Understanding Multiple Ray Tracing for Automotive Radar Simulations

Jin Zhang, R&D Engineer

Oct.13th 2020

Radar and Antenna

Design Engineering Software (EESof) R&D Team

Keysight Technologies, Inc.

Jin Zhang (Ph.D)Shenghui Zhou (Ph.D)

Agenda

➢ What is MRT (Multiple Ray Tracing)

➢ The Basic Theories of MRT

➢ Conducting MRT Analysis on Real-life Scenarios

➢ Advanced Scenario/Radar Architecture Designs

➢ Summary

WHAT IS MRT (MULTIPLE RAY TRACING)

Understanding Multiple Ray Tracing for Automotive Radar Simulations

Challenges in Automotive Radar Designs• Multiple System-Level Design Requirements

• PCS (Near-Range), ACC (Long Range), AEBS (for heavy trucks), Autonomous Driving, etc. • System-Level Radar Evaluations: High-Res (Wide BW), Beamforming, etc.• Multi-Domain Signal Analysis: Baseband, RF, Digitizing, DSP, etc.

• Complex Architectural & Algorithm Design for ARo FMCW, MFSK, Frequency Hopping, Pulse, etc.o MIMO Radar Architecture (TDM-MIMO, CDM-MIMO, etc.)o Multi-Target Range & Velocity Detection Performanceso Advanced DSP Algorithm Designs (Compressed Sensing, etc.)

• AR working under Complex Environment Scenarioso Radar Platforms in motiono Guard-rails, Road Clutters, Interferenceso Pedestrians, Bicycles o Atmospheric & Propagation Loss (Harsh Weather Conditions)

Multiple Ray Tracing (MRT) is the Solution!

A First Impression on MRT

Why Using MRT?MRT is an established large-scale EM simulation technology to

characterize EM wave travelling behaviors in Complex Scenarios.

MRT is a New Library in Keysight SystemVue that can do:

Complex Scenario Modeling with built-in/imported 3D CAD Targets

Authentic Radar Echo from Real-world Sensing Scenarios

Integrated System-Level Radar Design with SV Dataflow

Cross-check with Target Detections from other Sensors

Quick Demo of MRT CapabilitiesWhat unexpected targets can be seen

in a small-scale Complex Scenario? What can be seen in

a large-scale Complex Scenario ?

THE BASIC THEORIES OF MRT

Understanding Multiple Ray Tracing for Automotive Radar Simulations

Core Enabling Technologies in MRT

SBR + MRTSolution

3D CAD basedTarget Modeling

Facet Searching Algorithm

Scattered Field EM Calculation

Algorithms

Abundant built-in Target Models

Optimized KD-tree based Fast

Facet Searching

Facet ScatteringEdge Scattering

Surface Roughness

Externally Imported

Target Models

3D-CAD based built-in Target Models

Facet No.: ~0.3k Facet No.: ~1.5k Facet No.: ~460k

Cars -> 3 levels of fidelities

Facet No.: ~1k Facet No.: ~130k

Trucks/Buses -> 2 levels of fidelities

3D-CAD based built-in Target Models

Guardrails -> 4 Models Available

Lampposts -> 2 levels of fidelity

Manhole Cover

Road Signs

Optimized KDTree-based Facet Searching Algorithm

Left Fig: from https://en.wikipedia.org/wiki/K-d_tree

How to determine which facet is hit in a ray’s bouncing paths?

The algorithm narrows down the region of possible ray hitting:

White -> Red -> Green -> Blue

Scattered EM Calculation Algorithms

Scattered FieldElectromagnetic

Algorithms

Facet Scattering

Edge Scattering

Surface Roughness

Basic Ray Tracing Capability Enabled (GO + PO + Ray Tubes)

Target sharp edges caused additional scattered rays

Rough surfaces caused EM field fluctuations

Ray Tubes illuminating Trucks

CONDUCTING MRT ANALYSIS ON REAL-LIFE SCENARIOS

Understanding Multiple Ray Tracing for Automotive Radar Simulations

OfflineCalculatedRay Files

SystemVue MRT Channel Model

SystemVue MRT Channel Model

SystemVue MRT Simulation Methodology

Starting from one Simple Scenario

Target Characterization with MRT:

✓ True mmW Radar echoes obtained;✓ Not point-scatter NOW!

✓ Range/Doppler Multi-Scatter Spreading clearly observed

✓ Range/Velocity hot-spot positions consistent with input values

Far Range Car Complex Echo

Near Range Car Complex Echo

Target Profiling (WB mmW Radar Imaging Demo)

✓ Detected Point-Cloud positions

consistent with input values

✓ Both vehicles have been characterized

by multi-scatter profiles

❖ (Wideband mmW radar ranging capability demonstrated)

CFAR: SOCA

Target Grouping: Plots-Of-Centroid

Range/Angle: MathLang ScriptTarget Profiling for the

Far Range Car

Target Profiling for the Near Range Car

Complex Scenario Modeling and RayTracing Snapshot

Multiple Ray Bouncing Clearly Visible

Range Doppler Mapping for Complex Scenario

Two Sedans

Bus Side

Effects

Nearby Ground

Reflections

Far Bus +

Truck

Multiple

Reflections

Two Sedans

Bus

Side

Effects

Far Bus

+ Truck

Multiple

Reflections

Nearby Reflections

Significantly Attenuated

Pedestrian Pedestrian

Raw MRT RD Mapping Tx+Rx Antenna Patterns Imported

Point Scatter Plot for Complex Scenario Modeling

Target Scatter Distribution Results

ScenarioMRT Simulation

ADVANCED SCENARIO/RADAR ARCHITECTURE DESIGNS

Understanding Multiple Ray Tracing for Automotive Radar Simulations

Using External Scenario Model File – Tunnel MRT Demo

Integrating Advanced Radar Architectures

Integrating Advanced Radar ArchitecturesTDM-MIMO Src & Tx Modeling

Tx _TDM -M IM OSigGen

Subnetwork5 {Tx_TDM-MIMOSigGen}

AutomotiveRadar

CW

f r eq_out _

wavef or m _out _

SampleRate=400e+6 Hz [SampleRate]DeltaFreq=200e+6 Hz [BW]

LowerFreq=0 HzOffTime=5.4e-6 s [Toff_A]

FreqFixTime=0 sFreqDownTime=0 s

FreqUpTime=40e-6 s [Tup_A]Amplitude=1 V

Waveform_type=UserDefinedA19 {AutomotiveRadar_CW@Automotive Radar Models}

Tx _UpConv _RefTx

Subnetwork6 {Tx_UpConv_RefTx}

Tx_UpConv

Subnetwork7 {Tx_UpConv}

123

StartStopOption=TimeS1 {Sink@Data Flow Models}

MixerAe Term

LNA

Noise

Density

NDensit y=- 190. 021 dBm [ - 174- 10* log10( Sam pleRat e/ ADCFs) ]

NDensit yType=Const ant noise densit yA19 { AddNDensit y@Dat a Flow M odels}

Am pl i fier

G CType=none

NoiseFigur e=0

G ainEr r or =NoneQ uant izat ion=NO

G ain=70 [ Rx_G ain_CplxSce]

G ainUnit =dBA20 { Am plif ier @Dat a Flow M odels}

Noise

Density

NDensit y=- 190. 021 dBm [ - 174- 10* log10( Sam pleRat e/ ADCFs) ]NDensit yType=Const ant noise densit y

A21 { AddNDensit y@Dat a Flow M odels}

123

St ar t St opO pt ion=Sam plesDisabled: O PEN

S2 { Sink@Dat a Flow M odels}

Am pl i fier

G CType=none

NoiseFigur e=0G ainEr r or =None

Q uant izat ion=NO

G ain=70 [ Rx_G ain_CplxSce]G ainUnit =dB

A22 { Am plif ier @Dat a Flow M odels}

Rx_Doppler FFT

DopplerFFT_Out

DopplerFFT_BeforeCFAR

MIMOReshape_DataIn

Subnet wor k10 { Rx_Doppler FFT}

Rx _RangeFFT

FFTSize=512 [ RangeFFTSize]

ADCFs=10e+6 [ ADCFs]PW=40e- 6 [ Fr eqUpTim e1]

PRI =45. 4e- 6 [ PRI 1]Subnet wor k12 { Rx_RangeFFT}

Rx_TDM-MIMOSigReshape

TxNum =2 [ TxNum ]Doppler FFTSize=128 [ Doppler FFTSize]

RangeFFTSize=512 [ RangeFFTSize]Subnet wor k13 { Rx_TDM - M I M O SigReshape}

Rx _RangeFFT

FFTSize=512 [ RangeFFTSize]ADCFs=10e+6 [ ADCFs]

PW=40e- 6 [ Fr eqUpTim e1]PRI =45. 4e- 6 [ PRI 1]

Subnet wor k2 { Rx_RangeFFT}

Rx_TDM-MIMOSigReshape

TxNum =2 [ TxNum ]

Doppler FFTSize=128 [ Doppler FFTSize]RangeFFTSize=512 [ RangeFFTSize]

Subnet wor k3 { Rx_TDM - M I M O SigReshape}

Rx_Doppler FFT

DopplerFFT_Out

DopplerFFT_BeforeCFAR

MIMOReshape_DataIn

Subnet wor k17 { Rx_Doppler FFT}

Rx _RangeFFT

FFTSize=512 [ RangeFFTSize]

ADCFs=10e+6 [ ADCFs]PW=40e- 6 [ Fr eqUpTim e1]

PRI =45. 4e- 6 [ PRI 1]

Subnet wor k19 { Rx_RangeFFT}

G ain=1. 099e- 3 [ sqr t ( Ae_Ter m ) ]

G 1 { G ain@Dat a Flow M odels}

Rx_TDM-MIMOSigReshape

TxNum =2 [ TxNum ]Doppler FFTSize=128 [ Doppler FFTSize]

RangeFFTSize=512 [ RangeFFTSize]

Subnet wor k20 { Rx_TDM - M I M O SigReshape}

G ain=1. 099e- 3 [ sqr t ( Ae_Ter m ) ]

G 2 { G ain@Dat a Flow M odels}

Rx_Doppler FFT

DopplerFFT_Out

DopplerFFT_BeforeCFAR

MIMOReshape_DataIn

Subnet wor k22 { Rx_Doppler FFT}

Rx_Doppler FFT

DopplerFFT_Out

DopplerFFT_BeforeCFAR

MIMOReshape_DataIn

Subnet wor k6 { Rx_Doppler FFT}

Rx _RangeFFT

FFTSize=512 [ RangeFFTSize]

ADCFs=10e+6 [ ADCFs]

PW=40e- 6 [ Fr eqUpTim e1]PRI =45. 4e- 6 [ PRI 1]

Subnet wor k8 { Rx_RangeFFT}

Rx_TDM-MIMOSigReshape

TxNum =2 [ TxNum ]

Doppler FFTSize=128 [ Doppler FFTSize]RangeFFTSize=512 [ RangeFFTSize]

Subnet wor k9 { Rx_TDM - M I M O SigReshape}

G ain=1. 099e- 3 [ sqr t ( Ae_Ter m ) ]G 3 { G ain@Dat a Flow M odels}

G ain=1. 099e- 3 [ sqr t ( Ae_Ter m ) ]

G 4 { G ain@Dat a Flow M odels}

123

St ar t St opO pt ion=Aut o

Disabled: O PENLO { Sink@Dat a Flow M odels}

123

St ar t St opO pt ion=Aut oDisabled: O PEN

RF { Sink@Dat a Flow M odels}

BeatFreqBeatSignal

LoSignal

Recieve

DownSam pleFact or =40 [ f loor ( Sam pleRat e/ ADCFs) ]

Subnet wor k1 { Beat Fr eq}

BeatFreqBeatSignal

LoSignal

Recieve

DownSam pleFact or =40 [ f loor ( Sam pleRat e/ ADCFs) ]

Subnet wor k4 { Beat Fr eq}

BeatFreqBeatSignal

LoSignal

Recieve

DownSam pleFact or =40 [ f loor ( Sam pleRat e/ ADCFs) ]Subnet wor k5 { Beat Fr eq}

BeatFreqBeatSignal

LoSignal

Recieve

DownSam pleFact or =40 [ f loor ( Sam pleRat e/ ADCFs) ]

Subnet wor k21 { Beat Fr eq}

123St ar t St opO pt ion=Sam ples

S1 { Sink@Dat a Flow M odels}

Noise

Density

NDensit y=- 190. 021 dBm [ - 174- 10* log10( Sam pleRat e/ ADCFs) ]NDensit yType=Const ant noise densit y

A10 { AddNDensit y@Dat a Flow M odels}

Noise

Density

NDensit y=- 190. 021 dBm [ - 174- 10* log10( Sam pleRat e/ ADCFs) ]

NDensit yType=Const ant noise densit y

A16 { AddNDensit y@Dat a Flow M odels}

Am pl i fier

G CType=none

NoiseFigur e=0G ainEr r or =None

Q uant izat ion=NOG ain=70 [ Rx_G ain_CplxSce]

G ainUnit =dB

A18 { Am plif ier @Dat a Flow M odels}

A23 { Add@Dat a Flow M odels}

123St ar t St opO pt ion=Sam ples

Dat aCube { Sink@Dat a Flow M odels}

Am pl i fier

G CType=none

NoiseFigur e=0

G ainEr r or =NoneQ uant izat ion=NO

G ain=70 [ Rx_G ain_CplxSce]G ainUnit =dB

A5 { Am plif ier @Dat a Flow M odels}

AutomotiveRadarRayTracingChannel

RxAnt Slant Angle=0

RxAnt Downt ilt Angle=90

RxAnt Bear ingAngle=0RxPhaseCent er =NO

TxAnt Slant Angle=0

TxAnt Downt ilt Angle=90TxAnt Bear ingAngle=90

TxPhaseCent er =NORayTr acingFileNam e=C: …/ Pr oject Tr acing_Fr am e1_Ray3_256PRI _Syst em VueRays. bin

Num ber of Rays=100000 [ Num O f Rays]

RxElem ent Pat t er nFileScaleFact or =1RxAnt Pat t er nFileNam e=C: / User s/ shengzho…/ Rx_Ant enna_2line. csv

RxAnt Pat t er nFileType=HFSS

RxCom m onAnt Pat t er nFile=YESRxAnt Pat t er nType=Fr om File

RxAxisType=Y- Hor izont alRxElem ent Spacing=0. 513 [ Dr x]

Num RxAnt s1D=4 [ RxEleNum ]

RxAr r ayConf ig=Unif or m Linear Ar r ayTxElem ent Pat t er nFileScaleFact or =1

TxAnt Pat t er nFileNam e=C: / User s/ shengzho…/ Tx_Ant enna_8line. csv

TxAnt Pat t er nFileType=HFSSTxCom m onAnt Pat t er nFile=YES

TxAnt Pat t er nType=Fr om FileTxAxisType=Y- Hor izont al

TxElem ent Spacing=2. 053 [ Dt x]

Num TxAnt s1D=2 [ TxEleNum ]TxAr r ayConf ig=Unif or m Linear Ar r ay

AnglePair Pr ecision=0. 017 [ Angle_Res_Rad_CplxSce]DelayPair Pr ecision=2. 5e- 9 [ Tim eDelay_Res_sec]

A4

123

St ar t St opO pt ion=Aut oDisabled: O PEN

RayTr acingCH { Sink@Dat a Flow M odels}

Aut om ot iveRadar

CW

freq_out_

waveform_out_

Sam pleRat e=400e+6 Hz [ Sam pleRat e]Delt aFr eq=200e+6 Hz [ BandWidt h]

Lower Fr eq=0 Hz

O f f Tim e=5. 4e- 6 s [ O f f Tim e1]Fr eqFixTim e=0 s

Fr eqDownTim e=0 s

Fr eqUpTim e=40e- 6 s [ Fr eqUpTim e1]Am plit ude=1 V

Wavef or m _t ype=User Def inedA17 { Aut om ot iveRadar _CW@Aut om ot ive Radar M odels}

123

St ar t St opO pt ion=Aut oDisabled: O PEN

RayTr acingCHI nput { Sink@Dat a Flow M odels}

123

St ar t St opO pt ion=Sam ples

Disabled: O PENS4 { Sink@Dat a Flow M odels}

Tx_TDM - M I M O SigGen

Sam pleNum O f PRI =18160 [ Sam pleNum O f PRI 1]

TxNum =2 [ TxEleNum ]

Subnet wor k14 { Tx_TDM - M I M O SigG en}

Tx_UpConv_Ref Tx

Subnet wor k15 { Tx_UpConv_Ref Tx}

Tx _UpConv

Sam pleRat e=400e+6 [ Sam pleRat e]TxNum =2 [ TxNum ]

f c=77e+9 [ Fcar r ier ]

Subnet wor k16 { Tx_UpConv}

Tx Modeling

MRT Channel Modeling

2D FFTRx RF Modeling

2T4R TDM MIMO Radar Architecture

Integrating Advanced Radar Architectures

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