Sensor Fusion

Sensor Fusion

Combining multiple sensors for enhanced environmental awareness and superior system performance

Sensor fusion combines data from multiple sensors to produce a better understanding of the surrounding environment than possible with any single sensor alone. By integrating multiple sensors into a single system, the individual strengths of each sensor can be exploited to mitigate the weaknesses of the others. This results in enhanced performance across applications that require high levels of robustness, reliability and safety.

This sensor fusion approach can produce a combined system greater than the sum of its parts, providing a more comprehensive environmental awareness for autonomous vehicles, robotics, space missions and security applications.


The challenges

Modern sensing applications face environmental and operational challenges that single sensors struggle to address independently:

Environmental conditions

Fog, rain, snow, darkness, bright sunlight and dust can severely impact individual sensor performance. 

Spatial resolution limitations

Individual sensors may lack the resolution needed to distinguish between closely spaced objects or resolve fine detail.

Classification difficulties

Single sensors can struggle with accurate identification and classification across all target objects.

Range and detection limitations

Different sensors excel at different distances and detection scenarios.

Safety-critical applications

Autonomous systems require redundancy to prevent catastrophic failures from single sensor malfunctions.

The Plextek advantage

2 members of the software team
  • MIMO Radar Systems

    Design and development of Multiple-Input Multiple-Output radars using mmWave technology to produce 4D radars that can measure range and velocity along with azimuth and elevation angles. 

  • Multi-sensor Integration

    Capabilities in radar and other sensors, managing calibration, synchronisation and data volume challenges across different sensor modalities. 

  • System Integration

    Managing size, weight, power consumption and cost constraints while maintaining optimal sensor performance and reliability. 

  • Environmental Robustness

    Designing systems that maintain performance across adverse conditions including low visibility, extreme weather and challenging lighting. 

  • Algorithm Development

    Design of processing algorithms for extracting information from varied raw sensor data. 

  • Artificial Intelligence & Machine Learning

    Training AI models on large sets of sensor data to produce classifiers for different client applications. 

  • Real-time Processing

    Implementation of algorithms with real-time constraints and optimising processing hardware for multiple sensors. 

Sensor fusion can be used across multiple sectors and applications, including:

Autonomous Vehicles

Complete sensor fusion for self-driving cars, combining multiple sensor types, such as radars, cameras and lidars, for maximum safety and reliability. 

Advanced Driver Assistance Systems (ADAS)

Collision avoidance, lane keeping and adaptive cruise control systems using radar and camera fusion. 

Space Missions

Robust sensing for lunar landers, satellites and space probes where single sensor failure could be catastrophic. 

Robotics

Industrial and service robots requiring precise navigation and object manipulation in complex environments. 

Maritime Surveillance

Coastal and offshore monitoring systems combining radar with optical sensors for comprehensive coverage. 

Perimeter Security

Multi-sensor security systems providing reliable detection and classification in all weather conditions. 

Sensor fusion can combine the strengths of multiple sensors to produce a more accurate and robust understanding of the surrounding environment, which is essential for safety-critical applications.

Dr Damien Clarke, Lead Consultant
Dr Damien Clarke

Head of Data Science


Specialised Services

The following are examples of the work we specialise in:

  • MIMO radar system design
  • 4D imaging radar development
  • Multi-sensor calibration and synchronisation
  • Real-time fusion algorithm implementation
  • AI-driven sensor fusion
  • Environmental testing and validation
  • System integration and optimisation
  • Radar signal processing
  • Camera and lidar integration
  • Micro-Doppler analysis
  • Target classification algorithms
  • Motion detection and tracking
  • Multipath and clutter mitigation
  • Sensor placement optimisation
  • Performance analysis and validation
  • Cost-optimised sensor selection

Talk to our experts

Get in touch with our multi-disciplinary team of engineers, who combine decades of experience in mmWave technology, real-time algorithm development and multi-sensor system integration to deliver solutions for your most challenging sensing applications.

Mirco Carciani, Freddy Saunders, Josip Rozman & Damien Clarke
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