Radar Toolbox

Design, simulate, and test multifunction radar systems

Radar Toolbox includes algorithms and tools for designing, simulating, analyzing, and testing multifunction radar systems. Reference examples provide a starting point for implementing airborne, ground-based, shipborne, and automotive radar systems. Radar Toolbox supports multiple workflows, including requirements analysis, design, deployment, and field data analysis.    

You can perform link budget analysis and evaluate design trade-offs at the radar equation level interactively with the Radar Designer app. The toolbox includes models for transmitters, receivers, propagation channels, targets, jammers, and clutter. You can simulate radars at different levels of abstraction using probabilistic models and I/Q signal level models. You can process detections generated from these models or from data collected from radar systems using the signal and data processing algorithms provided in the toolbox. You can design cognitive radars that operate in crowded RF shared spectrum environments. For automotive applications, the toolbox lets you model radar sensors at the probabilistic and physics-based levels and simulate data, including micro-Doppler signatures and object lists.  

For simulation acceleration or rapid prototyping, the toolbox supports C code generation.  

Get Started:

Radar Applications

Simulate multifunction radars for automotive, surveillance, and SAR applications. Synthesize radar signals to train machine and deep learning models for target and signal classification.

Automotive Radar

Design probabilistic and physics-based radar sensor models. Simulate MIMO antennas, waveforms, I/Q radar signals. Generate micro-Doppler signatures, detections, clusters and tracks.

Ghost detections due to Radar Multipath Detections.

Multifunction and Cognitive Radar

Perform closed-loop radar simulation for multifunction radar systems. Model systems that respond to environmental conditions using waveform selection, pulse repetition frequency (PRF) agility, frequency agility, and interference mitigation.

Adaptive tracking of Maneuvering targets in radar coverage.

AI for Radar

Simulate radar signals to train machine and deep learning models for target and signal classification. Label radar signals manually or automatically.

Synthesized micro-Doppler signatures for bicyclist used to train deep learning networks for object classification.

Synthetic Aperture Radar (SAR)

Estimate SAR link budgets for airborne and space applications. Simulate and test image formation algorithms for spotlight and stripmap modes.

Designing SAR systems.

Radar Systems Engineering

Simulate radar architectures that connect requirements to models and tests. Analyze radar link budgets. Predict detection and tracking performance in different environments.

Radar Architecture Modeling

With System Composer, develop architectures for multifunction radars that include subsystem componentization, traceability, and requirements-based testing.

Radar architecture integrated with radar subsystem models.

Detecting and Tracking Statistics for Radar Equations

Explore designs using the Radar Designer app to populate radar equations for searching and tracking. Visualize results interactively to compare design choices. Determine detectability factors, receiver operating characteristics (ROC), and tracker operating characteristics (TOC) and generate range-angle-height (Blake) charts.

Interactively designing systems with the Radar Designer app.

Antenna and Receiver Gains and Losses

Calculate beam and scanning loss, beam-dwell factor, eclipsing loss, noise figure, matching loss, pulse integration loss, CFAR loss, and MTI loss.

Effective probability of detection stoplight chart.

Environment and Clutter

Model and analyze radar propagation effects of land and sea clutter; atmospheric attenuation due to gas, fog, rain and snow; and lens effects losses. Characterize clutter using sea state and permittivity in addition to land surface with vegetation type and permittivity.

Planning radar coverage in the presence of terrain.

Radar Data Synthesis

Design radar sensor models; signal, detection and track generators; propagation channels; clutter; target radar cross section (RCS); and micro-Doppler signatures. Create realistic radar scenarios for airborne, ground-based, and shipborne platforms and ground-truth trajectories.

Radar Sensor Models: Signal, Detection, and Track Generators

Simulate radar data at probabilistic or physics-based levels of abstraction. For faster simulations, generate probabilistic radar detections and tracks to test tracking and sensor fusion algorithms. Alternatively, higher fidelity physics-based simulations start with transmitted waveforms, propagate signals through the environment, reflect them off targets, and receive them at the radar.

Radar Scenario Generation

Create realistic radar scenarios for airborne, ground-based, and shipborne platforms and targets. Model platform motion and orientation based on waypoints and trajectories or by simulating inertial navigation systems. Visualize and record the time evolution of the radar scenario.

Multitarget scenario for radar system.

Radar Signal and Data Processing

Design waveform libraries for multifunction radars. Develop algorithms for detecting targets in the presence of noise and clutter. Estimate range, angle, and Doppler responses for radar targets. Perform clustering and multitarget tracking on radar returns.

Waveform Libraries and Doppler Estimation

Create pulse compression libraries of waveforms with corresponding matched filtering and stretch processing. Estimate received signal parameters. Determine direction-of-arrival, detection, range, angle, and Doppler responses of targets and interference sources.

Removing ground clutter with moving target indication (MTI) filtering.


Cluster radar detections generated from radar returns on extended objects using density-based algorithms.

Clustered detections of extended objects using a DBSCAN algorithm.

Search and track scheduling for multifunction phased array radar.