雷达

分析、设计和仿真雷达系统

您可以使用 MathWorks^® 产品来设计、仿真、分析和测试多功能雷达系统，将其作为实现的起点。将 Radar Toolbox 与其他产品相结合，研究在物理环境中移动和运行的平台中的机载、陆基、舰载与汽车雷达系统及场景。

根据指定的需求和性能指标对雷达系统应用权衡分析
在不同抽象层次上对发射机、接收机、传播信道、目标、干扰器和杂波进行建模
对有源和无源相控阵进行仿真以执行波束成形和信号处理算法设计
使用各种坐标系和地图来设计和仿真自主系统场景，包括平台、轨迹、路径、环境和传感器
将雷达测量结果与来自其他真实传感器和传感器阵列的数据进行融合
编译和处理检测与跟踪统计数据，并使用多目标跟踪器和估计滤波器来评估架构

使用 Deep Learning Toolbox™ 将 AI 集成到您的设计和模型中。使用 MATLAB^® Coder™，您可以根据开发的信号处理算法生成代码，并将解决方案部署到硬件上。

适用产品：雷达

Radar Toolbox

Design, simulate, and test multifunction radar systems

Phased Array System Toolbox

Design and simulate phased array and beamforming systems

Sensor Fusion and Tracking Toolbox

Design, simulate, and test multisensor tracking and positioning systems

Mapping Toolbox

Analyze and visualize geographic information

主题

建模和仿真

Airborne SAR System Design (Radar Toolbox)
Design an X-band Synthetic Aperture Radar (SAR) sensor parameters.
Radar Signal Simulation and Processing for Automated Driving (Radar Toolbox)
Model the hardware, signal processing, and propagation environment of an automotive radar.

地图构建

Create Maps with Data in Projected Coordinate Reference Systems (Mapping Toolbox)
Create a map using multiple data sets with coordinates in geographic and projected coordinate reference systems.
Export Images and Raster Grids to GeoTIFF (Mapping Toolbox)
Write data referenced to standard geographic and projected coordinate systems to GeoTIFF files.

态势感知和状态估计

Extended Object Tracking of Highway Vehicles with Radar and Camera (Sensor Fusion and Tracking Toolbox)
Track highway vehicles around an ego vehicle as extended objects that span multiple sensor resolution cells.
Visual-Inertial Odometry Using Synthetic Data (Sensor Fusion and Tracking Toolbox)
Estimate the pose (position and orientation) of a ground vehicle using an inertial measurement unit (IMU) and a monocular camera.

目标跟踪和运动规划

Object Tracking and Motion Planning Using Frenet Reference Path (Sensor Fusion and Tracking Toolbox)
Dynamically plan the motion of an autonomous vehicle based on estimates of the surrounding environment.
Adaptive Tracking of Maneuvering Targets with Managed Radar (Radar Toolbox)
This example employs radar resource management to efficiently track multiple maneuvering targets. An interacting multiple model (IMM) filter estimates when the target is maneuvering to optimize radar revisit times.

机器学习和深度学习

Label Radar Signals with Signal Labeler (Radar Toolbox)
Label the time and frequency features of pulse radar signals with added noise.
Radar and Communications Waveform Classification Using Deep Learning (Phased Array System Toolbox)
Classify radar and communications waveforms using the Wigner-Ville distribution (WVD) and a deep convolutional neural network (CNN).

硬件部署

FPGA-Based Range-Doppler Processing - Algorithm Design and HDL Code Generation (Phased Array System Toolbox)
Design a range-Doppler response that is implementation-ready for a FPGA and compare a simulation output of the model with a Simulink^® behavioral model.
Estimating Orientation Using Inertial Sensor Fusion and MPU-9250 (Sensor Fusion and Tracking Toolbox)
Obtain data from an InvenSense MPU-9250 IMU sensor, and to use the 6-axis and 9-axis fusion algorithms in the sensor data to compute orientation of the device.

精选示例

Phased Array Gallery

Model and visualize a variety of antenna, microphone, and hydrophone array geometries with Phased Array System Toolbox™.

Radar Link Budget Analysis

Use the Radar Designer app to perform a link budget analysis when designing a radar system. Set the radar performance requirements, compute radar metrics, configure the environment, and compare several alternative designs.

Pedestrian and Bicyclist Classification Using Deep Learning

Classify pedestrians and bicyclists based on their micro-Doppler characteristics using deep learning and time-frequency analysis.

Visualize Aircraft Line-of-Sight over Terrain

Compute and visualize line-of-sight visibility of an aircraft from a ground location over terrain.

Planning Radar Network Coverage over Terrain

Plan a radar network using propagation modelling over terrain. DTED level-1 terrain data is imported for a region that contains five candidate monostatic radar sites. The radar equation is used to determine whether target locations can be detected, where additional path loss is calculated using either the Longley-Rice propagation model or Terrain Integrated Rough Earth Model™ (TIREM™). The best three sites are selected for detection of a target that is flying at 500 meters above ground level. The scenario is updated to model a target that is flying at 250 meters above ground level. Radar coverage maps are shown for both scenarios.