Radar Toolbox

MAJOR UPDATE

 

Radar Toolbox

Design and analyze monostatic, bistatic, and multifunction radar systems

AI for Radar

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

Search grid visualization showing beam positions and 3dB contours for volume scanning. Grid points spread across azimuth (-60° to 60°) and elevation (-50° to 10°) with overlapping circular beam patterns.

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.

Contour map titled "Target SNR Including Antenna Patterns and Propagation Factors" displaying signal-to-noise ratio (SNR) in decibels over a geographical area, with longitude on the x-axis and latitude on the y-axis. 

Bistatic Radar

Design non-cooperative bistatic radars, synchronous and asynchronous transmitter and receiver pairs, passive signals, and bistatic propagation paths.

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

Radar Systems Engineering

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

Data Synthesis

Simulate radar data at different levels of abstraction, including the power, measurement, and waveform level.

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

Environment and Clutter

Model and analyze radar propagation effects, including land and sea clutter; atmospheric attenuation from gas, fog, rain and snow; and lens effect losses. Characterize clutter using sea state and permittivity, as well as land surface properties such as vegetation type and permittivity.

Radar display showing concentric range rings with multiple tracked objects. Display includes green radar detections, blue trajectory lines, orange track history, and numbered track labels.

Signal and Data Processing

Create waveform libraries; estimate detection ranges, angles, and Doppler; perform clustering and tracking of detections; and execute search and track operations.

Multi-look radar image showing coastal terrain with range vs azimuth. Grayscale visualization reveals topographical features, coastline, and varying surface textures.

Synthetic Aperture Radar (SAR)

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

Diagram of a Simulink model of a radar target emulator.

Performance Acceleration and Supported Hardware

Speed up simulations with GPU processing and generated C/C++ code. Deploy Simulink models to RFSoC hardware boards (with SoC Blockset).

“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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