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! 42.