レーダー

レーダーシステムの解析、設計、およびシミュレーション

MathWorks^®製品を使用することで、実装ための開始点として、多機能レーダーシステムの設計、シミュレーション、解析、テストを行うことができます。Radar Toolboxを他の製品と組み合わせると、空中、地上、船上、および車載向けのレーダーシステムと、物理環境で移動および動作するプラットフォームのシナリオを検討することができます。

指定された要件とパフォーマンスメトリクスに基づいて、レーダーシステムのトレードオフ解析を適用する
送信機、受信機、伝播チャネル、ターゲット、ジャマー、クラッターをさまざまなレベルの抽象化でモデル化する
アクティブおよびパッシブフェーズドアレイをシミュレーションし、ビームフォーミングと信号処理のアルゴリズム設計を行う
プラットフォーム、軌跡、パス、環境、センサーを組み込んだ自律システムのシナリオを設計し、さまざまな座標系とマップを使用してシミュレーションする
レーダーの測定値を、他の実際のセンサーやセンサーアレイから取得したデータと融合する
検出および追跡の統計をコンパイルして処理し、マルチオブジェクトトラッカーと推定フィルターを使用してアーキテクチャを評価する

Deep Learning Toolbox™ を使用して、AI を独自の設計やモデルに統合します。MATLAB^®Coder™を使用して、開発した信号処理アルゴリズムからコードを生成し、ハードウェア上にソリューションを展開します。

レーダー向け製品

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

トピック

モデル化とシミュレーション

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.

地図作成

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.

状況認識と状態推定

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.

オブジェクトの追跡とモーションプランニング

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.

機械学習と深層学習

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).

ハードウェアへの展開

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.

注目の例

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 theRadar Designer应用程序执行一个链接让步t 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.

Lidar and Radar Fusion in Urban Air Mobility Scenario

Use multiobject trackers to track various unmanned aerial vehicles in an urban environment.

Processor-in-the-Loop Verification of JPDA Tracker for Automotive Applications

Generate embedded code for a JPDA tracker and verify it using processor-in-the-loop (PIL) simulations.

Assessing Performance with the Tracker Operating Characteristic

Assess the probability of target track and the probability of false track.