jaysulk / radarsimpy

Radar Simulator built with Python and C++

!!! This module needs to be built/used together with RadarSimC (the C++ engine for radar simulator)

RadarSimPy

A Radar Simulator for Python

Key Features

• Radar Modeling

  • Radar transceiver modeling
  • Arbitrary waveform
  • Phase noise
  • Phase/Amplitude modulation
  • Fast-time/Slow-time modulation

• Simulation

  • Simulation of radar baseband data from point targets
  • Simulation of radar baseband data from 3D modeled objects/environment (#raytracing)
  • Simulation of target's RCS (#raytracing)
  • Simulation of LiDAR point cloud from 3D modeled objects/environment (#raytracing)

• Signal Processing

  • Range/Doppler processing
  • 1D/2D cell-averaging CFAR (CA-CFAR)
  • 1D/2D ordered-statistic CFAR (OS-CFAR)

• Characterization

  • Radar detection characteristics based on Swerling's models

Dependence

• numpy
• scipy
• meshio
• Visual C++ Runtime (Windows only)

Installation

Please fill out this form to request the module if you are interested in using RadarSimPy

To use the module, please put the radarsimpy folder within your project folder as shown below.

---

• Windows

  • your_project.py
  • your_project.ipynb
  • radarsimpy
    • __init__.py
    • radarsimc.dll
    • scene.xxx.pyd
    • ...

---

• Linux

  • your_project.py
  • your_project.ipynb
  • radarsimpy
    • __init__.py
    • libradarsimc.so
    • scene.xxx.so
    • ...

---

Acceleration

This module supports CPU/GPU parallelization. CPU parallelization is implemented through OpenMP. GPU parallelization (CUDA) has been added since v6.0.0.

	CPU	GPU (CUDA)
Windows	✅	✅
Linux	✅	✅
macOS	❌	❌

Coordinate Systems

• Scene Coordinate

  • axis (m): [x, y, z]
  • phi (deg): angle on x-y plane. Positive x-axis is 0 deg, positive y-axis is 90 deg
  • theta (deg): angle on z-x plane. Positive z-axis is 0 deg, x-y plane is 90 deg
  • azimuth (deg): azimuth -90 ~ 90 deg equal to phi -90 ~ 90 deg
  • elevation (deg): elevation -90 ~ 90 deg equal to theta 180 ~ 0 deg

• Object's Local Coordinate

  • axis (m): [x, y, z]
  • yaw (deg): rotation along z-axis. Positive yaw rotates object from positive x-axis to positive y-axis
  • pitch (deg): rotation along y-axis. Positive pitch rotates object from positive x-axis to positive z-axis
  • roll (deg): rotation along x-axis. Positive roll rotates object from positive z-axis to negative y-axis
  • origin (m): [x, y, z]
  • rotation (deg): [yaw, pitch, roll]
  • rotation (deg/s): rate [yaw rate, pitch rate, roll rate]

Usage Examples

• Radar modeling and point target simulation

  • Doppler radar
  • FMCW radar
  • TDM MIMO FMCW radar
  • PMCW radar
  • Arbitrary waveform
  • Phase noise
  • CFAR

• Radar modeling and 3D scene simulation with raytracing

  • Imaging radar
  • FMCW radar with a corner reflector
  • FMCW radar with a plate
  • FMCW radar with a car
  • Doppler of a turbine
  • Micro-Doppler
  • Multi-path effect

• 3D modeled target's RCS simulation

  • Corner reflector RCS
  • Plate RCS
  • Car RCS

• LiDAR point cloud

  • LIDAR point cloud

• Characterization

  • Receiver operating characteristic (ROC)

API Reference

• Radar Model: Classes to define a radar system
  • radarsimpy.Transmitter: Radar transmitter
  • radarsimpy.Receiver: Radar receiver
  • radarsimpy.Radar: Radar system
• Simulator: Radar baseband signal simulator
  • radarsimpy.simulator.simpy: Simulates and generates raw time domain baseband data (Python engine)
  • radarsimpy.simulator.simc: Simulates and generates raw time domain baseband data (C++ engine)
• Raytracing: Raytracing module for radar scene simulation
  • radarsimpy.rt.lidar_scene: Simulates LiDAR's point cloud based on a 3D environment model with ray tracing
  • radarsimpy.rt.rcs_sbr: Simulates target's radar cross section (RCS) based on the 3D model with ray tracing
  • radarsimpy.rt.scene: Simulates radar's response signal in a 3D environment model with ray tracing
• Processing: Basic radar signal processing module
  • radarsimpy.processing.range_fft: Calculate range profile matrix
  • radarsimpy.processing.doppler_fft: Calculate range-Doppler matrix
  • radarsimpy.processing.range_doppler_fft: Range-Doppler processing
  • radarsimpy.processing.cfar_ca_1d: 1D Cell Averaging CFAR (CA-CFAR)
  • radarsimpy.processing.cfar_ca_2d: 2D Cell Averaging CFAR (CA-CFAR)
  • radarsimpy.processing.cfar_os_1d: 1D Ordered Statistic CFAR (OS-CFAR)
  • radarsimpy.processing.cfar_os_2d: 2D Ordered Statistic CFAR (OS-CFAR)
• Tools: Receiver operating characteristic analysis
  • radarsimpy.tools.roc_pd: Calculate probability of detection (Pd) in receiver operating characteristic (ROC)
  • radarsimpy.tools.roc_snr: Calculate the minimal SNR for certain probability of detection (Pd) and probability of false alarm (Pfa) in receiver operating characteristic (ROC)