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Master’s in Advanced Radar and LiDAR Processing Engineering

Online master's programme of 1,900 hours in advanced radar and LiDAR processing engineering. 12 modules covering radar signals and waves, radar processing, 4D imaging radar, LiDAR physics, point cloud processing, 3D LiDAR detection, deep learning for radar, multi-object tracking, radar–LiDAR fusion, interference and robustness, and hardware and testing, with an assigned technical tutor.

Duration1900 h
ModeOnline
LanguageEnglish
Start datesSet by the faculty
AwardSEIUM Institute diploma
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Learning outcomes

What you will be able to do

01

Explain the physical principles of FMCW radar and LiDAR.

02

Implement radar processing chains.

03

Process LiDAR point clouds.

04

Train 3D detectors on LiDAR and radar data.

05

Design multi-object tracking systems.

06

Fuse radar and LiDAR robustly.

07

Develop an advanced radar and LiDAR processing project.

Profile

Who it's for

  • Radar signal processing engineers.
  • LiDAR engineers.
  • 3D perception engineers.
  • Sensor fusion engineers.
  • RF hardware engineers.
  • Sensor researchers.
  • Engineering or science graduates seeking to enter the automotive sector.
Standards & tools

What you'll work with

  • ETSI EN 301 091 (short-range radar equipment in the 76–77 GHz band)
  • ETSI EN 302 264 (short-range radar equipment in the 77–81 GHz band)
  • IEC 60825-1 (laser product safety)
  • ISO 26262 (road vehicle functional safety)
  • ISO 21448 (SOTIF, safety of the intended functionality)
  • ISO 23150 (sensor–data fusion interface)
  • Directive 2014/53/EU (radio equipment)
  • UN ECE R10 (electromagnetic compatibility)
  • MATLAB Radar Toolbox
  • Point Cloud Library
  • ROS 2
  • PyTorch
  • Open3D
Syllabus

12 modules · 1900 h

  1. 1.1The radar equation and radar cross-section
  2. 1.2FMCW modulation and chirps
  3. 1.3Mixing and beat frequency
  4. 1.4Thermal noise and SNR
  5. 1.5Antennas and radiation patterns
  6. 1.624, 77 and 79 GHz bands
  7. 1.7RF front-end architectures
  8. 1.8Virtual MIMO systems
  9. 1.9Spectrum requirements
  10. 1.10Lab: FMCW signal simulation
  1. 2.1Range and Doppler FFT
  2. 2.2Range–Doppler maps
  3. 2.3CFAR target detection
  4. 2.4Angle estimation by beamforming
  5. 2.5MUSIC and ESPRIT algorithms
  6. 2.6Doppler ambiguity and its resolution
  7. 2.7Clustering with DBSCAN
  8. 2.8Micro-Doppler classification
  9. 2.9DSP optimisation
  10. 2.10Practical exercise: radar processing chain in MATLAB
  1. 3.1Large antenna arrays
  2. 3.2Elevation resolution
  3. 3.3Dense radar point clouds
  4. 3.4SAR radar on vehicles
  5. 3.5High-resolution processing
  6. 3.6Comparison with LiDAR
  7. 3.7Semantics in radar data
  8. 3.84D radar datasets
  9. 3.9Integration in production
  10. 3.10Workshop: detection with 4D radar
  1. 4.1The LiDAR equation and reflectivity
  2. 4.2Pulsed lasers and diodes
  3. 4.3APD and SPAD detectors
  4. 4.4Direct time of flight
  5. 4.5FMCW modulation in LiDAR
  6. 4.6Mechanical, MEMS and OPA scanning
  7. 4.7Solid-state flash LiDAR
  8. 4.8Laser eye safety
  9. 4.9Range, resolution and noise
  10. 4.10Lab: LiDAR link budget
  1. 5.1Data structures and octrees
  2. 5.2Noise and outlier filtering
  3. 5.3Ground segmentation
  4. 5.4Euclidean point clustering
  5. 5.5Model fitting with RANSAC
  6. 5.6Registration with ICP and NDT
  7. 5.7Motion distortion correction
  8. 5.8Point cloud compression
  9. 5.9Formats and tools
  10. 5.10Practical exercise: ground segmentation with PCL
  1. 6.1Voxel and pillar representations
  2. 6.2PointNet and PointNet++
  3. 6.3VoxelNet and SECOND
  4. 6.4PointPillars architecture and variants
  5. 6.5Centre-based detection with CenterPoint
  6. 6.6Range-image detection
  7. 6.73D detection metrics
  8. 6.8KITTI and Waymo datasets
  9. 6.9Real-time deployment
  10. 6.10Workshop: training CenterPoint
  1. 7.1Radar tensor representations
  2. 7.2Detection on range–Doppler maps
  3. 7.3Networks for radar point clouds
  4. 7.4Radar semantic segmentation
  5. 7.5Deep radar–camera fusion
  6. 7.6RADIal and View-of-Delft datasets
  7. 7.7Radar data augmentation
  8. 7.8Deployment on radar processors
  9. 7.9Model evaluation
  10. 7.10Lab: network for raw radar data
  1. 8.1CV and CTRV motion models
  2. 8.2Extended and unscented Kalman filters
  3. 8.3Extended object tracking
  4. 8.4PHD and PMBM filters
  5. 8.5Data association
  6. 8.6Track management
  7. 8.7Shape and orientation estimation
  8. 8.8MOTA and HOTA metrics
  9. 8.9Tracking with Doppler velocity
  10. 8.10Practical exercise: tracking with Doppler radar
  1. 9.1Radar–LiDAR extrinsic calibration
  2. 9.2Sensor time synchronisation
  3. 9.3Detection-level fusion
  4. 9.4Feature-level fusion
  5. 9.5Sensor complementarity
  6. 9.6Occlusion handling
  7. 9.7Fusion with maps
  8. 9.8BEV architectures for fusion
  9. 9.9Fusion evaluation
  10. 9.10Workshop: radar–LiDAR fusion in ROS 2
  1. 10.1Mutual radar interference
  2. 10.2Mitigation techniques
  3. 10.3Interference between LiDARs
  4. 10.4Spoofing and jamming
  5. 10.5Weather effects
  6. 10.6Multiple reflections and ghost targets
  7. 10.7Stationary objects and false alarms
  8. 10.8Anomaly detection
  9. 10.9Robustness testing
  10. 10.10Case study: radar interference mitigation
  1. 11.1Radar MMIC chips
  2. 11.2Radar signal processors
  3. 11.3LiDAR processing units
  4. 11.4Mechanical integration and radomes
  5. 11.5Factory calibration
  6. 11.6Radar test benches
  7. 11.7Radar target simulators
  8. 11.8Environmental testing of sensors
  9. 11.9Radio regulations for radars
  10. 11.10Lab: testing with a target simulator
  1. 12.1Defining the application
  2. 12.2Selecting the sensors
  3. 12.3Designing the radar processing chain
  4. 12.4Designing the LiDAR processing chain
  5. 12.5Detection and tracking
  6. 12.6Sensor fusion
  7. 12.7Validation with real data
  8. 12.8Technical conclusions of the project
  9. 12.9Master's Thesis report
  10. 12.10Master's Thesis: defence before a panel
Methodology, software & labs

How you'll learn

Training is 100% online on the SEIUM campus, with downloadable materials, recorded classes and an assigned technical tutor. There are no fixed timetables: you progress at your own pace within each intake, so it fits around your job.

Each module combines theory, guided reading of the original standard and applied exercises. The working software is MATLAB Radar Toolbox, Point Cloud Library, ROS 2, PyTorch and Open3D, in free, academic or evaluation versions. Testing is studied through real procedures for raw radar data processing, 3D detection in point clouds and tests with a radar target simulator. Assessment is continuous by module, plus the final Master's Thesis.

Standards covered: ETSI EN 301 091, ETSI EN 302 264, IEC 60825-1, ISO 26262, ISO 21448, ISO 23150, Directive 2014/53/EU, UN ECE R10.

Assigned technical tutor:every module ends with a deliverable that your tutor marks individually.
Standards-based learning:guided reading of ETSI EN 301 091, ETSI EN 302 264 and IEC 60825-1 applied to real cases, not as a theoretical annex.
Industry software:MATLAB Radar Toolbox, Point Cloud Library, ROS 2, PyTorch, Open3D, in free, academic or evaluation versions.
Master's Thesis with defence:An advanced radar and LiDAR processing project covering sensor selection, radar and LiDAR processing chains, detection and tracking, fusion and validation with real data, defended before a panel.
Flexible format:100% online, no fixed timetables, compatible with full-time work.
Capstone project

Build it end to end

Your final project follows the real engineering workflow, step by step, reviewed by your tutor.

Application and sensor selection
Define the application and select the radar and LiDAR sensors.
Radar processing chain
Design the radar chain from FFT and CFAR to clustering.
LiDAR processing chain
Design point cloud processing and 3D detection.
Tracking and fusion
Implement multi-object tracking and radar–LiDAR fusion and validate them with real data.
Master's Thesis
A complete radar and LiDAR processing project, defended before a panel.
Career opportunities

Where this takes you

Radar signal processing engineerLiDAR engineer3D perception engineerSensor fusion engineerRF hardware engineerSensor researcher
Admissions & scholarships

Requirements

  • A university degree in engineering, science or architecture, or equivalent demonstrable professional experience.
  • An internet connection and a computer capable of running the programme's software.
  • English sufficient to read technical standards.

Ready to start?

Send your application today. Our admissions team replies within 72 hours with your study plan and funding options.

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FAQ

Frequently asked questions

The programme totals 1,900 hours over 12–14 months. You set your own pace within each intake, and your tutor helps you plan the workload around your job.

You work with MATLAB Radar Toolbox, Point Cloud Library, ROS 2, PyTorch, Open3D, using free, academic or evaluation versions. Your tutor indicates the free alternative wherever one exists.

A SEIUM Institute diploma, awarded on passing the continuous assessment and the Master's Thesis. It is SEIUM's own qualification, not an official state degree; its recognition depends on the programme completed and the regulations of each country.

Yes. Modules passed in a course or postgraduate diploma in the same area are recognised in the higher programme. Ask about the recognition policy when you enrol.

A degree in engineering, science or architecture (or equivalent professional experience), a computer able to run the software, and English sufficient to read technical standards.

Start dates are set by SEIUM's faculty for each intake. Our admissions team will confirm the next available date when you apply.

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