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Joint Sensing, Localization, and Communication for Next-Generation Autonomous Underwater Systems

  • Funding agency: WASP Sweden
  • Project leader: Isaac Skog
  • Project partners: Saab Dynamics
  • Duration: 2023 - 2028

News and results

  • Ph.D. student Ashwani Koul started working in the project in august 2023.

Project description

Historically, Sweden holds a strong position within the area of underwater technology, with world-class submarines and autonomous underwater systems. In 2014 the Swedish government also added the underwater domain to a list of vital strategic interest areas in which Sweden would seek to retain technological and operational advantage. An important component needed to ensure that Sweden remains at the technological forefront within this domain is the development of new technologies for sensing, localization, and communication in complex underwater environments. These are core technologies required to develop the situation-awareness and cooperation capabilities envisioned by next generation autonomous underwater systems. A generation of autonomous underwater systems that are foreseen to: increase the ocean productivity; improve the protection of critical infrastructure; and improve the monitoring and protection of the ocean health and its wildlife.

Currently, the tasks of sensing, localization, and communication are treated separately in most autonomous underwater systems. Nor are these tasks tightly integrated with the decision making process in the autonomous systems. This causes a suboptimal utilization of available resources, such as energy, communication bandwidth, location information, etc., which typically are very limited within subsurface systems. However, just as envisioned for sixth generation (6G) cellular systems, the possibility of designing solutions for joint sensing, localization, and communication in autonomous underwater networks and multi-agent systems is emerging. This is made possible thanks to the rapid development of fully digital sonar signal processing chains, massive hydrophone arrays, and energy efficient data processing units. But before these envisioned joint sensing, localization, and communication solutions can be realized there are several signal processing and machine learning related challenges that must be solved. Some of the most important are: “How should the communication signals be designed to maximize the gathered sensing and localization information?”, “How can reliable and real-time usable channel models and maps of the acoustic environment be learned online?”, and “How should, in a distributed fashion, decisions about what to communicate and when be taken, given that each information exchange also generates new information both on the transmitting and receiving side?”. To answer some of these questions and contribute to solving these challenges, the project aims to research techniques for:

  • Optimal communication signals design with respect to gained sensing and localization information, i.e., maximum echo sounding accuracy for perception and maximum ranging accuracy for multi-lateration.
  • Online learning of models and maps that describes the acoustic environments and its disturbances using first-order physical principles in conjugate with non-parametric data models
  • Distributed sensor-fusion and communication management that enables cooperative sensing and localization in multi-agent autonomous underwater systems using specially designed communication signals and where the acoustic footprint is minimized.

Map showing the received signal energy at a passive hydrophone array as function of beamforming direction and time. A goal of the project is to develop techniques that enables online learning of object behavior and background disturbances models to improve the tracking of objects, such as the Saab AUV 62.
Map of echoes from 10 sonar pulses sent from an autonomous system. A goal of the project is to develop techniques that enables inter-system communication signals to simultaneously be used for building such maps (sensing) and determine the relative location of the systems (localization).
Isaac Skog

Associate Professor in Automatic Control,
Docent in Signal Processing

(Swedish: Universitetslektor i reglerteknik)

+46 708186805
Dept. of Electrical Engineering
Linköping University
SE-581 83 Linköping
Visiting Address:
Campus Valla
Building B
Room 2A:526

Page responsible: Isaac Skog
Last updated: 2023-09-13