Radar

Design, simulate, and test radar systems

Radar engineering teams use MATLAB® and Simulink® to design, analyze, simulate, and test multifunction radar systems. Combine Radar Toolbox with other MathWorks® products to reduce development time, eliminate design problems early, and streamline analysis and testing for airborne, ground-based, maritime, and automotive radar systems.

Antenna and RF systems, signal processing, and data processing in a development environment. Create model scenarios and scenes including resource management and controls.

With MathWorks products for radar systems, you can develop models that support the entire life cycle of radar systems:

  • Perform link budget analysis, model architectures, and evaluate system design tradeoffs.

  • Create georeferenced scenarios and simulate radar signals, detections, and tracks.

  • Design signal and data processing chains for different waveforms and phased array front ends.

  • Automatically generate HDL or C code for prototyping and deployment.

Products for Radar

Radar Toolbox

Design and analyze monostatic, bistatic, and multifunction radar systems

Phased Array System Toolbox

Design and simulate phased array and beamforming systems

Sensor Fusion and Tracking Toolbox

Design, simulate, and test multisensor tracking and positioning systems

Mapping Toolbox

Analyze and visualize geographic information

Topics

Radar Systems Engineering

Radar Scenario Simulations

  • Radar Performance Analysis over Terrain (Mapping Toolbox)
    The performance of a radar system can depend on its operating environment. This example shows how radar detection performance improves as target elevation increases above the terrain.
  • Simulate and Track Targets with Terrain Occlusions (Sensor Fusion and Tracking Toolbox)
    This example shows you how to model a surveillance scenario in a mountainous region where terrain can occlude both ground and aerial vehicles from the surveillance radar. (Since R2022a)
  • Simulated Land Scenes for Synthetic Aperture Radar Image Formation (Radar Toolbox)
    Simulate I/Q signals from a synthetic aperture radar (SAR) system that uses platform motion to mimic a longer aperture to improve cross-range resolution using a range migration focusing algorithm. (Since R2022a)

Multifunction Radar

  • Adaptive Tracking of Maneuvering Targets with Managed Radar (Radar Toolbox)
    Employ radar resource management to efficiently track multiple maneuvering targets and use an interacting multiple model (IMM) filter to estimate when the target is maneuvering to optimize radar revisit times.
  • Multibeam Radar for Adaptive Search and Track (Radar Toolbox)
    Use radarDataGenerator as part of a closed-loop simulation of a multifunction phased array radar (MPAR) tracking multiple maneuvering targets. (Since R2022b)
  • Track Space Debris Using a Keplerian Motion Model (Sensor Fusion and Tracking Toolbox)
    This example shows how to model earth-centric trajectories using custom motion models within trackingScenario, how to configure a fusionRadarSensor in monostatic mode to generate synthetic detections of space debris, and how to set up a multi-object tracker to track the simulated targets.

Radar Antennas, Beamforming, and Waveforms

  • Modeling Mutual Coupling in Large Arrays Using Embedded Element Pattern (Phased Array System Toolbox)
    Model mutual coupling effects between array elements by using an embedded pattern technique. The example models an array two ways: (1) using the pattern of the isolated element or (2) using the embedded element pattern, and then compares both with the full-wave Method of Moments (MoM)-based solution of the array.
  • Conventional and Adaptive Beamformers (Phased Array System Toolbox)
    Apply three beamforming algorithms to narrowband array data: the phase shift beamformer, the minimum variance distortionless response (MVDR) beamformer, and the linearly constrained minimum variance (LCMV) beamformer.
  • Radar and Communications Waveform Classification Using Deep Learning (Phased Array System Toolbox)
    Classify radar and communications waveforms using the Wigner-Ville distribution (WVD) and a deep convolutional neural network (CNN).

Code Generation and Deployment

Related Information

Featured Examples

SAR Target Classification Using Deep Learning

SAR Target Classification Using Deep Learning

Create and train a simple convolution neural network to classify SAR targets using deep learning.

(Radar Toolbox)

  • Since R2021b
Extended Target Tracking with Multipath Radar Reflections in Simulink

Extended Target Tracking with Multipath Radar Reflections in Simulink

Model and mitigate multipath radar reflections in a highway driving scenario in Simulink similarly to the Highway Vehicle Tracking with Multipath Radar Reflections MATLAB example.

(Radar Toolbox)

  • Since R2021b
Maritime Clutter Suppression with Neural Networks

Maritime Clutter Suppression with Neural Networks

Train and evaluate a convolutional neural network to remove clutter returns from maritime radar PPI images using the Deep Learning Toolbox™.

(Radar Toolbox)

  • Since R2022b
Display Animation of Radar Images over GOES Backdrop

Display Animation of Radar Images over GOES Backdrop

Display a movie of radar images collected once per hour over a backdrop.

(Mapping Toolbox)

Air Traffic Control

Air Traffic Control

Generate an air traffic control scenario, simulate radar detections from an airport surveillance radar (ASR), and configure a global nearest neighbor (GNN) tracker to track the simulated targets using the radar detections. This enables you to evaluate different target scenarios, radar requirements, and tracker configurations without needing access to costly aircraft or equipment. This example covers the entire synthetic data workflow.

(Sensor Fusion and Tracking Toolbox)

Planning Radar Network Coverage over Terrain

Planning Radar Network Coverage over Terrain

Plan a radar network using propagation modeling over terrain and use the radar equation to determine whether target locations can be detected.

(Radar Toolbox)