KR102250621B1 - Apparatus and method for detecting parameters in frequency modulation continuous wave radar - Google Patents

Abstract

An apparatus and method for detecting parameters of an FMCW radar are disclosed. The parameter detection method includes the steps of: receiving a reception signal in which a transmission signal output from a radar is reflected on a target; Performing a fast Fourier transform (FFT) on the received signal; Estimating distance information between the target and the radar using the FFT result; Estimating angle information between a target and a radar by applying an extrapolation algorithm to the FFT result; And applying a RELAX (Relaxation) algorithm to the angle information to obtain ultra-high resolution angle information having an improved resolution than the angle information.

Description

FMCW radar parameter detection device and method {APPARATUS AND METHOD FOR DETECTING PARAMETERS IN FREQUENCY MODULATION CONTINUOUS WAVE RADAR}

The present invention relates to an apparatus and method for detecting a parameter of a frequency modulation continuous wave (FMCW) radar, and more particularly, to a method for improving parameter detection performance in a sensor using an FMCW radar.

The FMCW radar system not only can estimate distance-speed information for multiple targets by simultaneously using the time-frequency domain, but also has very high bandwidth efficiency and low complexity, so it is being used as a vehicle radar system.

In the FMCW radar system, since the beat frequency is determined according to the distance and speed of each target, it is very important to accurately estimate the frequency parameter component.

A conventional FMCW radar parameter detection apparatus uses an FFT-based method for frequency component estimation. However, the parameters detected by the parameter detection device of the FMCW radar using the FFT-based method do not have a high resolution, and thus, if a plurality of targets are adjacent, it is highly likely to be recognized as a single target. Accordingly, MUSIC (multiple signal classification) and ESPRIT (estimation of signal parameters via rotational invariance techniques) algorithms have been developed that have higher detection resolution than the FFT-based method, but it is difficult to satisfy the recently required parameter resolution even with the MUSIC algorithm and ESPRIT algorithm. Actually.

Accordingly, a method of detecting a parameter having a higher resolution than an FFT-based method, a MUSIC algorithm, and an ESPRIT algorithm in an FMCW radar system is requested.

The present invention estimates an angle parameter using an extrapolation algorithm, and by applying a RELEX algorithm to the estimated angle parameter, an apparatus for detecting an angle parameter having a higher resolution than the ultra-high resolution output from a conventional FMCW radar parameter detection device. And a method.

A parameter detection method according to an embodiment of the present invention includes the steps of: receiving a reception signal in which a transmission signal output from a radar is reflected to a target; Performing a fast Fourier transform (FFT) on the received signal; Estimating distance information between the target and the radar using the FFT result; Estimating angle information between a target and a radar by applying an extrapolation algorithm to the FFT result; And applying a RELAX (Relaxation) algorithm to the angle information to obtain ultra-high resolution angle information having an improved resolution than the angle information.

Estimating distance information of a parameter detection method according to an embodiment of the present invention includes: detecting a threshold value included in the FFT result; Extracting distance information between a target and a radar based on the threshold value; And obtaining an FFT size component corresponding to the distance information.

In the step of detecting the threshold value of the parameter detection method according to an embodiment of the present invention, at least one peak value is extracted from the FFT result using a fixed value, and the largest peak value among the extracted peak values is selected. It can be detected by a threshold value.

The step of performing the FFT of the parameter detection method according to an embodiment of the present invention may be performed by using a one-dimensional chirp index corresponding to a chirp included in a received signal and an antenna array index of an antenna receiving the received signal. You can perform dimensional FFT.

Estimating angle information of a parameter detection method according to an embodiment of the present invention includes: dividing the FFT result into a frequency spectrum; Extracting a data sequence for a signal in a subband by applying an inverse FFT to the spectrum division result; Estimating an autoregressive (AR) parameter from a chirp included in the received signal; Performing linear prediction using the estimated AR parameter; Modeling the data sequence using an autoregressive-moving average (ARMA) model using the linear prediction result; And estimating angle information using the ARMA model.

The step of obtaining the ultra-high resolution angle information of the parameter detection method according to an embodiment of the present invention includes inputting the extrapolated array and the extrapolated virtual array vector included in the angle information to the RELAX algorithm, and performing an operation using the output of the RELAX algorithm. The RELAX algorithm can be repeatedly performed until the maximum value is less than the preset RELAX threshold.

A parameter detection apparatus according to an embodiment of the present invention includes: a receiver for receiving a reception signal in which a transmission signal output from a radar is reflected to a target; An FFT performing unit for performing a fast Fourier transform (FFT) on the received signal and estimating distance information between a target and a radar using the FFT result; An extrapolation performing unit for estimating angle information between a target and a radar by applying an extrapolation algorithm to the FFT result; And a RELAX execution unit that obtains ultra-high resolution angle information having an improved resolution than the angle information by applying a RELAX (Relaxation) algorithm to the angle information.

The FFT performing unit of the parameter detection apparatus according to an embodiment of the present invention detects a threshold value included in the FFT result, extracts distance information between a target and a radar based on the threshold value, and corresponds to the distance information. The FFT size component can be obtained.

The extrapolation unit of the parameter detection apparatus according to an embodiment of the present invention divides the FFT result into a frequency spectrum, applies an inverse FFT to the spectrum division result, extracts a data sequence for a signal in a subband, and extracts a data sequence for a signal in a subband. The autoregressive (AR) parameter is estimated from the included chirp, linear prediction is performed using the estimated AR parameter, and the data sequence is modeled with an autoregressive-moving average (ARMA) model using the linear prediction result, Angle information can be estimated using the ARMA model.

The RELAX execution unit of the parameter detection apparatus according to an embodiment of the present invention inputs the extrapolated array and extrapolated virtual array vector included in the angle information into the RELAX algorithm, and the maximum value of the operation using the output of the RELAX algorithm is set in RELAX. The RELAX algorithm can be repeatedly performed until it is less than or equal to the threshold.

According to an embodiment of the present invention, by estimating an angular parameter using an extrapolation algorithm and applying a RELEX algorithm to the estimated angular parameter, Angle parameters can be detected.

1 is a diagram showing a parameter detection apparatus according to an embodiment of the present invention.
2 is an example of a received signal received by the parameter detection apparatus according to an embodiment of the present invention.
3 is an example of the complexity of a parameter detection apparatus according to an embodiment of the present invention.
4 is an example of estimated RMSE performance of a parameter detection apparatus according to an embodiment of the present invention.
5 is a flowchart showing a parameter detection method according to an embodiment of the present invention.

Hereinafter, embodiments will be described in detail with reference to the accompanying drawings. However, since various changes may be made to the embodiments, the scope of the patent application is not limited or limited by these embodiments. It is to be understood that all changes, equivalents, or substitutes to the embodiments are included in the scope of the rights.

The terms used in the examples are used for illustrative purposes only and should not be construed as limiting. Singular expressions include plural expressions unless the context clearly indicates otherwise. In the present specification, terms such as "comprise" or "have" are intended to designate the presence of features, numbers, steps, actions, components, parts, or combinations thereof described in the specification, but one or more other features. It is to be understood that the presence or addition of elements or numbers, steps, actions, components, parts, or combinations thereof does not preclude in advance.

In addition, in the description with reference to the accompanying drawings, the same reference numerals are assigned to the same components regardless of the reference numerals, and redundant descriptions thereof will be omitted. In describing the embodiments, when it is determined that a detailed description of related known technologies may unnecessarily obscure the subject matter of the embodiments, the detailed description thereof will be omitted.

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

1 is a diagram showing a parameter detection apparatus according to an embodiment of the present invention.

As shown in FIG. 1, the parameter detection apparatus 100 may include a receiver 110, an FFT performing unit 120, an extrapolation performing unit 130, and a RELAX performing unit 140. At this time, the receiver 110 is a communicator, and the FFT performing unit 120, the extrapolating unit 130, and the RELAX performing unit 140 are different processors, or each module included in a program executed by one processor. Can be

The receiver 110 may receive a received signal received by the radar 101 from the radar 101. For example, the radar 101 may be an FMCW radar. In addition, the radar 101 may output a transmission signal and receive a reception signal in which the transmission signal is reflected on a target.

The FFT performing unit 120 may perform a fast Fourier transform (FFT) on a received signal received by the receiver 110. In this case, the FFT performing unit 120 may perform a one-dimensional FFT using a one-dimensional chirp index corresponding to a chirp included in the received signal and an antenna array index of an antenna that has received the received signal.

이다. 이때, 레이더(101)에서 k는 수신 신호를 수신한 안테나 어레이의 인덱스이고, l은 수신 신호에 포함된 첩의 인덱스일 수 있다. 그리고, s번째 주파수 인덱스 기반의 거리 정보 FFT 출력은 수학식 1과 같이 나타낼 수 있다. _{For example, P kl, which} is a result of the FFT performing unit 120 performing a one-dimensional FFT, is

to be. In this case, in the radar 101, k may be an index of an antenna array that has received a received signal, and l may be an index of a chirp included in the received signal. In addition, the output of the distance information FFT based on the s-th frequency index may be expressed as Equation 1.

Here, W _N is a DFT matrix consisting of N column vectors of size N x 1. That is, W _N is [W ₀ , W ₁ , ..., W _{N-1 ], and W u, which} is a u-th column vector, can be expressed as in Equation 2.

In addition, the FFT performing unit 120 may estimate distance information between the target and the radar 101 by using the FFT result of the received signal. At this time, the FFT performing unit 120 detects a threshold value included in the FFT result of the received signal, extracts distance information between the target and the radar 101 based on the detected threshold value, and FFT corresponding to the extracted distance information. The size component can be obtained. In this case, the FFT performing unit 120 may extract at least one peak value from the FFT result using a fixed value, and detect the largest peak value among the extracted peak values as a threshold value.

_{For example, the FFT performing unit 120 may differentiate by applying P kl} , which is the FFT result of the received signal, to Equation 3.

In addition, the FFT performing unit 120 uses the differentiation result to determine the distance spectrum I = [I ₁ , I ₂ , ... , I _M ] can detect peaks between. In this case, the detected peak index may be distance information of the target.

The extrapolation performing unit 130 may estimate angle information between the target and the radar by applying an extrapolation algorithm to the FFT result of the received signal. In this case, the extrapolation performing unit 130 may divide the FFT result of the received signal into a frequency spectrum.

에 대한 DOA(direction of arrival)의 입력 및 주파수 변환된 DOA FFT 결과인

를 수학식 4와 같이 정의할 수 있다.For example, the m-th target in the l-th chirp symbol

The input and frequency-transformed DOA FFT result of the direction of arrival (DOA) for

Can be defined as in Equation 4.

는

이고,

는

일 수 있다.At this time,

Is

ego,

Is

Can be

At this time, the z-th subband z = 0, 1,… , The spectral partition matrix S _Z for Z-1 can be expressed as in Equation 5.

는 수학식 6과 같이 정의될 수 있다.At this time, the diagonal element included in Equation 1

May be defined as in Equation 6.

In this case, I _z may be a parameter set of an index defined as in Equation 7.

In this case, A may be the size of a sub band. That is, A = K / Z, and can have an integer value. In addition, the result of spectrum division in the z-th subband can be expressed as Equation 8.

에 역 FFT를 적용하여 수학식 9와 같은 z 번째 서브 밴드 내 신호에 대한 데이터 시퀀스를 획득할 수 있다.Next, the extrapolation performing unit 130 may extract a data sequence for a signal within a subband by applying an inverse FFT to the spectrum division result. For example, the extrapolation performing unit 130

By applying the inverse FFT to Equation 9, a data sequence for a signal in the z-th subband may be obtained.

는

일 수 있다.At this time,

Is

Can be

Next, the extrapolation performing unit 130 may estimate an autoregressive (AR) parameter from a chirp included in the received signal, and perform linear prediction using the estimated AR parameter. In this case, the extrapolation execution unit 130 may model the data sequence as an output of the linear system as shown in Equation 10.

Next, the extrapolation performing unit 130 may model the data sequence using an autoregressive-moving average (ARMA) model using the linear prediction result, and estimate angle information using the ARMA model. In this case, the ARMA model may be composed of an ARMA model having p and q values, an AR model, and a moving average (MA) model. At this time, among the three linear models, the AR model can be mainly used because the result of the AR model with q = 0 can show a sharp peak.

For example, the extrapolation performing unit 130 may model the received signal corresponding to the l-th chirp symbol and the m-th target as shown in Equation 11 using the AR model.

In this case, the power spectrum P x (z) of the p-order AR process may be defined as in Equation 12.

은 모델 파라미터가 얼마나 정확하게 추정 될 수 있는지에 따라 달라지는 변수이며,

은 모델링 오차일 수 있다.At this time, b(0) and a(k) can be estimated from the accuracy of data and spectrum estimation.

Is a variable that depends on how accurately the model parameter can be estimated,

May be a modeling error.

를 구하기 위하여 수학식 13과 같은 선형 방정식이 사용될 수 있다.A method of estimating an AR parameter for estimating distance information may be a covariance method. For example, the p-order AR parameter

A linear equation such as Equation 13 may be used to obtain.

In this case, the autocorrelation sequence c _x (k, l) may be defined as in Equation 14.

In the case of DOA spectrum estimation using the AR model, the extrapolation performing unit 130 may determine the order of the AR process p based on the number of targets M. If the order p is less than the number of target M, the result of the DOA spectrum may be smoothed and the resolution may be lowered. Also, when the order p is greater than the number of target M, the result of the DOA spectrum may have a false peak.

In addition, the extrapolation execution unit 130 may determine the model order p by using an Akaike information criterion (AIC) and a minimum description length (MDL), in this case, the extrapolation execution unit 130 has a value of MDL or AIC. You can select the optimal modeling order by changing the model order until it is minimized.

을 갖는 외삽 가상 어레이 벡터

가 RELAX 수행부(140)에 입력될 수 있다.And, since the extrapolation result of the extrapolation execution unit 130 has size and phase information, a plurality of extrapolation arrays K _E and

Extrapolated virtual array vector with

May be input to the RELAX execution unit 140.

The RELAX execution unit 140 may obtain ultra-high resolution angle information with improved resolution than the angle information estimated by the extrapolation execution unit 130 by applying a RELAX (Relaxation) algorithm to the angle information estimated by the extrapolation execution unit 130. have. At this time, the RELAX execution unit 140 inputs the extrapolated array and the extrapolated virtual array vector included in the angle information estimated by the extrapolation execution unit 130 into the RELAX algorithm, and the maximum value of the operation using the output of the RELAX algorithm is preset. The RELAX algorithm can be repeatedly performed until it is less than the RELAX threshold.

을 설정할 수 있다. 다만,

는 정의가 필요하지 않다.RELAX execution unit 140 first

Can be set. but,

Does not need a definition.

Next, the RELAX execution unit 140 m = 1,2,... During, M, Equation 15 may be repeatedly performed.

는

일 수 있다. 그리고, RELAX 수행부(140)는

의 최대 값이 사전 결정된 임계 값보다 작은 경우, 동작을 종료할 수 있다.At this time,

Is

Can be And, the RELAX execution unit 140

When the maximum value of is less than a predetermined threshold value, the operation may be terminated.

The parameter detection apparatus according to an embodiment of the present invention estimates the angle parameter using an extrapolation algorithm and applies the RELEX algorithm to the estimated angle parameter, thereby improving more than the ultra-high resolution output from the conventional FMCW radar parameter detection device. Angle parameters with resolution can be detected.

2 is an example of a received signal received by the parameter detection apparatus according to an embodiment of the present invention.

A transmission (TX) antenna of the radar 101 connected to the parameter detection device 100 may output a transmission (TX) signal 210.

For example, the transmission signal S _TX can be expressed as Equation 16.

In this case, L is the number of FMCW chirp signals, and T _F may indicate the total duration of chirp symbols and idle periods. In this case, T _F may be T + T _i , T may be the duration of the chirp symbol, and T _i may be the duration of the idle period. For example, the FMCW chirp symbol s ₀ (t) can be expressed as in Equation 17.

는 FMCW 첩 심볼의 주파수 기울기이며, f_BW는 FMCW의 대역폭일 수 있다.In this case, f _o is the initial frequency,

Is the frequency slope of the FMCW chirp symbol, and f _BW may be the bandwidth of the FMCW.

In this case, the transmission signal 210 is reflected by the target 220 and may be modulated. In addition, the reception (RX) antenna of the radar 101 may receive the transmitted signal 210 as a reception (RX) signal by reflecting the transmitted signal 210 on the target 220 and the modulated signal 230.

In this case, the received signal P _kl (t) includes a waveform 231 corresponding to each of the targets, and may be defined as in Equation 18.

는 m 번째 목표의 도플러 주파수이고,

는 가산 백색 가우스 잡음 (AWGN) 신호일 수 있다.At this time,

Is the Doppler frequency of the m-th target,

May be an additive white Gaussian noise (AWGN) signal.

3 is a graph showing the complexity 310 of the parameter detection apparatus of a conventional FMCW radar according to the number of received input samples and the complexity 320 of the parameter detection apparatus 100 according to an embodiment of the present invention.

As shown in FIG. 3, the parameter detection apparatus 100 according to an exemplary embodiment of the present invention may be more complex than a parameter detection apparatus of a conventional FMCW radar.

4 is an estimation RMSE performance 410 of a conventional FFT method parameter detection apparatus, an estimation RMSE performance 420 of a conventional MUSIC algorithm method parameter detection apparatus, and estimation of a parameter detection apparatus according to an embodiment of the present invention. It is a graph showing the RMSE performance 430.

Although the parameter detection apparatus 100 according to an embodiment of the present invention has improved complexity than that of the conventional FMCW radar parameter detection apparatus as shown in FIG. 3, RMSE performance may be improved as shown in FIG. 4.

5 is a flowchart showing a parameter detection method according to an embodiment of the present invention.

In step 510, the FFT performing unit 120 may perform a fast Fourier transform (FFT) on the received signal received from the radar 101 by the receiver 110. In this case, the FFT performing unit 120 may perform a one-dimensional FFT using a one-dimensional chirp index corresponding to a chirp included in the received signal and an antenna array index of an antenna that has received the received signal.

In step 520, the FFT performing unit 120 may estimate distance information between the target and the radar 101 by using the FFT result of the received signal. At this time, the FFT performing unit 120 detects a threshold value included in the FFT result of the received signal, extracts distance information between the target and the radar 101 based on the detected threshold value, and FFT corresponding to the extracted distance information. The size component can be obtained. In this case, the FFT performing unit 120 may extract at least one peak value from the FFT result using a fixed value, and detect the largest peak value among the extracted peak values as a threshold value.

In step 530, the extrapolation performing unit 130 may estimate the angle information between the target and the radar by applying an extrapolation algorithm to the FFT result of the received signal. In this case, the extrapolation performing unit 130 may divide the FFT result of the received signal into a frequency spectrum. Next, the extrapolation performing unit 130 may extract a data sequence for a signal within a subband by applying an inverse FFT to the spectrum division result. Next, the extrapolation performing unit 130 may estimate an autoregressive (AR) parameter from a chirp included in the received signal, and perform linear prediction using the estimated AR parameter. Next, the extrapolation performing unit 130 may model the data sequence using an autoregressive-moving average (ARMA) model using the linear prediction result, and estimate angle information using the ARMA model.

In step 540, the RELAX execution unit 140 applies a RELAX (Relaxation) algorithm to the angle information estimated by the extrapolation execution unit 130 to provide an ultra-high resolution angle with improved resolution than the angle information estimated by the extrapolation execution unit 130. Information can be obtained. At this time, the RELAX execution unit 140 inputs the extrapolated array and the extrapolated virtual array vector included in the angle information estimated by the extrapolation execution unit 130 into the RELAX algorithm, and the maximum value of the operation using the output of the RELAX algorithm is preset. The RELAX algorithm can be repeatedly performed until it is less than the RELAX threshold.

The present invention estimates angular parameters using an extrapolation algorithm and applies the RELEX algorithm to the estimated angular parameters, thereby detecting angular parameters having a higher resolution than the ultra-high resolution output from the conventional FMCW radar parameter detection device. have.

On the other hand, the parameter detection device or parameter detection method according to the present invention is written as a program that can be executed on a computer and can be implemented in various recording media such as a magnetic storage medium, an optical reading medium, and a digital storage medium.

Implementations of the various techniques described herein may be implemented in digital electronic circuitry, or in computer hardware, firmware, software, or combinations thereof. Implementations may include a data processing device, e.g., a programmable processor, a computer, or a computer program product, e.g., a machine-readable storage device (computer readable It can be implemented as a computer program tangibly embodied in a possible medium) Computer programs such as the above-described computer program(s) may be recorded in any type of programming language, including compiled or interpreted languages, and as a standalone program or in a module, component, subroutine, or computing environment. It can be deployed in any form, including as other units suitable for the use of. A computer program can be deployed to be processed on one computer or multiple computers at one site or to be distributed across multiple sites and interconnected by a communication network.

Processors suitable for processing a computer program include, by way of example, both general and special purpose microprocessors, and any one or more processors of any kind of digital computer. In general, the processor will receive instructions and data from read-only memory or random access memory or both. Elements of the computer may include at least one processor that executes instructions and one or more memory devices that store instructions and data. In general, a computer may include one or more mass storage devices that store data, such as magnetic, magnetic-optical disks, or optical disks, or receive data from or transmit data to them, or both. It can also be combined so as to be. Information carriers suitable for embodying computer program instructions and data are, for example, semiconductor memory devices, for example, magnetic media such as hard disks, floppy disks and magnetic tapes, Compact Disk Read Only Memory (CD-ROM). ), Optical Media such as DVD (Digital Video Disk), Magnetic-Optical Media such as Floptical Disk, ROM (Read Only Memory), RAM (RAM) , Random Access Memory), flash memory, EPROM (Erasable Programmable ROM), EEPROM (Electrically Erasable Programmable ROM), and the like. The processor and memory may be supplemented by or included in a special purpose logic circuit structure.

Further, the computer-readable medium may be any available medium that can be accessed by a computer, and may include all computer storage media.

While this specification includes details of a number of specific implementations, these should not be construed as limiting to the scope of any invention or claimable, but rather as a description of features that may be peculiar to a particular embodiment of a particular invention. It must be understood. Certain features described herein in the context of separate embodiments may be implemented in combination in a single embodiment. Conversely, various features described in the context of a single embodiment can also be implemented in multiple embodiments individually or in any suitable sub-combination. Furthermore, although features operate in a particular combination and may be initially described as so claimed, one or more features from a claimed combination may in some cases be excluded from the combination, and the claimed combination may be a sub-combination. Or sub-combination variations.

Likewise, although operations are depicted in the drawings in a specific order, it should not be understood that such operations must be performed in that particular order or sequential order shown, or that all illustrated operations must be performed in order to obtain a desired result. In certain cases, multitasking and parallel processing can be advantageous. In addition, separation of the various device components in the above-described embodiments should not be understood as requiring such separation in all embodiments, and the program components and devices described are generally integrated together into a single software product or packaged in multiple software products. It should be understood that you can.

On the other hand, the embodiments of the present invention disclosed in the specification and drawings are merely presented specific examples to aid understanding, and are not intended to limit the scope of the present invention. It is apparent to those of ordinary skill in the art that other modified examples based on the technical idea of the present invention may be implemented in addition to the embodiments disclosed herein.

101: radar
110: receiver
120: FFT execution unit
130: extrapolation execution unit
140: RELAX execution unit

Claims (11)

Receiving a reception signal in which the transmission signal output from the radar is reflected on the target;
Performing a fast Fourier transform (FFT) on the received signal;
Estimating distance information between the target and the radar using the FFT result;
Estimating angle information between a target and a radar by applying an extrapolation algorithm to the FFT result; And
Applying a RELAX (Relaxation) algorithm to the angle information to obtain ultra-high resolution angle information with improved resolution than the angle information
Radar parameter estimation method comprising a. The method of claim 1,
Estimating the distance information,
Detecting a threshold value included in the FFT result;
Extracting distance information between a target and a radar based on the threshold value; And
Obtaining an FFT size component corresponding to the distance information
Radar parameter estimation method comprising a. The method of claim 2,
The step of detecting the threshold value,
A radar parameter estimation method in which at least one peak value is extracted from the FFT result using a fixed value, and the largest peak value among the extracted peak values is detected as a threshold value. The method of claim 1,
The step of performing the FFT,
A radar parameter estimation method for performing a one-dimensional FFT using a one-dimensional chirp index corresponding to a chirp included in a received signal and an antenna array index of an antenna receiving the received signal. The method of claim 1,
Estimating the angle information,
Dividing the FFT result into a frequency spectrum;
Extracting a data sequence for a signal in a subband by applying an inverse FFT to the spectrum division result;
Estimating an autoregressive (AR) parameter from a chirp included in the received signal;
Performing linear prediction using the estimated AR parameter;
Modeling the data sequence using an autoregressive-moving average (ARMA) model using the linear prediction result; And
Estimating angle information using an ARMA model
Radar parameter estimation method comprising a. The method of claim 1,
The step of obtaining the ultra-high resolution angle information,
A radar parameter estimation method in which the extrapolated array and the extrapolated virtual array vector included in the angle information are input to the RELAX algorithm, and the RELAX algorithm is repeatedly performed until the maximum value of the operation using the output of the RELAX algorithm is less than or equal to a preset RELAX threshold. A computer-readable recording medium on which a program for executing the method of any one of claims 1 to 6 is recorded. A receiver for receiving a reception signal in which the transmission signal output from the radar is reflected on the target;
An FFT performing unit for performing a fast Fourier transform (FFT) on the received signal and estimating distance information between a target and a radar using the FFT result;
An extrapolation performing unit for estimating angle information between a target and a radar by applying an extrapolation algorithm to the FFT result; And
A RELAX execution unit that applies a RELAX (Relaxation) algorithm to the angle information to obtain ultra-high resolution angle information with improved resolution than the angle information
Radar parameter estimation device comprising a. The method of claim 8,
The FFT performing unit,
A radar parameter estimation apparatus for detecting a threshold value included in the FFT result, extracting distance information between a target and a radar based on the threshold value, and obtaining an FFT size component corresponding to the distance information. The method of claim 8,
The extrapolation performing unit,
Dividing the FFT result into a frequency spectrum, applying an inverse FFT to the spectrum division result, extracting a data sequence for a signal in a subband, estimating an autoregressive (AR) parameter from a chirp included in the received signal, A radar parameter estimation device that performs linear prediction using the estimated AR parameter, models a data sequence using an autoregressive-moving average (ARMA) model using the linear prediction result, and estimates angular information using an ARMA model. The method of claim 8,
The RELAX execution unit,
A radar parameter estimation device that repeatedly performs the RELAX algorithm until the extrapolated array and the extrapolated virtual array vector included in the angle information are input to the RELAX algorithm, and the maximum value of the operation using the output of the RELAX algorithm is less than or equal to a preset RELAX threshold.

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