RadarSimPy

A Radar Simulator for Python

RadarSimPy is a powerful and versatile Python-based Radar Simulator that models radar transceivers and simulates baseband data from point targets and 3D models. Its signal processing tools offer range/Doppler processing, direction of arrival estimation, and beamforming using various cutting-edge techniques, and you can even characterize radar detection using Swerling’s models. Whether you’re a beginner or an advanced user, RadarSimPy is the perfect tool for anyone looking to develop new radar technologies or expand their knowledge of radar systems.

Key Features

• Radar Modeling

  • Radar transceiver modeling
  • Arbitrary waveform (CW, FMCW, PMCW, Pulse, ...)
  • Phase noise
  • Phase/amplitude modulation (CDM, FDM, DDM, TDM, ...)
  • Fast-time/slow-time modulation

• Simulation

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

• 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
    • Iterative Adaptive Approach (IAA) for amplitude and phase estimation
  • 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

• Python >= 3.9

• NumPy >= 2.0

• SciPy

• PyMeshLab (preferred) or meshio

• Windows

  • Visual C++ Runtime

• Ubuntu 22.04

  • GCC 11 (Included by defualt, no additional installation required)

• Ubuntu 24.04

  • GCC 14 (Included by defualt, no additional installation required)

• Generic Linux x86-64

  • Try the module for Ubuntu 22.04 or Ubuntu 24.04
  • Request a Custom Build if it doesn't work

• MacOS Intel

  • GCC 13

    brew install gcc@13

• MacOS Apple Silicon

  • GCC 14

    brew install gcc@14

Installation

Download the pre-built module, and put the radarsimpy folder within your project folder as shown below:

---

• Windows

  • your_project.py
  • your_project.ipynb
  • radarsimpy
    • __init__.py
    • radarsimcpp.dll
    • simulator.xxx.pyd
    • rt.xxx.pyd
    • radar.py
    • processing.py
    • ...

---

• Linux

  • your_project.py
  • your_project.ipynb
  • radarsimpy
    • __init__.py
    • libradarsimcpp.so
    • simulator.xxx.so
    • rt.xxx.so
    • radar.py
    • processing.py
    • ...

---

• MacOS

  • your_project.py
  • your_project.ipynb
  • radarsimpy
    • __init__.py
    • libradarsimcpp.dylib
    • simulator.xxx.so
    • rt.xxx.so
    • radar.py
    • processing.py
    • ...

---

Acceleration

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

	CPU (x86-64)	CPU (ARM64)	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 rate (deg/s): [yaw rate, pitch rate, roll rate]

Usage Examples

Check all the usage examples on radarsimx.com. The source files of these examples are available at radarsimnb repository.

• Waveform

  • Pulsed Radar
  • Interferometric Radar
  • Arbitrary waveform
  • PMCW radar
  • Doppler radar
  • FMCW radar

• MIMO

  • Imaging radar
  • DoA estimation
  • TDM MIMO FMCW radar

• Angle Estimation

  • Imaging radar
  • DoA estimation

• Ray Tracing

  • Imaging radar
  • Multi-path effect
  • Micro-Doppler
  • Doppler of a turbine
  • FMCW radar with a car
  • FMCW radar with a plate
  • FMCW radar with a corner reflector
  • Cross-Polarization and Co-Polarization RCS
  • Car RCS
  • Plate RCS
  • Corner reflector RCS
  • LIDAR point cloud

• CFAR

  • FMCW radar with a corner reflector
  • CFAR

• Interference

  • Interference

• System Characterization

  • FMCW Radar Link Budget - Ideal Point Target
  • Phase noise
  • Receiver operating characteristic (ROC)

• Radar Cross Section

  • Cross-Polarization and Co-Polarization RCS
  • Car RCS
  • Plate RCS
  • Corner reflector RCS

• LiDAR

  • LIDAR point cloud

Build

Building radarsimpy requires to access the source code of radarsimcpp. If you don't have access to radarsimcpp, please use the pre-built module.

• Windows (MSVC)

  build_win.bat --arch cpu --test on

  Build for GPU (CUDA)

  build_win.bat --arch gpu --test on

• Linux (GCC)

  ./build_linux.sh --arch=cpu --test=on

  Build for GPU (CUDA)

  ./build_linux.sh --arch=gpu --test=on

• MacOS (GCC)

  ./build_macos.sh --arch=cpu --test=on

API Reference

Please check the Documentation