KR102371275B1 - Efficient Algorithm to Model Time-domain Signal Based on Physical Optics and Scenario-based Simulation Method and Apparatus for Automotive Vehicle Radar - Google Patents

Abstract

A physics-optical-based efficient time-domain signal generation algorithm and a scenario-based autonomous vehicle radar simulation method and apparatus are presented. An autonomous vehicle radar simulation method according to an embodiment may include calculating electromagnetic scattering with vehicle model data; calculating a time-domain response signal based on the calculated electromagnetic scattering; and generating a Range-Doppler (RD) map of the vehicle in a scenario in which the radar and the vehicle move based on the calculated time domain response signal.

Description

Efficient Algorithm to Model Time-domain Signal Based on Physical Optics and Scenario-based Simulation Method and Apparatus for Automotive Vehicle Radar}

The following embodiments relate to an algorithm for generating a time domain signal of a moving object such as a vehicle, and more particularly, an efficient time domain signal generation algorithm based on physics optics and a scenario-based autonomous vehicle radar simulation method and devices.

With the development of autonomous driving, the need for more accurate vehicle simulator development is emerging amid frequent autonomous driving-related accidents. The signal processing method of vehicle radar generally uses a frequency modulated continuous wave (FMCW) signal and estimates the target distance and speed through matched filter and Doppler processing. In a dynamic environment in which autonomous vehicles and radars move, it is very important to simulate autonomous vehicles by considering various scenarios such as RF specifications of the radar and the moving direction and speed of the vehicle.

Gibson, W. C., The method of moments in electromagnetics. CRC press, 2014.

Embodiments describe a physics-optical-based efficient time-domain signal generation algorithm and a scenario-based autonomous vehicle radar simulation method and apparatus, and more specifically, calculate electromagnetic scattering with real vehicle model data, and time based on this It provides a technique for calculating the domain response.

Embodiments calculate the incident angle in the local coordinate system of the target vehicle in consideration of the angle at which the radar faces the target vehicle whenever the vehicle moves, and then perform a numerical analysis of the electromagnetic field of the target using physical optics at the corresponding angle. An object of the present invention is to provide an autonomous vehicle radar simulation method and apparatus for calculating the magnitude of a radar reception signal based on an equation.

An autonomous vehicle radar simulation method according to an embodiment may include calculating electromagnetic scattering with vehicle model data; calculating a time-domain response signal based on the calculated electromagnetic scattering; and generating a Range-Doppler (RD) map of the vehicle in a scenario in which the radar and the vehicle move based on the calculated time domain response signal.

The step of calculating electromagnetic scattering with the vehicle model data includes calculating an incident angle based on the local coordinate system of the target vehicle in consideration of the angle at which the radar faces the target vehicle whenever the vehicle moves, and then using the incident angle as a physical optical ( and calculating a scattering matrix by performing numerical analysis of the electromagnetic field of the target vehicle using physical optics.

The calculating of electromagnetic scattering with the vehicle model data may further include calculating a magnitude of a time-domain radar reception signal in which polarization is considered based on a radar equation using the scattering matrix.

In the calculating of the time domain response signal, since distances between the meshes of the vehicle and the radar are different, a range bin is formed to a predetermined size, and then a range bin is entered into the bin. An impulse function may be created by finding meshes and adding the signal magnitudes of the meshes, and the phase may be corrected by the difference between the position of the impulse function and the position of the actual mesh.

An autonomous vehicle radar simulation apparatus according to another embodiment includes: an electromagnetic scattering calculator configured to calculate electromagnetic scattering from vehicle model data; a time-domain response signal calculator for calculating a time-domain response signal based on the calculated electromagnetic scattering; and an RD map generator that generates a Range-Doppler (RD) map of the vehicle in a scenario in which the radar and the vehicle move based on the calculated time domain response signal.

According to embodiments, it is possible to provide an autonomous vehicle radar simulation method and apparatus for calculating electromagnetic scattering with actual vehicle model data and calculating a time-domain response based thereon.

According to the embodiments, every time the vehicle moves, the angle of incidence is calculated in the local coordinate system of the target vehicle in consideration of the angle at which the radar faces the target vehicle, and then the numerical analysis of the electromagnetic field of the target using physical optics is performed at the corresponding angle. , it is possible to provide an autonomous vehicle radar simulation method and apparatus for calculating the magnitude of a radar reception signal based on a radar equation.

1 is a flowchart illustrating a method for simulating an autonomous vehicle radar according to an exemplary embodiment.
2 is a block diagram illustrating an autonomous driving vehicle radar simulation apparatus according to an exemplary embodiment.
3 is a diagram for explaining a simulation method of a radar simulation method for an autonomous driving vehicle according to an exemplary embodiment.
4 is a diagram for explaining a method of calculating a radar reception signal according to an embodiment.
5 is a diagram illustrating an example of a 2D RD map according to an embodiment.
6 is a diagram for explaining a principle of calculating a radar reception signal according to an embodiment.
7A and 7B are diagrams for explaining a radar reception signal calculation algorithm according to an embodiment.
8 is a diagram for explaining generation of a 2D signal according to an embodiment.
9 is a diagram illustrating an example of a scenario map according to an embodiment.
10 is a diagram illustrating an RCS distribution of a first vehicle according to an exemplary embodiment.
11 is a diagram illustrating a 1D signal corresponding to a scenario according to an embodiment.
12 shows a validation of a method according to an embodiment.
13 shows an example of a generated RD map according to an embodiment.

Hereinafter, embodiments will be described with reference to the accompanying drawings. However, the described embodiments may be modified in various other forms, and the scope of the present invention is not limited by the embodiments described below. In addition, various embodiments are provided in order to more completely explain the present invention to those of ordinary skill in the art. The shapes and sizes of elements in the drawings may be exaggerated for clearer description.

The following embodiments relate to an algorithm for generating a time domain signal of a moving object such as a vehicle, which calculates electromagnetic scattering with actual vehicle model data, and calculates a time domain response based on this. We propose an efficient method and numerically verify the proposed method. Based on the time-domain signal calculated in this way, the RD (Range-Doppler) map of the vehicle is calculated in the operation scenario where the vehicle and the radar are moving, and the feasibility is proposed.

1 is a flowchart illustrating a method for simulating an autonomous vehicle radar according to an exemplary embodiment.

Referring to FIG. 1 , the autonomous driving vehicle radar simulation method according to an embodiment includes calculating electromagnetic scattering with vehicle model data ( S110 ), and calculating a time domain response signal based on the calculated electromagnetic scattering. and generating a Range-Doppler (RD) map of the vehicle in a scenario in which the radar and the vehicle move based on the calculated time domain response signal ( S130 ).

Here, the step of calculating the electromagnetic scattering with the vehicle model data ( S110 ) includes calculating the incident angle based on the local coordinate system of the target vehicle in consideration of the angle at which the radar faces the target vehicle whenever the vehicle moves, and then physically using the incident angle. The method may include calculating a scattering matrix by performing numerical analysis of the electromagnetic field of the target vehicle using physical optics. The method may further include calculating a magnitude of a time-domain radar reception signal in which polarization is considered based on a radar equation using a scattering matrix.

Hereinafter, a method for simulating an autonomous vehicle radar according to an embodiment will be described in more detail.

The autonomous driving vehicle radar simulation method according to an embodiment may be described using the autonomous driving vehicle radar simulation apparatus according to the embodiment.

2 is a block diagram illustrating an autonomous driving vehicle radar simulation apparatus according to an exemplary embodiment.

Referring to FIG. 2 , the autonomous driving vehicle radar simulation apparatus 200 according to an embodiment includes an electromagnetic scattering calculator 210 , a time domain response signal calculator 220 , and an RD map generator 230 . can be done An autonomous vehicle radar simulation method/apparatus according to an embodiment is an efficient algorithm for generating a time-domain signal of a moving object such as a vehicle. For the autonomous driving vehicle radar simulation, vehicle mesh, radar position velocity, vehicle position velocity, RF specification, polarization, etc. can be used as input values.

In step S110 , the electromagnetic scattering calculator 210 may calculate electromagnetic scattering from vehicle model data.

The electromagnetic scattering calculator 210 calculates the incident angle based on the local coordinate system of the target vehicle in consideration of the angle at which the radar faces the target vehicle whenever the vehicle moves, and then uses physical optics as the incident angle for the target vehicle. The scattering matrix can be calculated by performing the electromagnetic field numerical analysis of

Also, the electromagnetic scattering calculator 210 may calculate the magnitude of the time-domain radar reception signal in which the polarization is considered based on a radar equation by using the scattering matrix. In other words, the electromagnetic scattering calculator 210 may calculate the signal size based on the radar equation after analyzing the physical and optical vehicle, and in this case, RCS, distance attenuation, and radar beam pattern may be considered.

In operation S120 , the time-domain response signal calculator 220 may calculate a time-domain response signal based on the calculated electromagnetic scattering.

Since the distances between the vehicle meshes and the radar are different, the time domain response signal calculating unit 220 forms a range bin with a predetermined size, and then, after forming a range bin with a predetermined size, a mesh entering the bin ) and adding the signal magnitudes of each mesh to create one impulse function, and to correct the phase by the difference between the position of the impulse function and the position of the actual mesh.

In step S130 , the RD map generator 230 may generate a Range-Doppler (RD) map of the vehicle in a scenario in which the radar and the vehicle move based on the calculated time domain response signal. The RD map generator 230 includes an algorithm for estimating the distance between the target and the radar through distance estimation and Doppler processing through matched filtering, and through this, a Range-Doppler (RD) map is generated. can create

3 is a diagram for explaining a method of simulating a radar simulation method of an autonomous driving vehicle according to an exemplary embodiment.

Referring to FIG. 3 , the autonomous driving vehicle radar simulation method according to an embodiment may provide an algorithm for calculating a time domain signal approximation signal by a large amount of mesh constituting a large-scale object. . It is possible to calculate the time domain response by a dynamic vehicle model placed on any other arbitrary scenario and there is no limit to the geometry of the vehicle model under consideration. The proposed simulation method can be extended without restrictions on RF specifications such as the frequency, bandwidth, polarization, antenna gain, and antenna pattern of the vehicle radar considered in the scenario.

The radar visual domain response calculation algorithm proposed in this embodiment calculates the incident angle in the local coordinate system of the target vehicle in consideration of the angle at which the radar faces the target vehicle whenever the vehicle moves. It performs numerical analysis of the target's electromagnetic field and can calculate the magnitude of the radar reception signal based on the radar equation.

First, it is possible to set the parameters of the radar and target required for the simulation. Radar-related parameters may include frequency, bandwidth, transmit power, antenna gain, polarization, position, speed, and the like. In addition, the target-related parameters may include position, speed, direction, and the like.

After taking the radar and vehicle positions into account in the scenario and calculating the incident angle based on the vehicle's local coordinate system, a monostatic scattering matrix can be calculated. Here, for example, the geometry of a Tesla vehicle model is considered, and a triangular mesh discretized by 770,000 is considered.

The vehicle scattering matrix can be calculated using a physical optics (PO) method (Non-Patent Document 1) that is a high-frequency approximation method. Based on the radar equation, the time domain radar reception signal in which the polarization is considered can be calculated as follows.

[Equation 1]

이며, G는 안테나 이득, R은 표적과 레이더 사이의 거리, P_t는 송신전력,

는 파장, f_c는 반송 주파수를 나타낸다. 안테나 패턴은 Gaussian 빔을 가정한다.

는 레이더의 송신파의 편파를 나타낸다. S_hh와 S_vv는 각각 수평/수직 편파에 대한 동일 편파 산란 행렬을 나타낸다.

및

는 각 입사파에 대한 수평/수직 편파를 나타낸다. 또한

및

는 반사파에 대한 수평/수직 편파를 나타낸다. [수학식 1]에서 교차 편파 산란 행렬 S_hv와 S_vh는 S_hh와 S_vv에 비해 크기가 매우 작으므로 무시되었다. 수신 신호는 IF(Intermediate Frequency) 대역에서 기준 신호와 정합 필터를 통과한 후 펄스 압축을 통해 표적의 거리를 추정할 수 있다. 차량의 모든 메시(mesh)에 의한 시간 응답의 펄스 압축 신호는 해석적으로 다음 식과 같이 표현된다.here,

where G is the antenna gain, R is the distance between the target and the radar, P _t is the transmit power,

is the wavelength and f _c is the carrier frequency. The antenna pattern assumes a Gaussian beam.

represents the polarization of the radar's transmit wave. S _hh and S _vv represent co-polarization scattering matrices for horizontal/vertical polarization, respectively.

and

denotes the horizontal/vertical polarization for each incident wave. also

and

represents the horizontal/vertical polarization for the reflected wave. In [Equation 1], the cross-polarization scattering matrices S _hv and S _vh were ignored because they were very small compared to S _hh and S _vv . After the received signal passes through a reference signal and a matched filter in an intermediate frequency (IF) band, the distance to the target may be estimated through pulse compression. The pulse compression signal of the time response by all meshes of the vehicle is analytically expressed as the following equation.

[Equation 2]

이며, f_IF는 IF 주파수이다. N은 차량 메시(mesh) 개수이며,

는 i 번째 차량 메시(mesh)의 시간지연으로

이며, 여기서 R_i와 c는 거리 및 빛의 속도이다. BW는 대역폭이며, T는 chirp 신호의 주기이다.

는

를 입력으로 갖는 삼각 함수이며, [수학식 2]의 계산 복잡도는

이다. 여기서 N과 M은 차량의 메시(mesh) 개수와 거리 샘플링 개수이다. IF 대역에서 나이퀴스트 샘플링 조건을 만족하기 위해 요구되는 거리 샘플링 수는 매우 많으며, 차량의 메시(mesh) 개수 또한 매우 많기 때문에 [수학식 2]를 기반으로 레이더 수신 신호를 계산하는 것은 비효율적이다. 따라서 본 실시예에서 제안하는 방법은 다음과 같다.here,

and f _IF is the IF frequency. N is the number of vehicle meshes,

is the time delay of the i-th vehicle mesh.

where R _i and c are distance and speed of light. BW is the bandwidth and T is the period of the chirp signal.

Is

is a trigonometric function having as an input, and the computational complexity of [Equation 2] is

am. Here, N and M are the number of meshes of the vehicle and the number of distance sampling. Since the number of distance sampling required to satisfy the Nyquist sampling condition in the IF band is very large, and the number of meshes of the vehicle is also very large, it is inefficient to calculate the radar reception signal based on [Equation 2]. Therefore, the method proposed in this embodiment is as follows.

4 is a diagram for explaining a method of calculating a radar reception signal according to an embodiment.

라고 할 때

만큼 위상을 곱하여 보정한다. 위상 보정을 통해 거리 빈 안에서 통합된 신호를

라 하고 펄스 압축된 기준 신호를

라 할 때 차량에 의해 반사된 레이더 수신 신호는 다음과 같이 표현된다.As shown in FIG. 4 , impulse responses calculated based on Equation 1 are sequentially arranged according to a time delay, and impulses corresponding to a range bin are coherently integrated. sampling time

when you say

It is corrected by multiplying the phase by The integrated signal within the distance bin with phase correction

and the pulse-compressed reference signal

The radar received signal reflected by the vehicle is expressed as follows.

[Equation 3]

Here, * denotes convolution.

Table 1 shows the parameters to be considered for the simulation.

[Table 1]

In all distance domains, the analytical results calculated through [Equation 2] and the approximation method proposed in this embodiment agree very well for all distance bins.

Table 2 compares the simulation times of the proposed approximation method and the analytical method.

Comparing the simulation time of the proposed approximation method and the analytical method, the proposed method is 1,260 times more efficient.

5 is a diagram illustrating an example of a 2D RD map according to an embodiment.

Referring to FIG. 5 , a 2D RD map may be generated in consideration of the Doppler effect due to the movement of the radar and the vehicle when transmitting and receiving signals of multiple periods. For example, it can be seen that the target signal is generated at a distance of 30 m and a gaze velocity of 3 m/s. When considering the relative speed of the radar and the target, it can be seen that the estimated speed agrees well with the relative speed difference.

As such, the embodiments propose a numerical method for analyzing the scattering of a vehicle using a physical-optical method and calculating a time-domain received signal of the radar based thereon. The proposed method was numerically verified through comparison with the results of the analytical technique, and the time efficiency of the proposed method was quantitatively presented. In addition, an RD map considering the Doppler effect was derived in the autonomous driving scenario, and the numerical validity of the results was presented.

6 is a diagram for explaining a principle of calculating a radar reception signal according to an embodiment.

Referring to FIG. 6 , the vehicle radar uses a frequency modulated continuous wave (FMCW), and the original exact signal calculation is performed by multiplying the signal size in consideration of the vehicle RCS and the radar beam pattern by a chirp signal whose frequency changes with time. signal can be generated. In this case, chirp uses one cycle.

Then, the number of signals equal to the number of meshes is generated with one signal per mesh, and when these signals are combined and bag-matched filtered, a pulse-compressed 1D signal is generated to generate a target vehicle and radar. It may include distance information between them. At this time, the number of meshes in the simulation is very large with 770,000, so it takes a long time to process the signal in this way.

7A and 7B are diagrams for explaining a radar reception signal calculation algorithm according to an embodiment.

7A , according to an embodiment, a new algorithm using an impulse function may be used to reduce the calculation time. Although the distances between the meshes of the vehicle 710 and the radar 720 are different, the distance bin 730 is made by determining the size, and the meshes entering the bin are found and the signal size of each mesh. can be added to make one impulse function. At this time, the position of the generated impulse function is different from the position of the actual mesh, and this difference can be compensated for in phase.

As shown in FIG. 7B , after generating one impulse for each bin, it can pass through a matched filter in exact and then convolve the equation.

[Equation 4]

Convolution can be expressed as

[Equation 5]

8 is a diagram for explaining generation of a 2D signal according to an embodiment.

As shown in FIG. 8 , thereafter, a 2D signal is created using a Doppler frequency and several cycles of chirp signals, and the number of cycles is stacked as shown in the figure below. Here, by performing Doppler processing using a Fast Fourier Transform (FFT), speed information is obtained and an RD map can be generated.

9 is a diagram illustrating an example of a scenario map according to an embodiment.

Referring to FIG. 9 , a scenario map in which a radar and a first vehicle 910 , a second vehicle 920 , and a third vehicle 930 exist is assumed. Here, the second vehicle 920 and the third vehicle 930 are stopped, and the first vehicle 910 moves at 15 m/s. Move.

10 is a diagram illustrating an RCS distribution of a first vehicle according to an exemplary embodiment.

As shown in FIG. 10 , since the radar vehicle is located at the rear of the first vehicle, the scattered wave and RCS using electromagnetic waves incident from the rear are calculated, and the RCS distribution can be expressed by applying the calculated RCS to the vehicle mesh. there is.

11 is a diagram illustrating a 1D signal corresponding to a scenario according to an embodiment.

11 , a 1D signal is displayed after passing through a matched filter corresponding to a scenario, and a case in which the second vehicle is obscured by the first vehicle is illustrated by the RD map at the beginning of the scenario.

12 shows a validation of a method according to an embodiment.

As shown in FIG. 12 , it can be verified by comparing a signal that has been pulse compressed by the original signal processing method and a signal convolved by generating an impulse function.

The processing time of the pulse-compressed signal, which is the original signal processing method, is 2513 sec, and the processing time of the signal convolved by the impulse function generation is 2 sec. Thus, it is shown that the method according to the embodiments is approximately 1260 times faster and efficient.

13 shows an example of a generated RD map according to an embodiment.

As shown in FIG. 13 , it is possible to improve readability in reading the position and speed information of the target vehicle. This indicates a case in which the second vehicle is obscured by the first vehicle by the RD map at the beginning of the scenario.

According to the embodiments, every time the vehicle moves, the angle of incidence is calculated in the local coordinate system of the target vehicle in consideration of the angle at which the radar faces the target vehicle, and then numerical analysis of the electromagnetic field of the target using physical optics is performed at the angle. In addition, it is possible to provide a method and apparatus for simulating the radar of an autonomous vehicle by calculating the magnitude of the radar reception signal based on the radar equation.

The device described above may be implemented as a hardware component, a software component, and/or a combination of the hardware component and the software component. For example, devices and components described in the embodiments may include, for example, a processor, a controller, an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, a field programmable array (FPA), It may be implemented using one or more general purpose or special purpose computers, such as a programmable logic unit (PLU), microprocessor, or any other device capable of executing and responding to instructions. The processing device may execute an operating system (OS) and one or more software applications running on the operating system. A processing device may also access, store, manipulate, process, and generate data in response to execution of the software. For convenience of understanding, although one processing device is sometimes described as being used, one of ordinary skill in the art will recognize that the processing device includes a plurality of processing elements and/or a plurality of types of processing elements. It can be seen that can include For example, the processing device may include a plurality of processors or one processor and one controller. Other processing configurations are also possible, such as parallel processors.

Software may comprise a computer program, code, instructions, or a combination of one or more thereof, which configures a processing device to operate as desired or is independently or collectively processed You can command the device. The software and/or data may be any kind of machine, component, physical device, virtual equipment, computer storage medium or apparatus, to be interpreted by or to provide instructions or data to the processing device. may be embodied in The software may be distributed over networked computer systems and stored or executed in a distributed manner. Software and data may be stored in one or more computer-readable recording media.

The method according to the embodiment may be implemented in the form of program instructions that can be executed through various computer means and recorded in a computer-readable medium. The computer-readable medium may include program instructions, data files, data structures, etc. alone or in combination. The program instructions recorded on the medium may be specially designed and configured for the embodiment, or may be known and available to those skilled in the art of computer software. Examples of the computer-readable recording medium include magnetic media such as hard disks, floppy disks and magnetic tapes, optical media such as CD-ROMs and DVDs, and magnetic such as floppy disks. - includes magneto-optical media, and hardware devices specially configured to store and execute program instructions, such as ROM, RAM, flash memory, and the like. Examples of program instructions 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 or the like.

As described above, although the embodiments have been described with reference to the limited embodiments and drawings, various modifications and variations are possible from the above description by those skilled in the art. For example, the described techniques are performed in an order different from the described method, and/or the described components of the system, structure, apparatus, circuit, etc. are combined or combined in a different form than the described method, or other components Or substituted or substituted by equivalents may achieve an appropriate result.

Therefore, other implementations, other embodiments, and equivalents to the claims are also within the scope of the following claims.

Claims (5)

calculating electromagnetic scattering with vehicle model data;
calculating a time-domain response signal based on the calculated electromagnetic scattering; and
Generating a Range-Doppler (RD) map of the vehicle in a scenario in which the radar and the vehicle move based on the calculated time-domain response signal
Including, autonomous vehicle radar simulation method. According to claim 1,
Calculating electromagnetic scattering with the vehicle model data comprises:
Every time the vehicle moves, the angle of incidence is calculated based on the local coordinate system of the target vehicle in consideration of the angle at which the radar faces the target vehicle, and then numerical analysis of the electromagnetic field of the target vehicle is performed using physical optics as the angle of incidence. to calculate the scattering matrix
Including, autonomous vehicle radar simulation method. 3. The method of claim 2,
Calculating electromagnetic scattering with the vehicle model data comprises:
Calculating the magnitude of a time-domain radar reception signal in which polarization is considered based on a radar equation using the scattering matrix
Further comprising a, autonomous vehicle radar simulation method. According to claim 1,
Calculating the time domain response signal comprises:
Since the distances between the meshes of the vehicle and the radar are different, a range bin is formed to a predetermined size, and then meshes entering the bin are searched for each mesh. To create one impulse function by adding the signal magnitudes of the signals, and to correct the phase by the difference between the position of the impulse function and the position of the actual mesh
A method for simulating an autonomous vehicle radar, characterized in that. an electromagnetic scattering calculator that calculates electromagnetic scattering from vehicle model data;
a time-domain response signal calculator for calculating a time-domain response signal based on the calculated electromagnetic scattering; and
An RD map generator that generates a Range-Doppler (RD) map of a vehicle in a scenario in which the radar and the vehicle move based on the calculated time domain response signal
Including, autonomous vehicle radar simulator.

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