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.
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.
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“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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