KR102400235B1 - Target location determine method with low-complexity of spectrum - Google Patents

Abstract

The present invention relates to a target positioning method using a low-complexity spectrum, and the target positioning method tracks a fixed or moving single/plural target located in the attention of a vehicle in real time using radar.

Description

TARGET LOCATION DETERMINE METHOD WITH LOW-COMPLEXITY OF SPECTRUM

The present invention relates to a method for determining a target location using a low-complexity spectrum, and more particularly, to a method for determining a location of a target by improving the detection performance of a frequency based on a radar.

The FMCW radar system is a system for estimating distance-velocity information for multiple targets by using the time-frequency domain at the same time. In addition, the FMCW radar system has very high bandwidth efficiency and low complexity, so it is considered as a major technology for automotive radar systems. In the FMCW radar system, since the beat frequency is determined according to the distance and speed of each target, it is a very important issue to accurately estimate the frequency component. In this case, in the FMCW radar system, the FFT method is most widely used for frequency component estimation.

However, the FFT-based schemes do not have high resolution, so when a plurality of targets are adjacent to each other, the probability of recognition as a single target is high. Since the vehicle radar system requires high resolution and long-range detection conditions, there is a problem in that it is difficult to satisfy various requirements for vehicle detection in the case of the existing 24 GHz ISM band radar.

To improve this, an ultra-high-resolution frequency detection algorithm such as estimation of signal parameters via rotational invariance techniques (ESPRIT) or multiple signal classification (MUSIC) has been proposed. However, the proposed methods have a problem of very high computational complexity, so they are not suitable for vehicles that estimate surrounding objects in real time. In addition, the proposed schemes achieve excellent performance only in a high SNR condition, and there is a disadvantage in that the performance is degraded in a low SNR environment.

Accordingly, in order to improve the estimation performance in the low SNR condition, an algorithm for increasing the resolution using SP (spectrum partitioning) has been developed. The SP-based high-resolution algorithm divides the spectrum into multiple partial spectra, and applies the high-resolution algorithm to each subband. However, since the complexity of the SP-based high-resolution algorithm is very high and it is difficult to implement in real time, it is not suitable for vehicles.

Therefore, there is a need for a method capable of determining a target in real time in a vehicle using low complexity.

The present invention provides a method for determining a target position for detecting the position of a target in real time using a high-resolution algorithm based on low-complexity spectral division.

The present invention provides a target positioning method for determining the positions of a plurality of adjacent targets using a high-resolution algorithm under a condition having a low signal to noise ratio (SNR).

A method for determining a target location according to an embodiment includes: applying a discrete Fourier transform (DFT) to a radar signal reflected from N targets; dividing the radar signal to which the discrete Fourier transform is applied into S subbands using S windowings; determining indices for M targets by comparing output signals of each of the subbands divided by windowing with a threshold signal; storing output signals of subbands corresponding to the index in a memory; applying M inverse Discrete Fourier Transforms (IDFTs) to data stored in a memory; generating a spectrum using data to which an inverse discrete Fourier transform is applied; and determining the positions of the N targets from the generated spectrum.

In the determining of the index according to an embodiment, an index indicating a signal-based spectral index vector for the N targets may be determined.

Determining the index according to an embodiment may include: comparing a magnitude of an output signal of each of the subbands divided by windowing with a magnitude value of a threshold signal; and when the magnitude of the output signal indicates a high value, recognizing the output signal indicating the high value as a target.

The storing in the memory may include storing the output signals of the subbands in the memory based on the eigenvector included in the signal subspace of the N * N correlation matrix.

The generating of the spectrum according to an embodiment may include generating a spectrum of a noise reduction signal for a subband by using an eigenvalue of an eigenvector.

The determining according to an embodiment may include: detecting peak values for N targets through a spectrum; And it is possible to determine the positions of the N targets by using the delay times of the N targets according to the detected peak values.

According to an embodiment of the present invention, the target location determination method can detect the target location in real time by using a low-complexity spectral division-based high-resolution algorithm.

According to an embodiment of the present invention, the target positioning method may determine the positions of a plurality of adjacent targets by using a high-resolution algorithm in a condition having a low signal to noise ratio (SNR).

1 is an overall configuration diagram for determining the position of a target according to an embodiment of the present invention.
2 is a configuration diagram for explaining a detailed configuration of a radar device according to an embodiment of the present invention.
3 is a graph illustrating a spectrum range for a single target according to an embodiment of the present invention.
4 is a graph illustrating spectral ranges for a plurality of targets according to an embodiment of the present invention.
5 is a flowchart of a method for determining a target location according to an embodiment of the present invention.

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

1 is an overall configuration diagram for determining the position of a target according to an embodiment of the present invention.

Referring to FIG. 1 , a radar device 101 is a method for determining a target location using a spectrum having a low complexity based on a multiple signal classification algorithm (MUSIC) for radar that estimates a target located around a vehicle. can suggest Here, the multi-signal classification technique is a widely used method in the array signal processing problem. By dividing the space consisting of data measured by an antenna or sensor into a signal space and a noise space, vectors for the positions of signal sources are orthogonal to the noise space. A method of tracking a location using a At this time, in order to separate the signal space and the noise space, there should be no interference of the waveforms on the time axis of the signal sources.

In addition, the radar device 101 in the present invention is a device implemented in a vehicle FMCW (Frequency Modulation Continuous Wave) radar method, and may be a radar device that emits a radar signal as a continuous wave, and the radar signal continuously changes with time.

The radar device 101 may detect N targets fixed or moving around the vehicle by using the tracking range and resolution of the radar. In order to improve the radar tracking range, the radar device 101 may estimate a signal parameter related to a target according to a multi-signal classification technique. In this case, in estimating signal parameters in the prior art, the radar apparatus 101 estimates the signal parameters with degraded performance at a low signal-to-noise ratio by using a high-resolution based algorithm. Accordingly, the radar apparatus 101 of the present invention can improve the accuracy of the estimation range and the resolution performance in a low signal-to-noise ratio environment by using a low-complexity SP-based multi-signal classification technique.

To this end, the radar device 101 may divide a spectrum into subbands of a multi-spectrum based on an SP-based high-resolution algorithm, and the high-resolution algorithm may use each subband signal. In addition, positions of a plurality of targets may be estimated using each subband signal.

라 할 때,

은 다음의 수학식 1과 같이 나타낼 수 있다.When there are N targets around the vehicle 102 , the radar device 101 may generate a continuous radar signal in a space in which the N targets are located. In addition, the radar device 101 may receive the radar signal reflected by the N targets, and may perform analog-digital converter (ADC) on the received radar signal. Here, the nth component of the radar signal to which the analog-to-digital conversion is applied

When you say

can be expressed as in Equation 1 below.

은

번째 타겟의 복소 진폭 성분을 나타내고,

는 첩심볼의 변화율을 나타내며,

는 레이더 신호의 샘플링 주기를 나타낼 수 있다. 그리고,

는 FMCW 기반의 레이더 장치(101)의 초기 주파수를 나타내고,

은

번째 타겟의 거리에 따른 지연시간을 나타내며,

은 부가 백색 가우스 잡음(AWGN: Additive white Gaussian noise)성분을 나타낼 수 있다. 또한, 레이더 신호에 대한

의 N point DFT(Discrete Fourier Transform) 출력 가운데

번째 성분을

로 표기할 때,

은 다음의 수학식 2와 같이 나타낼 수 있다. here,

silver

represents the complex amplitude component of the th target,

represents the rate of change of the chirp symbol,

may represent a sampling period of the radar signal. And,

represents the initial frequency of the FMCW-based radar device 101,

silver

It represents the delay time according to the distance of the second target,

may represent an additive white Gaussian noise (AWGN) component. In addition, for radar signals

Center the N-point Discrete Fourier Transform (DFT) output of

the second ingredient

When denoted as

can be expressed as in Equation 2 below.

That is, the radar apparatus 101 may apply the discrete Fourier transform to the radar signals reflected from the N targets by using Equation (2). The radar device 101 may divide the radar signal to which the discrete Fourier transform is applied into S subbands. Here, the radar device 101 may divide the radar signal into S subbands using S windowings. Here, in the windowing, in selecting a specific section of the digital signal x[n], an operation of increasing w[n] may be referred to as a windowing operation. That is, the radar device may divide the radar signal to which the discrete Fourier transform is applied into S subbands using windowing as a method for preventing spectrum distortion due to discontinuity during signal restoration.

The radar device 101 compares the output signal of each of the subbands divided by windowing with the threshold signal to determine the indexes for the M targets, and stores the output signals of the subbands corresponding to the determined indexes in the memory. there is.

The radar apparatus 101 may apply M inverse discrete Fourier transforms (IDFTs) to data stored in the memory, and generate a spectrum using the data to which the inverse discrete Fourier transform is applied. In this case, the radar apparatus 101 may generate a final spectrum by applying a multi-signal classification technique (MUSIC) to the M IDFT results, that is, data to which the inverse discrete Fourier transform is applied, and then performing summing. Thereafter, the radar device 101 may determine the positions of the N targets from the spectrum.

As a result, the radar apparatus uses a low-complexity spectrum-based multi-signal classification technique for automotive radar, and through this, reduces the noise effect included in the radar signal, thereby improving positional accuracy for a plurality of targets.

2 is a configuration diagram for explaining a detailed configuration of a radar device according to an embodiment of the present invention.

Referring to FIG. 2 , the radar device may include a signal converter 203 , a spectrum divider, and a spectrum generator.

The radar device may generate a continuous type of radar signal in a space in which N targets are located. When receiving the radar signal reflected by the N targets, the signal converter 203 may convert the received radar signal from an analog form to a digital form.

More specifically, the FMCW signal transmitted from the radar device of the present invention to the vehicle radar may be expressed as in Equation 3 below.

는 레이더 장치의 초기 주파수를 나타내고,

는 처프 심볼의 순간 주파수의 변화율을 나타내며,

는 FMCW 기반의 레이더 신호의 대역폭이며,

은 FMCW의 기호를 나타낼 수 있다. 자세하게, 차량이 이동하는 도로 환경에서 N 개의 표적을 고려하고, m 번째 표적의 왕복 시간 지연을 τ m이라 가정할 때,

에 대한 시간 - 불변 채널의 신호는 수학식 4과 같이 나타날 수 있다..here,

represents the initial frequency of the radar device,

represents the rate of change of the instantaneous frequency of the chirp symbol,

is the bandwidth of the FMCW-based radar signal,

may represent the symbol of FMCW. In detail, considering N targets in the road environment on which the vehicle moves, and assuming that the round-trip time delay of the m-th target is τ m,

The signal of the time-invariant channel with respect to can be expressed as Equation (4).

은 m 번째 타겟에 대한 복소 진폭을 나타내고,

는 부가 백색 가우스 잡음일 수 있다. 여기서, 수신한 레이더 신호에서의 디처링(dechirping)은 수신 된 FMCW의 레이더 신호

의 곱과 송신된 FMCW의 레이더 신호

의 공액으로 정의될 수 있다. 이는 수학식 5와 같이 나타낼 수 있다. 여기서, 디처링은 주파수 변화율을 역으로 보상하는 선형 기법으로 시간에 따라 변화하는 신호를 일정한 주파수 특성을 유지할 수 있도록 변환하는 기법으로, 레이더 신호에 포함된 잔향 신호를 효율적으로 백색화할 수 있다.here

denotes the complex amplitude for the m-th target,

may be additive white Gaussian noise. Here, dechirping in the received radar signal is the received radar signal of the FMCW.

The product of , and the radar signal of the transmitted FMCW

can be defined as the conjugate of This can be expressed as Equation 5. Here, the decharging is a linear technique for inversely compensating for a frequency change rate, and is a technique for converting a time-varying signal to maintain a constant frequency characteristic, and it is possible to efficiently whiten the reverberation signal included in the radar signal.

는

가 정현파 형태로 변환됨을 나타냅니다. 수학식 5를 통해 변환된 레이더 신호

는 수학식 6에 따른 정현파 형태의 파형으로 나타날 수 있다.here,

Is

indicates that is converted to sinusoidal form. Radar signal converted through Equation 5

may be expressed as a waveform in the form of a sinusoid according to Equation (6).

가 주어진다고 가정할 때, 신호 변환기(203)를 통해 수신되는

의 이산 시간 모델

은 다음의 수학식 7과 같이 나타낼 수 있다.with Nyquist sampling frequency

Assuming that is given, the signal received through the converter 203 is

The discrete-time model of

can be expressed as in Equation 7 below.

In the radar device, the radar signal passing through the signal converter 203 may be subjected to discrete Fourier transform (DFT) through the discrete Fourier transform 204 .

As for the signal, the discrete Fourier transform 204 may detect a plurality of radar signals reflected from N targets, and the radar signal to which the discrete Fourier transform is applied may be expressed as Equation 8.

은 레이더 신호에 의한 타겟으로부터의 시간 지연을 추정하기 위한 이산 주파수의 인덱스를 나타낼 수 있다.here,

may represent the index of the discrete frequency for estimating the time delay from the target by the radar signal.

스펙트럼 샘플에 대응하는 S 개의 서브 대역들로 이산 푸리에 변환이 적용된 레이더 신호를 분할할 수 있다. 여기서, 표기법 [x]는 x보다 큰 가장 작은 정수를 나타낼 수 있다.Then, the present invention uses S windowings 205 according to the spectrum,

The radar signal to which the discrete Fourier transform is applied may be divided into S subbands corresponding to the spectral sample. Here, the notation [x] may represent the smallest integer greater than x.

The present invention can compare the output signal of each of the S subbands with the threshold signal 207 using the S comparators 206 . That is, the comparator 206 may determine indices for the M targets by comparing the threshold signal 207 with the radar signal to which the discrete Fourier transform is applied as output signals of each of the S subbands.

In the present invention, the output signal of the subbands including the index can be stored in the memory through the signal selector 208 . Here, in the present invention, when there are N multiple targets through the signal selector 208, a signal-based spectral exponential vector can be obtained. Then, it is possible to define a spectrum partition matrix for the s-th subband (s = 0, 1, 2, . . . , S-1) of the region of interest. This can be expressed as Equation (9).

에 의해 획득할 수 있으며,

는 수학식 10과 같이 나타낼 수 있다.Here, the diagonal element is

can be obtained by

can be expressed as in Equation (10).

은 전체 부대 역 중에서 선택된 윈도우 신호에 대한 인덱스의 집합을 나타낼 수 있다. 그리고,

은 수학식 11과 같이 나타낼 수 있다.here,

may represent a set of indices for a window signal selected from among all subbands. And,

can be expressed as in Equation (11).

When the signal-based window processing according to the spectrum partition matrix is completed, the present invention can obtain a result as in Equation 12.

및

는 s 번째 서브 밴드에 대한 윈도윙을 갖는 DFT 결과를 정의하고,

는 DFT 결과에 대한 전치(transpose)이다. 신호 선택기(208)의 스위치에 따른 스펙트럼 결과는 크기

의 메모리 블록에 저장될 수 있다.here

and

define the DFT result with windowing for the s-th subband,

is the transpose for the DFT result. The spectral result according to the switch of the signal selector 208 is

may be stored in a memory block of

In the present invention, M inverse discrete Fourier transforms (IDFTs) may be applied to data stored in a memory. In other words, the present invention uses the DFT result with the windowing selected by the signal selector 208 and stores it in a memory block, so that the inverse DFT (IDFT) block for the s-th subband can be expressed as: can

는 인덱스 s와 n을 가진 메모리에 저장될 수 있다. 예를 들어

와

는 각각 MEM00과 MEM11에 저장 될 수 있다. 다중 신호 분류 기법(MUSIC)은 감소된 잡음 전력 및

샘플을 갖는 신호 D를 사용하여 처리할 수 있다. N × N의 자기 상관 행렬은 다음의 수학식 14와 같이 나타낼 수 있다.here

can be stored in memory with indices s and n. for example

Wow

can be stored in MEM00 and MEM11, respectively. Multiple Signal Classification Technique (MUSIC) provides reduced noise power and

It can be processed using signal D with samples. An N × N autocorrelation matrix can be expressed as in Equation 14 below.

로 나타낼 수 있으며,

의 고유치 분해(EVD: eigenvalue decomposition)는 다음의 수학식 15와 같이 나타낼 수 있다.Here, the sequence is

can be expressed as

The eigenvalue decomposition (EVD) of ? can be expressed as Equation 15 below.

은 상관 행렬의 신호 부분 공간에 걸쳐있는 M 개의 고유 벡터들을 포함하고, 잡음 고유 벡터 행렬

은 상관 행렬의 잡음 부분 공간에 걸쳐있는 고유 벡터를 포함하며,

은

의 n 번째 고유 값을 나타낼 수 있다.where the signal eigenvector matrix

contains M eigenvectors spanning the signal subspace of the correlation matrix, and the noise eigenvector matrix

contains the eigenvectors spanning the noise subspace of the correlation matrix,

silver

can represent the nth eigenvalue of .

의 가장 큰 M 개의 고유치는

의 M 고유 벡터에 해당할 수 있다. 그리고, 다른 고유치

은

가 되도록 하는

의 고유 벡터에 대응할 수 있다. 수학식 5에 기초한 s 번째 서브 밴드에 대한 잡음 감소 신호의 주파수 스펙트럼은 다음의 수학식 16과 같이 나타낼 수 있다.

The largest M eigenvalues of

may correspond to the M eigenvector of . and other eigenvalues

silver

to become

can correspond to the eigenvector of . The frequency spectrum of the noise reduction signal for the s-th subband based on Equation 5 may be expressed as Equation 16 below.

는

다음과 같이 정의될 수 있다. SP에 기반한 모든 MUSIC 결과가 합산 된 후에, 제안된 스펙트럼에서 피크 검출을 찾기 위해 N 개의 타겟의 시간 지연

를 최종적으로 획득할 수 있다. 다시 말해, 본 발명은 스펙트럼을 통해 N개의 타겟에 대한 피크 값을 검출하고, 검출된 피크값에 따른 N개의 타겟의 지연 시간을 획득함으로써, 지연 시간에 의한 N개의 타겟의 위치를 결정할 수 있다.here,

Is

It can be defined as After all MUSIC results based on SP are summed, time delay of N targets to find peak detection in the proposed spectrum.

can finally be obtained. In other words, the present invention detects peak values for the N targets through a spectrum and obtains delay times of the N targets according to the detected peak values, thereby determining the positions of the N targets by the delay times.

3 is a graph illustrating a spectrum range for a single target according to an embodiment of the present invention.

The graph of Figure 3 shows the DFT method, the conventional multi-signal classification method and the multi-signal classification method proposed through the present invention in an AWGN environment showing a center frequency of 24 GHz, a frequency change rate of 2.5 × 1012, a sampling interval of 200 ns, and a symbol period of 80. It may be the result of performing a simulation experiment. Here, the condition of the simulation is a condition of a low signal-to-noise ratio, SNR=-22 dB, and the detection result in the case of a single target is shown.

At this time, it can be seen that the peak value can be observed in the DFT method and the multi-signal classification technique proposed by the present invention, whereas the peak value cannot be observed in the conventional multiple-signal classification technique because the SNR is low.

4 is a graph illustrating spectral ranges for a plurality of targets according to an embodiment of the present invention.

The graph of FIG. 4 shows the detection result in the case where SNR = 10 dB and the number of targets is 2 under the conditions of the signal-to-noise ratio in the same environment as that of FIG. 3 . In the case of the detection result according to FIG. 4, it can be seen that peaks for two targets exist in all three methods, unlike in the low SNR region.

However, it can be seen that the peak detected by the DFT method and the conventional multi-signal classification technique is not sharp, and there is a difference from the actual distance. On the other hand, it can be seen that the peak detected by the multi-signal classification method proposed by the present invention is not only sharper than the peak of the DFT method and the conventional multi-signal classification method, but also approximates the actual value.

As a result, the peak detected by the multi-signal classification technique proposed through the present invention is more distinct than the peaks of other conventional algorithms and is closest to the actual value, and this characteristic is also found in the measured results as shown in section V.

5 is a flowchart of a method for determining a target location according to an embodiment of the present invention.

In step 501, the radar apparatus may apply a discrete Fourier transform (DFT) to the radar signal reflected from the N targets. The radar apparatus may perform analog-to-digital conversion on the radar signal reflected from the N targets, and discrete Fourier-transform the analog-to-digital converted radar signal.

In step 502, the radar apparatus may divide the radar signal to which the discrete Fourier transform is applied into S subbands using S windowings.

In step 503, the radar apparatus may determine the index for the M targets by comparing the output signal of each of the subbands divided by windowing with the threshold signal. The radar device may determine an index indicating a signal-based spectral index vector for the N targets.

Also, the radar device may compare the magnitude of the output signal of each of the subbands divided by the windowing with the magnitude value of the threshold signal. Here, when the magnitude of the output signal indicates a high value, the radar device may recognize the output signal indicating the high value as the target. Conversely, when the magnitude of the output signal indicates a low value, the radar device may recognize that the output signal indicating the low value is not a target.

The radar apparatus may determine indices for the M targets in response to an output signal recognized as a target.

In step 504 , the radar device may store output signals of subbands corresponding to the index in the memory. The radar device may store the output signals of the subbands in the memory based on the eigenvector included in the signal subspace of the N*N correlation matrix.

In step 505 , the radar device may apply M inverse discrete Fourier transforms (IDFTs) to data stored in the memory.

In step 506 , the radar device may generate a spectrum using data to which the inverse discrete Fourier transform is applied. The radar device may generate a final spectrum by applying a multi-signal classification technique (MUSIC) to data to which the inverse discrete Fourier transform is applied, and then performing summing.

In step 507, the radar device may determine the positions of the N targets from the spectrum. The radar device may detect peak values for the N targets through the spectrum. In addition, the radar apparatus may determine the positions of the N targets by using the delay times of the N targets according to the detected peak values.

The present invention relates to a method for determining a target location for reducing the noise effect on an automobile radar. By proposing an algorithm using a low-complexity spectrum for detecting a signal region among subbands of a multi-spectrum, a low-complexity radar device The result of a multi-signal classification technique that is robust to the signal-to-noise ratio can be obtained. In this way, it is possible to accurately recognize the positions of multiple targets.

The present invention uses the MUSIC algorithm for detecting the positions of single and multiple targets in a low SNR environment. By implementing the proposed algorithm composed of the existing FFT or MUSIC in the embedded system of the vehicle radar, the parameter performance of the vehicle radar can be improved. can

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 be implemented for processing by, or controlling the operation of, a data processing device, eg, a programmable processor, computer, or number of computers, a computer program product, ie an information carrier, eg, a machine readable storage It may be implemented as a computer program recorded on an apparatus (computer-readable medium). A computer program, such as the computer program(s) described above, may be written in any form of programming language, including compiled or interpreted languages, 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 use in A computer program may be deployed to be processed on one computer or multiple computers at one site or distributed across multiple sites and interconnected by a communications 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, a processor will receive instructions and data from either read-only memory or random access memory or both. Elements of a 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 for storing data, for example magnetic, magneto-optical disks, or optical disks, receiving data from, sending data to, or both. may be combined to become 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 recording media such as DVD (Digital Video Disk), magneto-optical media such as optical disk, ROM (Read Only Memory), RAM (RAM) , Random Access Memory), flash memory, EPROM (Erasable Programmable ROM), EEPROM (Electrically Erasable Programmable ROM), and the like. Processors and memories may be supplemented by, or included in, special purpose logic circuitry.

In addition, the computer-readable medium may be any available medium that can be accessed by a computer, and may include both computer storage media and transmission media.

While this specification contains numerous specific implementation details, they should not be construed as limitations on the scope of any invention or claim, but rather as descriptions of features that may be specific to particular embodiments of particular inventions. should be understood Certain features that are described herein in the context of separate embodiments may be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment may also be implemented in multiple embodiments, either individually or in any suitable subcombination. Further, although features operate in a particular combination and may be initially depicted as claimed as such, one or more features from a claimed combination may in some cases be excluded from the combination, the claimed combination being a sub-combination. or a variant of a sub-combination.

Likewise, although acts are depicted in the drawings in a particular order, it should not be construed that all acts shown must be performed or that such acts must be performed in the specific order or sequential order shown to obtain desirable results. In certain cases, multitasking and parallel processing may be advantageous. Further, the separation of the various device components of the above-described embodiments should not be construed as requiring such separation in all embodiments, and the program components and devices described may generally be integrated together into a single software product or packaged into multiple software products. You have to understand that you can.

On the other hand, the embodiments of the present invention disclosed in the present specification and drawings are merely presented as specific examples to aid understanding, and are not intended to limit the scope of the present invention. It will be apparent to those of ordinary skill in the art to which the present invention pertains that other modifications based on the technical spirit of the present invention can be implemented in addition to the embodiments disclosed herein.

Claims (6)

applying a Discrete Fourier Transform (DFT) to the radar signals reflected from the N targets;
dividing the radar signal to which the discrete Fourier transform is applied into S subbands using S windowings;
determining indices for M targets by comparing output signals of each of the subbands partitioned by windowing with a threshold signal;
storing output signals of subbands including the index in a memory;
applying M inverse Discrete Fourier Transforms (IDFTs) to the data stored in the memory;
generating a spectrum using the data to which the inverse discrete Fourier transform is applied; and
Determining the positions of the N targets from the generated spectrum
A target positioning method comprising a. According to claim 1,
The step of determining the index comprises:
A target positioning method for determining an index indicating a signal-based spectral exponential vector for N targets. According to claim 1,
The step of determining the index comprises:
comparing the magnitude of the output signal of each of the subbands divided by the windowing with the magnitude value of the threshold signal; and
Recognizing the output signal indicating the high value as a target when the magnitude of the output signal indicates a high value
A target positioning method comprising a. According to claim 1,
Storing in the memory comprises:
A method for determining a target location for storing output signals of subbands in a memory based on an eigenvector included in a signal subspace of an N * N correlation matrix. 5. The method of claim 4,
The step of generating the spectrum comprises:
A method for determining a target location for generating a spectrum of a noise reduction signal for a subband by using the eigenvalue of the eigenvector. According to claim 1,
The determining step is
detecting peak values for N targets through the spectrum; and
A target positioning method for determining the positions of the N targets by using the delay times of the N targets according to the detected peak values.

Images