Radar engineers use MATLAB and Simulink to improve the pace of radar system design from the antenna array, to radar signal processing algorithms, to data processing and control.
With MATLAB and Simulink, radar engineers can:
- Perform feasibility studies, predict system performance, and coverage analysis on 3D terrain
- Interactively design and analyze radar system architecture
- Design, analyze, integrate, and visualize antenna elements and arrays, and RF components
- Model the propagation channel, targets, jammers, and clutter
- Design and test multifunction, multisensor phased array tracking and positioning systems
- Generate code for prototyping or production, in floating or fixed-point, for MCUs, GPUs, SoCs, and FPGA devices
- Synthesize the data and train deep learning models for applications like target classification and modulation identification
Using MATLAB and Simulink for Radar Systems
Engineers use MATLAB and Simulink for the end-to-end design, simulation, and test of multifunction radar systems. Radar system engineers can perform feasibility analysis, parameterized performance prediction with metrics, resource management, and coverage analysis using 3D terrain. Engineers can explore the characteristics of sensor arrays and waveforms to perform link budget analysis. Engineers can also define and analyze system or software architectures. Subsystem engineers can populate the architectural model with the behavioral models developed in MATLAB or Simulink or C/C++.
Antenna and RF
Antenna and RF engineers use MATLAB and Simulink as a common design environment to prototype signal chain elements, including RF, antenna, and digital elements. Engineers can then combine the work of multiple teams as a system-level executable model.
Engineers can mix high-level and high-fidelity models to simulate component interactions, evaluate design tradeoffs, and analyze the performance impact of design choices. Engineers can also include S-parameters and other RF measurements in system simulations.
Radar signal processing engineers use MATLAB, Simulink, and apps to design and analyze phased array multifunction systems that require frequency, PRF, waveform, and beam pattern agility. Engineers can model the dynamics of radar and EW systems and targets for ground-based, airborne, and ship-borne. Engineers can also leverage built-in libraries of signal processing algorithms, including beamforming, matched filtering, direction of arrival (DOA) estimation, and target detection.
Radar data processing engineers use MATLAB and Simulink for designing, simulating, and testing systems that fuse data from multiple sensors to maintain situational awareness and localization. Engineers can test their algorithms with real-world data or synthetic data by modelling radar, EO/IR, IMU, and GPS sensors in MATLAB. Built-in libraries for multi-object trackers and estimation filters are available to evaluate architectures that combine grid-level, detection-level, and object- or track-level fusion with metrics to validate the performance against ground truth.
Targets and Environment
Radar or EW engineers use MATLAB and Simulink to model wave propagation, clutter and jammer and interference, target motion with constant velocity and acceleration, and target cross-section. Engineers can also model atmospheric attenuation using line-of-sight (LOS) propagation models. These models calculate signal propagation through atmospheric gases, rain, and fog and clouds.
Radar engineers deploy MATLAB or Simulink models to many deployment targets in a production environment. Engineers can convert models to C, C++, HDL, and CUDA® to deploy onto embedded or edge devices. Engineers can also integrate models with in-house developed enterprise desktop or server applications. Engineers can speed up simulations and applications with generated C/C++ and MEX code, or by using GPUs or pool of nodes.
AI for Radar
Radar engineers use MATLAB for developing artificial intelligence-based applications in cognitive radar, software-defined radio, and intelligent receiver. Engineers synthesize data with MATLAB models that can be used to train deep learning and machine learning networks for a range of applications like modulation identification or target classification.