雷达
R2026b雷达工程团队使用 MATLAB® 和 Simulink® 来设计、分析、仿真和测试多功能雷达系统。将 Radar Toolbox 与其他 MathWorks® 产品结合使用，可以缩短开发时间，尽早消除设计问题，并简化机载、陆基、海上和汽车雷达系统的分析和测试。
使用 MathWorks 针对雷达系统提供的产品，可以开发支持雷达系统完整生命周期的模型：
执行链路预算分析，进行架构建模并评估系统设计利弊。
创建地理参考场景并仿真雷达信号、检测和轨迹。
针对不同波形和相控阵前端设计信号和数据处理链。
自动生成用于原型构建和部署的 HDL 或 C 代码。
适用产品: 雷达
Radar Toolbox
Design and analyze monostatic, bistatic, and 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
分析并可视化地理信息
主题
雷达系统工程
- Radar Link Budget Analysis (Radar Toolbox)
Use the Radar Designer app to perform a link budget analysis when designing a radar system. - Radar Architecture: System Components and Requirements Allocation (Part 1) (Radar Toolbox)
Starting from a set of performance requirements, design, implement, and test a radar system in Simulink - Part 1. - Define and Test Tracking Architectures for System-of-Systems (Sensor Fusion and Tracking Toolbox)
This example shows how to define the tracking architecture of a system-of-systems that includes multiple detection-level multi-object trackers and track-level fusion algorithms.
雷达场景仿真
- Radar Performance Analysis over Terrain (Mapping Toolbox)
The performance of a radar system can depend on its operating environment. This example shows how radar detection performance improves as target elevation increases above the terrain. - Simulate and Track Targets with Terrain Occlusions (Sensor Fusion and Tracking Toolbox)
This example shows you how to model a surveillance scenario in a mountainous region where terrain can occlude both ground and aerial vehicles from the surveillance radar. (自 R2022a 起) - Simulated Land Scenes for Synthetic Aperture Radar Image Formation (Radar Toolbox)
Simulate I/Q signals from a synthetic aperture radar (SAR) system that uses platform motion to mimic a longer aperture to improve cross-range resolution using a range migration focusing algorithm. (自 R2022a 起)
多功能雷达
- Adaptive Tracking of Maneuvering Targets with Managed Radar (Radar Toolbox)
Employ radar resource management to efficiently track multiple maneuvering targets and use an interacting multiple model (IMM) filter to estimate when the target is maneuvering to optimize radar revisit times. - Multibeam Radar for Adaptive Search and Track (Radar Toolbox)
UseradarDataGeneratoras part of a closed-loop simulation of a multifunction phased array radar (MPAR) tracking multiple maneuvering targets. (自 R2022b 起) - Track Space Debris Using a Keplerian Motion Model (Sensor Fusion and Tracking Toolbox)
This example shows how to simulate space debris withtrackingScenarioand how to configure afusionRadarSensorin monostatic mode to generate synthetic detections of space debris.
雷达天线、波束成形和波形
- Modeling Mutual Coupling in Large Arrays Using Embedded Element Pattern (Phased Array System Toolbox)
Model mutual coupling effects between array elements by using an embedded pattern technique. The example models an array two ways: (1) using the pattern of the isolated element or (2) using the embedded element pattern, and then compares both with the full-wave Method of Moments (MoM)-based solution of the array. - Conventional and Adaptive Beamformers (Phased Array System Toolbox)
Apply three beamforming algorithms to narrowband array data: the phase shift beamformer, the minimum variance distortionless response (MVDR) beamformer, and the linearly constrained minimum variance (LCMV) beamformer. - 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).
代码生成和部署
- Rectangular Array MVDR Beamformer (Phased Array System Toolbox)
Implement a minimum-variance distortionless-response (MVDR) beamformer for a 4x4 rectangular antenna array on FPGA. - Processor-in-the-Loop Verification of JPDA Tracker for Automotive Applications (Sensor Fusion and Tracking Toolbox)
Generate embedded code for a JPDA tracker and verify it using processor-in-the-loop (PIL) simulations. (自 R2022a 起)
精选示例
SAR Target Classification Using Deep Learning
Create and train a simple convolutional neural network (CNN) to classify SAR targets using deep learning.
Extended Target Tracking with Multipath Radar Reflections in Simulink
Model and mitigate multipath radar reflections in a highway driving scenario in Simulink similarly to the Highway Vehicle Tracking with Multipath Radar Reflections MATLAB example.
Maritime Clutter Suppression with Neural Networks
Train and evaluate a convolutional neural network to remove clutter returns from maritime radar PPI images using the Deep Learning Toolbox™.
Display Animation of Radar Images over GOES Backdrop
Display a movie of radar images collected once per hour over a backdrop.
Air Traffic Control
Generate an air traffic control scenario, simulate radar detections from an airport surveillance radar (ASR), and configure a global nearest neighbor (GNN) tracker to track the simulated targets using the radar detections. This enables you to evaluate different target scenarios, radar requirements, and tracker configurations without needing access to costly aircraft or equipment. This example covers the entire synthetic data workflow.
Planning Radar Network Coverage over Terrain
Plan a radar network using propagation modeling over terrain and use the radar equation to determine whether target locations can be detected.
视频
什么是 Radar Toolbox
本视频介绍了 Radar Toolbox，该工具箱提供了用于设计、仿真、分析和测试单基地、双基地及多功能雷达系统的算法和工具。
什么是 Phased Array System Toolbox
本视频介绍了 Phased Array System Toolbox，该工具箱提供了用于设计和仿真无线通信、雷达、声呐及声学应用中传感器阵列和波束成形系统的算法和 App。
什么是 Sensor Fusion and Tracking Toolbox
本视频概述了什么是传感器融合，以及它如何有助于自主系统的设计，并涵盖了若干示例场景，展示了传感器融合的多种实现方式。
什么是 Mapping Toolbox
本视频介绍了 Mapping Toolbox，它提供了用于转换地理数据和创建地图显示的算法与函数。
