Radar Toolbox

 

Radar Toolbox

Design, simulate, and test multifunction radar systems

AI for Radar

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

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.

Radar Systems Engineering

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

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.

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.

Synthetic Aperture Radar (SAR)

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

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.”

Ram Pravesh, Bharat Electronics Limited

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