Joint Multitarget Tracking and Dynamic Network Localization in the Underwater Domain

Mendrzik, Rico; Brambilla, Mattia; Allmann, Clemens; Nicoli, Monica; Koch, Wolfgang; Bauch, Gerhard; LePage, Kevin D.; Braca, Paolo

doi:10.1109/icassp40776.2020.9054047

Cited by 8 publications

(11 citation statements)

References 21 publications

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“…The first major research topic of my Ph.D. studies covers the area of cooperative localization, and most of the contents have been published in [2][3][4]. A network of multiple agents has the advantage of overcoming the ego-agent capability by enlarging the set of available information.…”

Section: Cooperative Localizationmentioning

confidence: 99%

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Sensor-Assisted Cooperative Localization and Communication in Multi-agent Networks

Brambilla

2022

Special Topics in Information Technology

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This brief highlights research advances on cooperative techniques for localization and communication. These two macro trends are investigated in the general context of mobile multi-agent networks for situational awareness applications, where time-varying agents of unknown locations are asked to fulfill positioning and information sharing tasks. Cooperative localization is conceived for both active and passive agents, i.e., targets to be detected and localized, and it is analyzed in vehicular and maritime environments. Communication is investigated for vehicular scenarios, where vehicles are requested to share massive data in the perspective development of connected and automated mobility systems. Both research areas rely on the integration of heterogeneous sensors and communication. Specifically, it is studied how to improve localization by exploring communication techniques as well as how to enhance communication performances by extracting information from perception sensors. The dynamic environment of multi-agent systems calls for robust, flexible and adaptive techniques, capable of profitably fuse different types of information, and the outcomes of these researches show how a statistical approach based on cooperation guarantees higher resilience, reliability and confidence.

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Section: Cooperative Localizationmentioning

confidence: 99%

“…This chapter summarizes the main research works characterizing my Ph.D. studies at Politecnico di Milano from Nov. 2017 until Oct. 2020, under the supervision of Professor Monica Nicoli, which lead to the doctoral dissertation in [1] and the scientific publications in [2][3][4][5][6][7][8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Sensor-Assisted Cooperative Localization and Communication in Multi-agent Networks

Brambilla

2022

Special Topics in Information Technology

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“…We focus the attention on the holistic approach for cooperative self-localization and multitarget tracking, and on the real world experimentation. In [53] an ad-hoc scenario with defined roles for transmitters and receivers is considered; that work is here advanced as follows:…”

Section: B Contributions and Paper Organizationmentioning

confidence: 99%

“…• a more general formulation is provided in which agents can both sense the environment and communicate with each other; • the measurement uncertainty handles signal reflections from both agents and targets (i.e., the data association is not limited to targets only as in [16], [49], [50], [53]).…”

Section: B Contributions and Paper Organizationmentioning

confidence: 99%

“…Moreover, the solutions proposed in these works are not suitable for a full multistatic network configuration, and do not consider the case of signal reflections from agents, thus easing the data association problem. The above issues have been partially addressed in [53], that indeed represents the preliminary study at the basis of this research; it is purpose of this paper to further extend that work as detailed in the next subsection.…”

Section: Introduction a Background And Motivationmentioning

confidence: 98%

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Cooperative Localization and Multitarget Tracking in Agent Networks with the Sum-Product Algorithm

Brambilla,

Gaglione,

Soldi

et al. 2021

Preprint

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This paper addresses the problem of multitarget tracking using a network of sensing agents with unknown positions. Agents have to both localize themselves in the sensor network and, at the same time, perform multitarget tracking in the presence of clutter and target miss detection. These two problems are jointly resolved in a holistic approach where graph theory is used to describe the statistical relationships among agent states, target states, and observations. A scalable message passing scheme, based on the sum-product algorithm, enables to efficiently approximate the marginal posterior distributions of both agent and target states. The proposed solution is general enough to accommodate a full multistatic network configuration, with multiple transmitters and receivers. Numerical simulations show superior performance of the proposed joint approach with respect to the case in which cooperative self-localization and multitarget tracking are performed separately, as the former manages to extract valuable information from targets. Lastly, data acquired in 2018 by the NATO Science and Technology (STO) Centre for Maritime Research and Experimentation (CMRE) through a network of autonomous underwater vehicles demonstrates the effectiveness of the approach in practical applications.

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Cross-scale cascade transformer for multimodal human action recognition

Liu

Qin

Song

et al. 2023

Pattern Recognition Letters

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Joint Multitarget Tracking and Dynamic Network Localization in the Underwater Domain

Cited by 8 publications

References 21 publications

Sensor-Assisted Cooperative Localization and Communication in Multi-agent Networks

Sensor-Assisted Cooperative Localization and Communication in Multi-agent Networks

Cooperative Localization and Multitarget Tracking in Agent Networks with the Sum-Product Algorithm

Cross-scale cascade transformer for multimodal human action recognition

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