레이다

레이다 시스템 설계, 시뮬레이션 및 테스트

레이다 엔지니어링 팀은 MATLAB^®과 Simulink^®를 사용하여 다기능 레이다 시스템을 설계, 분석, 시뮬레이션, 테스트합니다. Radar Toolbox를 다른 MathWorks^® 제품과 함께 사용하여 개발 시간을 줄이고 설계 문제를 조기에 제거하며 공중, 지상, 해상, 자동차의 레이다 시스템 분석과 테스트를 간소화합니다.

Antenna and RF systems, signal processing, and data processing in a development environment. Create model scenarios and scenes including resource management and controls.

레이다 시스템을 위한 MathWorks 제품을 사용하여 레이다 시스템 전체 라이프사이클을 지원하는 모델을 개발할 수 있습니다.

링크 버짓 분석을 수행하고, 아키텍처를 모델링하고, 시스템 설계의 상충관계를 평가합니다.
지리참조 시나리오를 생성하고 레이다 신호, 탐지, 트랙을 시뮬레이션합니다.
다양한 파형과 위상 배열 프론트엔드에 대한 신호 및 데이터 처리 체인을 설계합니다.
프로토타이핑 및 배포를 위한 HDL 또는 C 코드를 자동 생성합니다.

레이다 관련 제품

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

다중 센서 추적 시스템 및 측위 시스템 설계, 시뮬레이션, 테스트

Mapping Toolbox

Analyze and visualize geographic information

도움말 항목

레이다 시스템 공학

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.
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.

레이다 시나리오 시뮬레이션

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.
Simulated Land Scenes for Synthetic Aperture Radar Image Formation (Radar Toolbox)
Simulate of an L-band remote-sensing SAR system by generating IQ signals from a scenario containing three targets and a wooded-hills land surface and then processing the returns using a range migration focusing algorithm.

다기능 레이다

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.
Multibeam Radar for Adaptive Search and Track (Radar Toolbox)
Use radarDataGenerator as part of a closed-loop simulation of a multifunction phased array radar (MPAR) tracking multiple maneuvering targets. At each update interval the radar requests resources from the MPAR to search for targets and revisit existing tracks.
Track Space Debris Using a Keplerian Motion Model (Sensor Fusion and Tracking Toolbox)
This example shows how to model earth-centric trajectories using custom motion models within trackingScenario, how to configure a fusionRadarSensor in monostatic mode to generate synthetic detections of space debris, and how to setup a multi-object tracker to track the simulated targets.

레이다 안테나, 빔포밍, 파형

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).

코드 생성 및 배포

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.
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.

추천 예제

SAR Target Classification Using Deep Learning

Create and train a simple convolution neural network 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®. It closely follows 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 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.

레이다