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.

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
• Information theory and Sparse representations

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
• 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

History