lijiunderstand / radarsimpy

Radar Simulator built with Python and C++

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

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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 interference
  • 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
  • Direction of arrival (DoA) estimation
    • MUltiple SIgnal Classification (MUSIC) DoA estimations for a uniform linear array (ULA)
    • Root-MUSIC DoA estimation for a ULA
    • Estimation of Signal Parameters via Rational Invariance Techniques (ESPRIT) DoA estimation for a ULA
  • Beamformer
    • Capon beamformer
    • Bartlett beamformer
  • Constant false alarm rate (CFAR)
    • 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
• pymeshlab (preferred) or meshio
• Visual C++ Runtime (Windows)

Installation

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 the x-y plane. 0 deg is the positive x-axis, 90 deg is the positive y-axis
  • theta (deg): angle on the z-x plane. 0 deg is the positive z-axis, 90 deg is the x-y plane
  • 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 the z-axis. Positive yaw rotates the object from the positive x-axis to the positive y-axis
  • pitch (deg): rotation along the y-axis. Positive pitch rotates the object from the positive x-axis to the positive z-axis
  • roll (deg): rotation along the x-axis. Positive roll rotates the object from the positive z-axis to the 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

The source files of these Jupyter notebooks are available here.

• Radar modeling and point target simulation

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

• 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

• Receiver 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)
  • radarsimpy.processing.doa_music: Estimate DoA using MUSIC for a ULA
  • radarsimpy.processing.doa_root_music: Estimate DoA using Root-MUSIC for a ULA
  • radarsimpy.processing.doa_esprit: Estimate DoA using ESPRIT for a ULA
  • radarsimpy.processing.doa_capon: Capon (MVDR) beamforming for a ULA
  • radarsimpy.processing.doa_bartlett: Bartlett beamforming for a ULA

• 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 a certain probability of detection (Pd) and probability of false alarm (Pfa) in receiver operating characteristic (ROC)