Key Features#

RadarSimPy provides comprehensive tools for radar system modeling, simulation, and signal processing. This page outlines the core capabilities available in the library.

Radar System Modeling#

Transceiver Configuration

RadarSimPy supports flexible radar transceiver modeling with:

  • Arbitrary Waveforms - Full support for various radar waveforms:

    • Continuous Wave (CW)

    • Frequency Modulated Continuous Wave (FMCW)

    • Phase Modulated Continuous Wave (PMCW)

    • Pulsed radar waveforms

    • Custom user-defined waveforms

  • Phase Noise Modeling - Simulate realistic oscillator phase noise effects on radar performance

  • Modulation Schemes - Multiple modulation techniques for MIMO and multi-channel systems:

    • Code Division Multiplexing (CDM)

    • Frequency Division Multiplexing (FDM)

    • Doppler Division Multiplexing (DDM)

    • Time Division Multiplexing (TDM)

    • Hybrid modulation schemes

  • Signal Modulation - Advanced modulation capabilities:

    • Fast-time modulation (pulse-to-pulse variation)

    • Slow-time modulation (across radar frames)

    • Amplitude and phase control

Radar Simulation Capabilities#

Target Simulation

  • Point Target Simulation - Generate radar baseband data from point scatterers with configurable:

    • Position, velocity, and acceleration

    • Radar cross-section (RCS)

    • Multi-path and reflection effects

  • 3D Object Simulation - High-fidelity simulation from 3D mesh models:

    • Support for common 3D formats (STL, OBJ, PLY, etc.)

    • Ray-tracing based electromagnetic scattering

    • Dynamic object motion and articulation

    • Complex scene environments with multiple objects

  • RCS Calculation - Compute monostatic and bistatic radar cross-sections for 3D models across:

    • Multiple frequencies

    • Various aspect angles

    • Polarization configurations

Interference Simulation

  • Model radar-to-radar interference scenarios

  • Evaluate mutual interference effects in dense radar environments

  • Support for both intra-vehicle and inter-vehicle interference

LiDAR Simulation

  • Generate realistic LiDAR point clouds from 3D environments

  • Configurable sensor parameters (resolution, field of view)

Signal Processing Toolkit#

Range-Doppler Processing

RadarSimPy includes optimized algorithms for standard radar signal processing:

  • FFT-based Range Processing - Fast Fourier Transform for range compression

  • Doppler Processing - Coherent integration and velocity estimation

  • 2D Range-Doppler Maps - Generate and visualize range-Doppler spectra

Direction of Arrival (DoA) Estimation

Advanced DoA estimation for uniform linear arrays (ULA):

  • MUSIC Algorithm - MUltiple SIgnal Classification for super-resolution angle estimation

  • Root-MUSIC - Polynomial-rooting variant for improved computational efficiency

  • ESPRIT Algorithm - Estimation of Signal Parameters via Rotational Invariance Techniques

  • Iterative Adaptive Approach (IAA) - High-resolution amplitude and phase estimation with excellent sidelobe suppression

Beamforming Techniques

  • Capon Beamformer - Minimum variance distortionless response (MVDR) for optimal interference rejection

  • Bartlett Beamformer - Conventional delay-and-sum beamforming

CFAR Detection

Constant False Alarm Rate (CFAR) detectors for automatic target detection:

  • CA-CFAR - Cell-Averaging CFAR for homogeneous clutter

    • 1D implementation for range or Doppler

    • 2D implementation for range-Doppler maps

  • OS-CFAR - Ordered-Statistic CFAR for heterogeneous environments

    • 1D implementation for range or Doppler

    • 2D implementation for range-Doppler maps

    • Improved performance in multi-target scenarios and clutter edges

Radar Performance Characterization#

Detection Analysis

  • Swerling Target Models - Evaluate radar detection performance using statistical target models:

    • Swerling Case I - Constant RCS (one scan)

    • Swerling Case II - Variable RCS (pulse-to-pulse)

    • Swerling Case III - Dominant constant scatterer

    • Swerling Case IV - Dominant variable scatterer

    • Swerling Case V - Non-fluctuating targets

  • Probability of Detection (Pd) - Calculate detection probabilities for given:

    • Signal-to-noise ratio (SNR)

    • Probability of false alarm (Pfa)

    • Number of pulses integrated

    • Target fluctuation model

Performance Considerations#

RadarSimPy leverages optimized C++ implementations for computationally intensive operations, providing:

  • High-speed simulations suitable for Monte Carlo analysis

  • Multi-threaded processing for parallel computation

  • Efficient memory management for large-scale scenarios

  • GPU acceleration support (where applicable)

Note

For detailed API documentation and usage examples, refer to the API and Usage Examples sections.