MATLAB을 활용한 자동차 레이더 개발
서기환
© 2015 The MathWorks,1 Inc. Agenda
▪ Different Usage for Radar Modeling
▪ Radar Design Workflow – DSP Design and Simulation – RF/Antenna Modeling
▪ High Level Simulation with Probabilistic Model
2 Agenda
▪ Radar Modeling with Fidelity Control
▪ Radar Design Workflow – DSP Design and Simulation – RF/Antenna Modeling
▪ High Level Simulation with Probabilistic Model
3 Two Personas using Automotive Radar Sensor Models
Radar Designers Radar Users
Detections Detections Radar Sensor Radar Sensor World / Tracks World / Tracks Truth Truth IQ Data …
Lidar Sensor RF Signal Antenna Front-End Processing Camera Sensor
Ultrasound Sensor
4 Radar Modeling and Simulation
RF/Antenna Modeling DSPDSP AlgorithmAlgorithm ModelingModeling
Target Classification
Radar Signal Antenna/Array/RF Processing
Tracking and Sensor Fusion Scheduling and Radar Control
5 Radar Modeling and Simulation
Probabilistic Model
Target Radar Sensor Classification
Tracking and Sensor Fusion
6 Automotive Radar Sensor Models
DSP algorithm Model RF/Antenna Model Probabilistic Model Engineers Radar Designers Radar Designers Radar Users
Analog-mixed Signal Sensor Fusion, Model usage Radar Algorithms Simulation Controller
Outputs IQ Data or Detections IQ Data Detections or Tracks
Benefit - Highest Fidelity Simulation Speed
Disadvantage - Simulation speed Can’t Access IQ Data
7 Agenda
▪ Radar Modeling with Fidelity Control
▪ Radar Design Workflow – DSP Design and Simulation – RF/Antenna Modeling
▪ High Level Simulation with Probabilistic Model
8 Radar Simulation and Modeling Architecture
Waveform Transmit Transmitter Generator Array Pt λ Gt
Radar 2 Environment, 푃푡퐺푡퐺푟휆 휎 Scheduler and Targets, & 푃푟 = 2 2 3 Interference (L, 흈, 푹 푹 ) Tracker 4휋 푅푡 푅푟 퐿 풓 , 풕
Receive Signal Receiver Array Processing Gr Gr
▪ Functions for calculations and analysis ▪ Apps for common workflows ▪ Parameterized components for system modeling ▪ Easy path to increased fidelity for antenna and RF design ▪ Code generation for deployment
9 Radar Model to Simulate High Fidelity Raw Data
Mixed-Signal Algorithms Antenna, Antenna arrays Continuous & discrete time beamforming, beamsteering, type of element, # elements, configuration MIMO • Simulink • Phased Array System Toolbox • Antenna Toolbox • DSP System Toolbox • Communications System Toolbox • Phased Array System Toolbox • Control System Toolbox • DSP System Toolbox
RX LNA ADC Signal Processing Tracking & • SensorSensor Fusion and Fusion Tracking Toolbox
PA DAC • Communications System Toolbox TX • Phased Array System Toolbox Channel • Phased Array System Toolbox interference, clutter, noise • RF Blockset • Signal Processing Toolbox • RF Toolbox • Wavelet Toolbox
RF Impairments Waveforms & Resource Scheduling frequency dependency, non-linearity, noise, mismatches 10 Path to Higher Fidelity
▪ Extend model fidelity over project evolution ▪ Simple interface to replace off-the-shelf components with custom ones
Antenna element Target model Propagation model RF signal chain
Ideal elements Point target Free space Baseband
EM solver with mutual Synthesized backscatter Line of sight atmospheric RF components coupling (angle & frequency) effects
Measured pattern import Measured return (angle & Multipath, terrain and ducting Analog-mixed simulation frequency) effects
11 DSP Algorithms for Radar Systems
Beamforming Detections Direction of Arrival
12 Antenna Array Design
or
Replicate to build array Design subarray with desired fidelity
Assess resulting pattern
13 Beam Steering
14 Synthesizing an Array from a Specified Pattern
▪ Introduce optimization workflow
https://blog.naver.com/matlablove/221205030640 https://kr.mathworks.com/company/newsletters/articles/synthesizing-an-array-from-a-specified-pattern-an-optimization-workflow.html
15 Hybrid Beamforming
▪ 4 subarrays of 8 patch antennas operating at 66GHz → 4x8 = 32 antennas ▪ Digital beamforming applied to the 4 subarrays (azimuth steering)
▪ RF beamforming (phase shifters) applied to the 8 antennas (elevation steering)
Beamformers (array and subarray)
4 subarrays
Array pattern Subarray weights 16 RF Front End Modelling using Circuit Envelope
▪ Direct conversion to IF (5GHz) and superhet up-conversion to mmWave (79GHz) ▪ Non-linear impairments such as IP2, IP3, P1dB. ▪ Power dividers (e.g. S-parameters) ▪ Variable phase-shifters
17 Radar Waveform Analyzer
18 Sensor Array Analyzer
19 Radar Equation Calculator, RF Budget Analyzer
20 Model FMCW RADARs at mmWave Frequencies
Waveform Transmitter Generator Transmit Array
Signal Processing Receive Receiver Array
21 Model FMCW Radar – RF Front-End
Transmitter
or
22 Model FMCW Radar – Transmit Array Antenna
Transmit Array
23 Model FMCW Radar – Signal Processing
Signal Processing
24 Visualizing Radar and Target Trajectory
25 Automated Driving Simulation with IQ-level Radar Signal
26 Simulating Micro-Doppler Signatures
Micro-Doppler for Pedestrian Bicycle
27 Pedestrian Micro-Doppler with and without Parked Vehicle
Micro-Doppler for pedestrian (only) Micro-Doppler for pedestrian and parked vehicle 28 Pedestrian and Bicyclist Classification Using Deep Learning
29 Pedestrian and Bicyclist Classification Using Deep Learning
30 Increasing Angular Resolution with MIMO Radars (Virtual Array)
2 Tx 4 Rx
Two options: Increase number of receive elements or perform signal processing
31 MIMO non-MIMO Non-MIMO MIMO
Simulation
Scene Live Data
32 Agenda
▪ Radar Modeling with Fidelity Control
▪ Radar Design Workflow – DSP Design and Simulation – RF/Antenna Modeling
▪ High Level Simulation with Probabilistic Model
33 Virtual Driving Scenarios with Radar Sensor
Scenes Cuboid 3D Simulation
Testing Controls, sensor fusion, planning Controls, sensor fusion, planning, perception
Authoring Driving Scenario Designer App Unreal Engine Editor Programmatic API (drivingScenario) Sensing Probabilistic radar (detection list) Probabilistic radar (detection list) Probabilistic vision (detection list) Monocular camera (image, labels, depth) Probabilistic lane (detection list) Fisheye camera (image) Lidar (point cloud)
34 Synthesize Radar Sensor Data
Simulate Radar Sensors in 3D Environment ▪ Extract the center locations ▪ Use center location for road creation using driving scenario ▪ Define multiple moving vehicles ▪ Export trajectories from app ▪ Configure multiple probabilistic radar models ▪ Calculate confirmed track
Automated Driving ToolboxTM
35 Design trackers
Multi-Object Tracker
Association & Tracking Detections Track Tracks Filter Management
From various sensors at various update rates
▪ Multi-object tracker ▪ Linear, extended, and ▪ Global Nearest Neighbor (GNN) tracker unscented Kalman filters ▪ Joint Probabilistic Data Association (JPDA) tracker ▪ Particle, Gaussian-sum, ▪ Track-Oriented Multi-Hypothesis Tracker (TOMHT) and Interacting Multiple ▪ Probability Hypothesis Density (PHD) tracker Model (IMM) filters
Automated Driving ToolboxTM Sensor Fusion and Tracking ToolboxTM
36 Design Multi-Object Trackers
Multi-Object Tracker Extended Object Tracking ▪ Design multi-object tracker ▪ Design extended object trackers ▪ Evaluate tracking metrics ▪ Evaluate error metrics ▪ Evaluate desktop execution time
Sensor Fusion and Tracking ToolboxTM Automated Driving ToolboxTM
37 Design track level fusion systems
Vehicle 1
Multi-Object Track Detections Tracker Fuser Tracks
Vehicle 2
Multi-Object Track Detections Tracker Fuser Tracks
38 Track-Level Fusion
Track-to-Track Fusion for Automotive Safety Applications
Occluded Parked vehicles vehicle fused observed by from vehicle 1 vehicle 1 Occluded Pedestrian pedestrian observed by fused from vehicle 1 Pedestrianvehicle 1 observed by vehicle 2
Sensor Fusion and Rumor control: the fused TM Tracking Toolbox track is dropped by vehicle 1 TM Automated Driving Toolbox because vehicle 2 is coasting and there is no update by vehicle 1 sensors 39 Radar System Modeling for Perception
Antenna/RF Waveforms Tracking/ Sensor Fusion Scenario Generation
Spatial Signal Processing Environment Deep Learning Code generation
HDL
40 Key Takeaways
▪ Choose the Right Modeling Method – You can control the fidelity
▪ Start Your First Radar Design with Various Apps
▪ High Level Simulation with Probabilistic Model – Tracking – Control
41 Thank You!
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