AI for Radar
Simulate radar signals to train machine and deep learning models for target and signal classification. Label radar signals manually or automatically.
Synthetic Aperture Radar (SAR)
Estimate SAR link budgets for airborne and space applications. Simulate and test image formation algorithms for spotlight and stripmap modes.
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.
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.
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.
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 Scenes: Land and Sea Surface Models
Model land and sea surfaces to generate radar surface returns across different abstraction levels. Assess the effects of surface occlusions on probabilistic detections and received I/Q signals. Synthesize radar data from a realistic scene, including surface models with custom reflectivity map and Speckle to test and evaluate image formation algorithms.
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.
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.
Cluster radar detections generated from radar returns on extended objects using density-based algorithms.