Autonomous sensor systems

Contact: Kofi Makinwa

Sensor systems are used for measuring, monitoring and control. Autonomous sensor systems are sensor systems in which distributed sensor nodes contain local signal and data pre-processing capabilities. Thus, self-testing, auto-calibration, filtering, data compression, etc., within sensor nodes without or with very limited external intervention is crucial. In many applications, wiring is not possible, unreliable or undesirable, which makes the powering of sensors a major issue and calls for ultra-low power electronics and smart design at all levels in the system.

In IoT applications, sensors operate in a network. Limited power resources favor power-efficient communications. Sensors can communicate only with neighboring nodes, creating a dynamic and complex network that relies on self-organizing and self-healing capabilities.

In biomedical applications, the sensors need to be miniaturized and packaged to be bio-compatible or implantable, as e.g. with smart (multi-modal) catheters or electroceuticals, which combine sensing, local data processing, and actuation.

In other applications (autonomous driving or flying enabled by a variety of radar sensors distributed over many nodes), the focus is on robustness and reliability in view of a very dynamic network, and optimal fusion of different sources of information. With TUE we collaborate on a program to realize a low-frequency radio telescope in space using a swarm of nano-satellites (OLFAR). Emerging technologies such as neuromorphic computing and structured data science are expected to play an important role in future.

Projects under this theme


Physics inspired inference, classification and prediction in networked systems

Reliable POwerDown for Industrial Drives

The pioneering EU research project R-PODID started on the 1st of September 2023. This KDT JU co-funded project aims to develop an automated, cloudless, short-term fault-prediction for electric drives, power modules, and power devices, that can be integrated into power converters.


Science and technology for Lunar surface missions

Signal processing for environment-aware radar

In future, cars will exploit multiple radars towards autonomous driving. Before this becomes a reality, several challenges will have to be solved.


Odour Based Selective Recognition of Veterinary Diseases

Delft Sensor AI Lab

Bringing AI to sensor networks

Cooperative Relative Navigation of Multi-agent Systems

Develop algorithms for multi-target position, time and orientation tracking in a mobile network of multi-agent systems

Next-generation chip assembly processes

Developing technology for ultra-high throughput and sustainable chip assembly processes.

Automotive Intelligence for Connected Shared Mobility

Architectures for embedded intelligence and functional virtualization for connected and shared mobility using trustworthy AI

Distributed Artificial Intelligent Systems

Running existing algorithms on vastly distributed edge devices

Compact modelling of high-tech systems for health management and optimization along the supply chain

In-vehicle health monitoring

Airborne data collection on resilient system architectures

Develop algorithms to realize efficient, robust, cost-effective perception and control for autonomous navigation of drones


Virtual platforms for perception and control in highly automated vehicles, based on safety by design

Coded-Radar for Interference Suppression in Super-Dense Environments

CRUISE will address the challenges regarding spectrum crowding and ensures proper radar signal detection, accurate ranging, Doppler and azimuth measurements, and object classification in a highly-occupied frequency spectrum


Challenging environments tolerant smart systems for IoT

Intelligent Reliability 4.0

Graphene Flagship core 3: Transferless graphene in sensing applications

Internet of Things (IoT) security through machine learning and data sharing

High Performance Vehicle Computer and Communication System for Autonomous Driving

Solid State Lighting reliability for automotive application

PIPP OLFAR: Breakthrough technologies for Interferometry in Space

Combine multiple satellites into one single scientific instrument: a radio telescope in space

Programmable Systems for Intelligence in Automobiles

(a) fail-operational sensor-fusion framework, (b) dependable embedded E/E architectures, (c) safety compliant integration of AI approaches for object recognition, scene understanding, and decision making

Monolithically integrated SiC sun sensor for Space

Task-cognizant sparse sensing for inference

Low-cost sparse sensing designed for specific tasks

Low-frequency distributed radio telescope in space

Below 15 MHz, the ionosphere blocks EM signals from the sky. Therefore, can we design a radio telescope in space, using a swarm of inexpensive nano-satellites? Accurate localization and clock recovery is important.