PhD Thesis defense: Algorithms for propagation-aware underwater ranging and localization

9 Jun
Elizaveta Dubrovinskaya

Elizaveta Dubrovinskaya, PhD Student, IMDEA Networks Institute and University Carlos III of Madrid

PhD Defense

While oceans occupy most of our planet, their exploration and conservation are one of the crucial research problems of modern times. Underwater localization stands among the key issues on the way to the proper inspection and monitoring of this significant part of our world.

In this thesis, we investigate and tackle different challenges related to underwater ranging and localization. In particular, we focus on algorithms that consider underwater acoustic channel properties. This group of algorithms employs additional information about the environment and its impact on acoustic signal propagation, in order to improve the accuracy of location estimates, or to achieve a reduced complexity, or to size down the localization infrastructure (e.g., the number of deployed anchor nodes) compared to traditional algorithms.

For certain applications that consider the localization of sporadic targets, it is important to simultaneously observe broad underwater environments. While most modern localization methods assume the deployment of multiple sensors, the deployment of a large number of active sonars to cover wide areas is often challenging, and may be cost- and energy-ineffective. Moreover, active acoustic equipment may introduce various changes in the natural habitat of marine animals. Autonomous Underwater Vehicles (AUVs) can be another key instrument for the observation of marine life: localizing them in a cost- effective way with easily deployed infrastructure is therefore a very relevant issue.

There were several attempts at addressing underwater localization using a single passive sensor, by leveraging additional information about the environment, and by modeling sound signal propagation.

First, we tackle the problem of passive range estimation using the differences in the times of arrival of multipath replicas of a transmitted acoustic signal. This is a cost- and energy- effective algorithm that can be used for the localization of autonomous underwater vehicles (AUVs) and utilizes information about signal propagation. We study the accuracy of this method in the simplified case of constant sound speed profile (SSP) and compare it to a more realistic case with various non-constant SSPs. We also propose an auxiliary quantity called effective sound speed. This quantity, when modeling acoustic propagation via ray models, takes into account the difference between rectilinear and non-rectilinear sound ray paths. According to our evaluation, this offers improved range estimation results with respect to standard algorithms that consider the actual value of the speed of sound.

Then we propose another approach inspired by traditional wireless sensor networks (WSN) fingerprint-based localization. In this approach, we utilize the dependence of the acoustic channel on the spatial diversity of the bathymetry profile. We propose an algorithm that exploits this diversity in a computationally effective way. By applying a modified Viterbi algorithm to filter out locations that were unlikely visited, we obtain the most likely path of a mobile source. We test the accuracy of our algorithm in different simulated conditions, and validate its performance using data from real sea experiments.

In search of a balance between localization accuracy and the cost and complexity of deployment, there could be situations when, for a given accuracy, it is necessary to use multiple pre-assembled arrays. Fusing information from multiple acoustic sensors can have a positive effect on the reliability of the results. Despite this, often such design may not meet the condition that the distance between the elements should be no more than half the minimum wavelength. This may be caused by design considerations such as cabling, batteries or other construction issues. In this case, the ability to use information from all the sensors is limited due to the spatial ambiguity caused by the improper spacing of array elements. In the context of the SYMBIOSIS project, we addressed this issue by designing and implementing a localization algorithm for pelagic fish species. For this, we used a software-defined version of a commercial ultra-short baseline (USBL) array of five elements.

To reach the expected degree of accuracy, more sensors are often required than are available in typical commercial off-the-shelf (COTS) phased arrays found, e.g., in USBL systems. Direct combination of multiple COTS arrays may be constrained by array body elements, and lead to breaking the optimal array element spacing, or the desired array layout. Thus, the application of state-of-the-art direction of arrival estimation algorithms may not be possible. We propose a solution for passive 3D localization and tracking using a wideband acoustic array of arbitrary shape, and validate the algorithm in multiple experiments, involving both active and passive targets. Therefore we propose an algorithm for 3D wideband DOA estimation for such “opportunistically” joined arrays. We provide an in-depth analysis of its performance in various simulated conditions, and validate the results in a preliminary experiment performed in a lake in Germany.

Timely software maintenance and proper assembly of the complex final design of the SYMBIOSIS platform was challenged by the travel difficulties caused by the well-known pandemic-related events of 2020. In this regard, changes were made to the algorithm that allowed for additional functions and worked in the absence of key elements. We summarized our work on the validation of the localization and tracking algorithm in real sea environments with different acoustic conditions, where the task of the algorithm is to detect and track marine fauna specimens, in order to establish when fishes would come sufficiently close to an underwater platform. The algorithm forecasts when fish targets approach the platform and triggers image acquisitions from the platform’s cameras. The outcomes show that the algorithm can localize not just highly reflective or active targets but also smaller and weaker targets such as the fish species of interest for the SYMBIOSIS project. The algorithm works as expected in different environments.

Overall, this work has shown the effectiveness of using additional information about acoustic signal propagation and its benefits in further application of this approach to similar problems.

About Elizaveta Dubrovinskaya

Elizaveta Dubrovinskaya received her Diploma degree with honors from the St. Petersburg State Transport University, Russia and her M.Sc. degree from the Christian Albrechts University of Kiel, Germany in 2008 and 2012, respectively. Her Master’s thesis was focused on the development of algorithms for a shallow-water acoustic localization.

Currently, she is a board member of Teleone OU (a telecommunications startup in Tallinn, Estonia), and is working towards her Ph.D. degree at the IMDEA Networks Institute and Carlos III University of Madrid, Spain. During her Ph.D., Elizaveta has been a visiting student at the Florida Atlantic University, where she worked on underwater LiDAR systems, and at the University of Haifa, Israel, where she worked on environment-aware acoustic localization algorithms and took part in multiple sea trials.

The latter work received the Best Paper Award at the WPNC 2017 symposium. She was collaborating with the EU H2020 SYMBIOSIS project, focusing on acoustic array design, array processing, as well as on the implementation of an active sonar for marine life monitoring in collaboration with the University of Haifa and EvoLogics GmbH (Berlin, Germany). Her research interests revolve around many aspects of acoustic communication networks, including algorithm design and field experiments for acoustic localization.

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The thesis defense will be online. However, you are all most welcome to attend. To be able to join, use this link:

Please, keep microphones and cameras off.


PhD Thesis Advisor: Dr. Paolo Casari (University of Trento, Italy)

PhD Thesis Tutor: Dr. Albert Banchs (University Carlos III of Madrid / IMDEA Networks)

University: University Carlos III of Madrid, Spain

Doctoral Program: Telematics

PhD Committee members:

  • President: Paul Daniel Mitchell, Full Professor at the University of York, UK.
  • Secretary: Antonio Fernandez Anta, Research Professor at IMDEA Networks
  • Panel member: Santiago Zazo Bello, Full Professor at the Universidad Politécnica de Madrid

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