Radar Toolbox

 

Radar Toolbox

Design, simulate, and test multifunction radar systems

Graph shows Precision versus Recall curve for a threshold value of zero point five for different classes.

AI for Radar

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

Visualize the use of the MPAR resources over the time of a scenario.

Multifunction 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.

Automotive Radar

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

System Composer with radar design and panel to show requirements status.

Radar Systems Engineering

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

Radar Designer App with active design shown, including requirements, stoplight chart, and pattern plots.

Detecting and Tracking Statistics for Radar Equations

Explore designs using the Radar Designer app to determine detectability factors, receiver operating characteristics (ROC), and tracker operating characteristics (TOC) and generate range-angle-height (Blake) charts.

Terrain-based map showing combined target coverage area for two radar systems.

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.

Reduced speckles through multi-looking processing by trading image resolution.

Synthetic Aperture Radar (SAR)

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

Simulate detection and tracking with a monostatic radar with different scanning models for various scenarios.

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.

Radar Scenes: Land and Sea Surface Models

Model land and sea surfaces for radar returns at various abstraction levels. Assess surface occlusions’ impact on probabilistic detections and received I/Q signals. Synthesize radar data from realistic scenes, including surface models with custom reflectivity map and Speckle, to test and evaluate image formation algorithms.

“With the help of AI, a lot more can be done. We have found that if more data is not available, then simulated data can also be generated with the help of MATLAB.”

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