Distributed Autonomous Sensing Systems

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 Navigation (including localization and synchronization). One of the underlying challenges is to develop algorithms for resource constrained sensor systems, and which are particularly deployed in inaccessible 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.
Techniques
- 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
Applications
- 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
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?
Projects under this theme
Delft Sensor AI Lab
Distributed AI for 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
History
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.
MSc students
- Chen Xi
- Xuchang Zhang
- Ban Hanyuan
- Lan Jia
- Vishakha Marathe
- Nishanth Ramesh
- Haobo Wang
- Zeineh Bou Cher
- Siddhy Ganesh Shetty
- Rui Tang
- Mosab Diab
- Peiyuan Zhai
- Zhonggang Li
- Brenda Hernandez Perez
- Xuzhou Yang
Alumni
- Calum Turner (2021)
- Bichi Zhang (2021)
- Felix Abel (2021)
- Elke Salzmann (2021)
- Martijn van der Marel (2021)
- Yikai Zeng (2020)