간에 피크(peak)가 발생함을 알 수 있고, 이를 수식으로 나타내면 수학식 23과 같다.Referring to Figure 5,

If the inverse fast Fourier transform (Inverse FFT) is applied to , it can be seen that a peak occurs at a delay time corresponding to the distance to the target, and this is expressed as Equation 23.

에는 목표물 위치에 대응되는 시간에 피크를 확인할 수 있고, 이로부터 범위(range)를 추정할 수 있다. 목표물 위치에 대응 되는 시간에 발생한 피크의 위치에 해당하는 거리는 수학식 24에 의해 도출될 수 있다.in other words,

A peak can be identified at a time corresponding to the target position, and a range can be estimated therefrom. A distance corresponding to the position of the peak occurring at a time corresponding to the target position may be derived by Equation 24.

을 결합하여 목표물의 거리를 추정할 수 있고, 이는 수학식 25로 표현될 수 있다.However, there may be differences in positions of peaks corresponding to targets for each virtual antenna, and this may be defined as a delay migration phenomenon that is affected by an azimuth and an antenna index. That is, in order to improve the estimation reliability,

It is possible to estimate the distance of the target by combining , which can be expressed by Equation 25.

은 수학식 26과 같이 정의될 수 있다.here,

can be defined as in Equation 26.

을 결합하여 목표물의 거리를 추정하기 위해서는 지연 이주를 보상한 뒤 피크를 추정해야 한다. 또한, 가장 큰 피크의 지연 이주를 기준으로 보상할 수 있고, 가장 큰 피크의 위치는 각 안테나 별로 수학식 27에 의해 표현될 수 있고, 가장 큰 피크의 위치는 안테나 인덱스와 방위각의 영향을 받을 수 있다.According to Equation 25, estimated from several virtual antennas

In order to estimate the target distance by combining In addition, compensation can be made based on the delay shift of the largest peak, the position of the largest peak can be expressed by Equation 27 for each antenna, and the position of the largest peak can be affected by the antenna index and azimuth. have.

을 추정하기 위해 가상 안테나 배열 방식을 원점을 중심으로 대칭이 되도록 설계할 수 있다. 즉, 가상 안테나의 위치가

이 되도록 설계하면 수학식 28에 의해 각 안테나 별 추정된 피크 값의 위치의 평균 값을 활용하여 안테나의 위치와 무관하게

을 추정할 수 있다.On the other hand, the present invention

In order to estimate , the virtual antenna array method can be designed to be symmetrical with respect to the origin. That is, the position of the virtual antenna is

When designed to be , the average value of the positions of the peak values estimated for each antenna is used regardless of the position of the antenna by Equation (28).

can be estimated.

을 추정할 수 있고, 다중 목표물들의 거리에 피크가 발생하는 것을 확인할 수 있다. 즉,

의 값이 사용자가 미리 설정한 임계값보다 크면 목표물이 있다고 판단할 수 있다, 한편, 수학식 13으로부터 해당 거리에 해당하는 픽셀에 대해서만 CS를 위한 선형 방정식을 수학식 29와 같이 표현할 수 있다.Therefore, according to Equations 25 to 28,

can be estimated, and it can be confirmed that a peak occurs at the distances of multiple targets. in other words,

If the value of is greater than the threshold value preset by the user, it can be determined that there is a target. On the other hand, the linear equation for CS can be expressed as Equation 29 only for pixels corresponding to the corresponding distance from Equation 13.

일 수 있다.At this time,

can be

에서

로 감소되어 압축 센싱에서 요구하는 계산 복잡도를 감소시킬 수 있다. Meanwhile, according to the present invention, the size of the matrix A that determines the size of the system equation for CS application is

at

, it is possible to reduce the computational complexity required for compression sensing.

In the MIMO OFDM radar, in order to classify the waveform transmitted by each transmitting antenna at the receiving end, subcarriers for each transmitting antenna are allocated so that the subcarriers do not overlap, and the OFDM waveform can be transmitted. Meanwhile, since there may be a difference in compression sensing-based radar imaging performance depending on the allocation method, the present invention can estimate the radar image by applying compression sensing to the linear equation of Equation 13.

That is, the lower bound of the mean square error (MSE) of the estimate of a general linear equation through information on the support set indicating where a non-zero value exists in the vector x to be estimated. ), the Krame-Rao lower limit can be expressed as in Equation 30.

는 행렬 A의 부행렬로써 서포트 세트에 해당하는 행렬 A의 열벡터들을 모아둔 행렬을 의미할 수 있다. 즉, 본 발명은 수학식 30으로부터 행렬 A의 임의의 부행렬들의

값이 작아지도록 부반송파를 할당하여, 추정한 영상의 평균제곱오차를 감소시키고 보다 개선된 레이더 영상을 획득할 수 있다.here,

As a sub-matrix of the matrix A, may mean a matrix in which column vectors of the matrix A corresponding to the support set are collected. That is, the present invention can be obtained from Equation (30) of any sub-matrices of the matrix A.

By allocating subcarriers so that the value is small, the mean square error of the estimated image can be reduced and a more improved radar image can be obtained.

6A is a first diagram for comparing radar image acquisition results, FIG. 6B is a second diagram for comparing radar image acquisition results, FIG. 6C is a third diagram for comparing radar image acquisition results, and FIG. 6D is a fourth diagram for comparing the radar image acquisition results, and FIG. 6E is a fifth diagram for comparing the radar image acquisition results.

6A to 6D , imaging techniques used in a process of acquiring a radar image based on a MIMO OFDM radar reception signal using four transmit/receive antennas, respectively, may be compared.

6A is an original image and means a radar image showing five objects in front. In addition, FIG. 6B may mean a radar image obtained when using the back projection method, FIG. 6C may mean a radar image obtained when using the conventional BMP method, and FIG. 6D is this view. When the BMP method with reduced computational complexity according to the present invention is used, it may mean a radar image. At this time, it can be seen that the radar image is obtained by clearly distinguishing five objects compared to the original image of FIG. 6A in FIGS. 6C and 6D .

FIG. 6E is a diagram comparing the amount of calculation in the case of acquiring a radar image using the conventional fast Fourier transform method and the case of acquiring the radar image using the BMP method with reduced computational complexity of the present invention. In general, as the number of antennas increases, the amount of computation increases. However, when the radar image is acquired according to the BMP method with reduced computational complexity of the present invention, the calculation complexity can be reduced by 8 times or more compared to when the radar image is acquired according to the existing BMP method.

7 is an operation flowchart of a method for acquiring a radar image according to an embodiment of the present invention.

Referring to FIG. 7 , the radar image acquisition method according to an embodiment of the present invention includes multiple input multiple output (MIMO) orthogonal frequency division multiplexing (OFDM) radar reception signals (S110) may include

Also, the radar image acquisition method of the present invention may include converting the radar reception signal based on image patch based modeling ( S120 ).

In addition, the radar image acquisition method of the present invention may include compression-sensing the converted radar reception signal by using a Fast Fourier Transform (FFT) (S130).

Also, the radar image acquisition method of the present invention may include acquiring a radar image from the compressed sensed radar reception signal ( S140 ).

8 is a block diagram of an apparatus for acquiring a radar image according to another embodiment of the present invention.

Referring to FIG. 8 , the radar image acquisition apparatus 100 according to an embodiment of the present invention includes a processor 110 and at least one command executed through the processor and a memory 120 and a network for storing the result of command execution. It may include a transceiver 130 that is connected to and performs communication.

The radar image acquisition device 100 may further include an input interface device 140 , an output interface device 150 , a storage device 160 , and the like. Each of the components included in the radar image acquisition apparatus 100 may be connected by a bus 170 to communicate with each other.

The processor 110 may execute a program command stored in at least one of the memory 120 and the storage device 160 . The processor 110 may refer to a central processing unit (CPU), a graphics processing unit (GPU), or a dedicated processor on which methods according to an embodiment of the present invention are performed. Each of the memory 120 and the storage device 160 may be configured of at least one of a volatile storage medium and a non-volatile storage medium. For example, the memory 120 may be configured as at least one of a read only memory (ROM) and a random access memory (RAM).

The storage device 160 includes information on multiple input/output (MIMO) antennas used in the present invention, information on orthogonal frequency-divisioned radar reception signals, information obtained by converting a radar reception signal based on image patch modeling, and a converted radar reception signal. It is possible to store information about the fast Fouray transform used when compressing and sensing .

Here, the at least one command, Multiple Input Multiple Output (MIMO) orthogonal frequency division (Orthogonal Frequency Division Multiplexing; OFDM) command to obtain a radar reception signal; a command to convert the radar received signal based on image patch based modeling; a command to compress and sense the converted radar reception signal using Fast Fourier Transform (FFT); and a command to acquire a radar image from the compressed sensed radar reception signal.

The operation of the method according to the embodiment of the present invention can be implemented as a computer-readable program or code on a computer-readable recording medium. The computer-readable recording medium includes all types of recording devices in which data that can be read by a computer system is stored. In addition, the computer-readable recording medium may be distributed in a network-connected computer system to store and execute computer-readable programs or codes in a distributed manner.

In addition, the computer-readable recording medium may include a hardware device specially configured to store and execute program instructions, such as ROM, RAM, and flash memory. The program instructions may include not only machine language codes such as those generated by a compiler, but also high-level language codes that can be executed by a computer using an interpreter (Internetpreter) or the like.

Although some aspects of the invention have been described in the context of an apparatus, it may also represent a description according to a corresponding method, wherein a block or apparatus corresponds to a method step or feature of a method step. Similarly, aspects described in the context of a method may also represent a corresponding block or item or a corresponding device feature. Some or all of the method steps may be performed by (or using) a hardware device such as, for example, a microprocessor, programmable computer or electronic circuit. In some embodiments, one or more of the most important method steps may be performed by such an apparatus.

In embodiments, a programmable logic device (eg, a field programmable gate array) may be used to perform some or all of the functionality of the methods described herein. In embodiments, the field programmable gate array may operate in conjunction with a microprocessor to perform one of the methods described herein. In general, the methods are preferably performed by some hardware device.

Although described above with reference to the preferred embodiment of the present invention, those skilled in the art can variously modify and change the present invention within the scope without departing from the spirit and scope of the present invention as set forth in the claims below. You will understand that you can.

Claims (1)

obtaining a multiple input multiple output (MIMO) orthogonal frequency division multiplexing (OFDM) radar reception signal;
converting the radar reception signal based on image patch based modeling;
compressing and sensing the converted radar reception signal using Fast Fourier Transform (FFT); and
and acquiring a radar image from the compressed sensed radar reception signal.

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