Distributed autonomous sensing systems

Contact: Raj Thilak Rajan

The Distributed Autonomous Systems theme centers on the fundamental signal processing challenges of multi-agent sensing systems. The current research focus is on distributed/decentralized processing, probabilistic machine learning, federated learning, resource allocation, autonomous inference through sensor fusion, and PNT (Position, Navigation and Timing). One of the underlying challenges is to develop algorithms for resource constrained sensor systems and systems deployed in inaccessible/intermittentily accesible locations. We work closely with various universities (e.g, TU/e, Radboud), research institutes (e.g., TNO, SRON, NLR) and industries (e.g., VSL, KPN, NXP), in the Netherlands and internationally.



  • Estimation and Detection theory
  • Convex and Non-convex optimization, Mixed integer optimization
  • Statistical signal processing, performance analysis, and bounds
  • Distributed signal processing and Distributed systems
  • Parametric/Non-parametric models for complex dynamical systems
  • Dynamics and Kinematics of multi-agent systems
  • Probabilistic machine learning, Sensor fusion
  • Information theory and Sparse representations
  • Affine transformations, SE(d) and SO(d) groups


  • Wireless sensor networks
  • Cooperative and non-cooperative localization and tracking of mobile agents
  • Data and clock synchronization, multi-sensor calibration
  • Autonomous navigation, Environment perception, Path planning and Detect and Avoid
  • Multi-agent systems e.g., aircrafts, rovers, satellite arrays and drone swarms
  • Learning underlying manifolds in unknown environements using multi-agent systems

Research Themes

  • Signal processing for Distributed inference: Sensor systems consist of numerous nodes with only local communication capabilities. Challenges include localization of the nodes, clock synchronization, low-power communication protocols, and distributed estimation/detection algorithms (e.g., consensus, ADMM, PDMM), where local estimates are combined to form global parameter estimates.
  • Signal processing for Autonomous inference: A single system consisting of multi-modal sensors (e.g., IMU, Camera, LIDAR, Radar) aims to autonomous perform desired tasks with minimal control from other actors/agents/humans e.g., environmental perception, path planning, detect and avoid, and self-calibration. How can the diverse streams of data from various sensors (e.g., continuous, discrete, intermittently available, block datasets) be combined in a prudent way for optimal statistical inference?
  • Signal processing for multi-agent mobile systems:  Consider a network of mobile agents (e.g., automotive, satellites, rovers, drones), which cooperatively perform a given task e.g., platooning, collaborative path planning and formation flying. How can we exploit the underlying physics, and optimally combine information of kinematics and dynamics of these multi-agent systems to perform these tasks? These tasks involve a combination of estimation, detection, information processing and control theory.

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.

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

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

Airborne data collection on resilient system architectures

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


PIPP OLFAR: Breakthrough technologies for Interferometry in Space

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

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.